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Active Distribution Networks Planning Considering Multi-DG Configurations and Contingency Analysis
Get PDFYesThis paper proposes a novel method for planning active distribution networks (ADNs) with the integration of an active network management (ANM) scheme using coordinated voltage control (CVC) through on-load tap changer (OLTC) transformers. The method was formulated as a security-constrained optimal power flow (SCOPF) problem to minimize total operational costs, which maximizes the utilization of renewable distributed generators (DGs) over a planning horizon. The ANM scheme was applied using OLTC to ensure safe operation and reduce voltage violations in the network. To analyse the impact of ANM, the planning problem was examined both with and without the ANM scheme. Moreover, SCOPF, considering the N-1 line contingency analysis and multi-DG configuration, was implemented to analyse the feasibility of the proposed method and the advantages of ANM under contingency situations. The method was validated on a weakly-meshed 16-bus UK generic distribution system (UKGDS). The results showed that ANM can lower operational costs and maintain network voltage for operation in feasible conditions even in the case of a contingency. Moreover, the ANM scheme mitigated the voltage rise effect caused by DGs and maximized their utilization
Nostalgic experiences in time-honored restaurants: Antecedents and outcomes
Get PDFYesWith a long history and strong culinary heritage, time-honored restaurants are often associated with the phenomenon of nostalgia. However, research on nostalgia and nostalgic experiences in time-honored restaurants is largely absent. This study built a framework for nostalgic experiences to understand nostalgia triggers as antecedents and consumers’ revisit intention as the outcome. A survey of 366 residents in Beijing and Shanghai, China, revealed that nostalgia triggered by food and service staff significantly evoked consumers’ memories, and the food and restaurant environment stimulated the communitas component of nostalgic experiences. Memory had a positive effect on both communitas and positive emotions, while communitas had a positive effect on positive emotions. Finally, positive emotions resulted in significantly increased revisit intention
Patients as qualitative data analysts: Developing a method for a process evaluation of the 'Improving the Safety and Continuity of Medicines management at care Transitions' (ISCOMAT) cluster randomised control trial
No full textYesHow to meaningfully partner with patients as data analysts remains obscure. A process evaluation of the ‘Improving the Safety and Continuity Of Medicines management at care Transitions’ (ISCOMAT) cluster randomised control trial of an intervention for improving medicines use for people living with heart failure is being conducted. The intervention includes patient held information on heart medicines and care, enhanced communication between hospital and community pharmacists, and increased engagement of community pharmacists with patient care post-hospital discharge. ISCOMAT patients living with heart failure were interviewed about experiences with the intervention. We sought to gain insights from patients on data collected to enhance our understanding of experiences with the intervention.
To develop a method for involving patients as analysts of qualitative data in a process evaluation.
Design:
Patients and researchers co-analysed qualitative data. A framework method was applied involving; familiarisation, coding, developing an analytical framework and interpretation. The process was facilitated through home working and a workshop with a training component.
Results:
The co-designed framework enabled researchers to map all further patient interview data. Patients' specialist knowledge enhanced understanding of how the ISCOMAT intervention can be best implemented.
Conclusions:
Patients’ unique experiences can enhance validity and rigour in data analysis through sharing their interpretations of qualitative data. The involvement process is crucial in elucidating knowledge and avoiding tokenism. As analysts, patients gain an appreciation of research processes, building trust between researchers and patients. Group dynamics and involving patients throughout the whole research process are important considerations.NIHR Grant RP-PG-0514-20009. NIHR Yorkshire and Humber Patient Safety Translational Research Centre
Online Anomaly Detection for Time Series. Towards Incorporating Feature Extraction, Model Uncertainty and Concept Drift Adaptation for Improving Anomaly Detection
No full textTime series anomaly detection receives increasing research interest given
the growing number of data-rich application domains. Recent additions
to anomaly detection methods in research literature include deep learning
algorithms. The nature and performance of these algorithms in sequence
analysis enable them to learn hierarchical discriminating features
and time-series temporal nature. However, their performance is affected
by the speed at which the time series arrives, the use of a fixed threshold,
and the assumption of Gaussian distribution on the prediction error
to identify anomalous values. An exact parametric distribution is often
not directly relevant in many applications and it’s often difficult to select
an appropriate threshold that will differentiate anomalies with noise.
Thus, implementations need the Prediction Interval (PI) that quantifies the
level of uncertainty associated with the Deep Neural Network (DNN) point
forecasts, which helps in making a better-informed decision and mitigates
against false anomaly alerts. To achieve this, a new anomaly detection
method is proposed that computes the uncertainty in estimates using quantile
regression and used the quantile interval to identify anomalies. Similarly,
to handle the speed at which the data arrives, an online anomaly detection
method is proposed where a model is trained incrementally to adapt
to the concept drift that improves prediction. This is implemented using a
window-based strategy, in which a time series is broken into sliding windows
of sub-sequences as input to the model. To adapt to concept drift,
the model is updated when changes occur in the new arrival instances.
This is achieved by using anomaly likelihood which is computed using the
Q-function to define the abnormal degree of the current data point based
on the previous data points. Specifically, when concept drift occurs, the
proposed method will mark the current data point as anomalous. However,
when the abnormal behavior continues for a longer period of time,
the abnormal degree of the current data point will be low compared to the
previous data points using the likelihood. As such, the current data point is
added to the previous data to retrain the model which will allow the model
to learn the new characteristics of the data and hence adapt to the concept
changes thereby redefining the abnormal behavior. The proposed method
also incorporates feature extraction to capture structural patterns in the
time series. This is especially significant for multivariate time-series data,
for which there is a need to capture the complex temporal dependencies
that may exist between the variables. In summary, this thesis contributes
to the theory, design, and development of algorithms and models for the
detection of anomalies in both static and evolving time series data.
Several experiments were conducted, and the results obtained indicate the
significance of this research on offline and online anomaly detection in
both static and evolving time-series data. In chapter 3, the newly proposed
method (Deep Quantile Regression Anomaly Detection Method) is evaluated
and compared with six other prediction-based anomaly detection
methods that assume a normal distribution of prediction or reconstruction
error for the identification of anomalies. Results in the first part of
the experiment indicate that DQR-AD obtained relatively better precision
than all other methods which demonstrates the capability of the method
in detecting a higher number of anomalous points with low false positive
rates. Also, the results show that DQR-AD is approximately 2 – 3
times better than the DeepAnT which performs better than all the remaining
methods on all domains in the NAB dataset. In the second part of the
experiment, sMAP dataset is used with 4-dimensional features to demonstrate
the method on multivariate time-series data. Experimental result
shows DQR-AD have 10% better performance than AE on three datasets
(SMAP1, SMAP3, and SMAP5) and equal performance on the remaining
two datasets. In chapter 5, two levels of experiments were conducted
basis of false-positive rate and concept drift adaptation. In the first level
of the experiment, the result shows that online DQR-AD is 18% better
than both DQR-AD and VAE-LSTM on five NAB datasets. Similarly, results
in the second level of the experiment show that the online DQR-AD
method has better performance than five counterpart methods with a relatively
10% margin on six out of the seven NAB datasets. This result
demonstrates how concept drift adaptation strategies adopted in the proposed
online DQR-AD improve the performance of anomaly detection in
time series.Petroleum Technology Development Fund (PTDF
Diagnostics and prognostics for complex systems: A review of methods and challenges
No full textYesDiagnostics and prognostics have significant roles in the reliability enhancement of systems and are focused topics of active research. Engineered systems are becoming more complex and are subjected to miscellaneous failure modes that impact adversely their performability. This ever increasing complexity makes fault diagnostics and prognostics challenging for the system-level functions. A significant number of successes have been achieved and acknowledged in some review papers; however, these reviews rarely focused on the application of complex engineered systems nor provided a systematic review of diverse techniques and approaches to address the related challenges. To bridge the gap, this paper firstly presents a review to systematically cover the general concepts and recent development of various diagnostics and prognostics approaches, along with their strengths and shortcomings for the application of diverse engineered systems.
Afterward, given the characteristics of complex systems, the applicability of different techniques and methods that are capable to address the features of complex systems are reviewed and discussed, and some of the recent achievements in the literature are introduced. Finally, the unaddressed challenges are discussed by taking into account the characteristics of automotive systems as an example of complex systems. In addition, future development and potential research trends are offered to address those challenges. Consequently, this review provides a systematic view of the state of the art and case studies with a reference value for scholars and practitioners
Reusable rainwater quality at Ikorodu area of Lagos, Nigeria: Impact of first-flush and household treatment techniques
No full textYesWater scarcity is a huge problem in Africa, and hence rainwater becomes a crucial water source for fulfilling basic human needs. However, less attention has been given by African countries to the effectiveness of common rainwater treatments to ensure the population's health. This study investigates the impact of different household treatment techniques (HHTTs), i.e. treatments by chlorine, boiling, alum, and a combination of alum and chlorine, on its storage system using a case study at the Ikorodu area of Lagos state, which is a rural area in Nigeria. The first-flush quality has been particularly studied here, where the microbial reduction through its practice has been examined from five different roofs. One of the investigated roofs was from a residential building, and four were constructed for the purpose of this study. In this study, the physical parameters (i.e. total suspended solids and turbidity) and the microbial parameters (i.e. total coliform and Escherichia coli) of the collected rainwater have been investigated. From the results, it has been observed that: (1) the water quality at the free phase zone is better than that at the tank's bottom; (2) the combination of chlorine and alum gives the best rainwater quality after comparing the application of different HHTTs; and (3) a reduction of about 40% from the original contaminant load occurs in every 1 mm diversion.Hidden Histories of Environmental Science Grant Project (at Seed-grant Stage), funded by the Natural Environment Research Council (NERC) and Arts and Humanities Research Council (AHRC), part of UK Research and Innovation (UKRI
Performance of Multimodal Biometric Systems Using Face and Fingerprint (Short Survey)
No full textYesBiometric authentication is the science and engineering of assessing and evaluating bioinformatics from the human body in order to increase system security by providing reliable and accurate behaviors and classifiers for personal identification and authentication. Its solutions are widely used in industries, governments, and the military. This paper reviews the multimodal biometric systems that integrated both faces and fingerprints as well as shows which one has the best accuracy and hardware complexity with the methods and databases. Several methods have been used in multimodal biometric systems such as KNN (K-Nearest Neighbor), CNN (Convolutional Neural Network), PCA (Principal Component Analysis), and so on. A multimodal biometric system for face and fingerprints that uses an FoM (Figure of Merit) to compare and show between the articles the best accuracy that have used multimodal biometric system face and fingerprints methods. The best performance has been found is 99.43% by using the cascade multimodal method.Horizon-MSCA-RISE-2019-2023, Marie Sklodowska-Curi
Quantum ReLU activation for Convolutional Neural Networks to improve diagnosis of Parkinson’s disease and COVID-19
No full textYesThis study introduces a quantum-inspired computational paradigm to address the unresolved problem of Convolutional Neural Networks (CNNs) using the Rectified Linear Unit (ReLU) activation function (AF), i.e., the ‘dying ReLU’. This problem impacts the accuracy and the reliability in image classification tasks for critical applications, such as in healthcare. The proposed approach builds on the classical ReLU and Leaky ReLU, applying the quantum principles of entanglement and superposition at a computational level to derive two novel AFs, respectively the ‘Quantum ReLU’ (QReLU) and the ‘modified-QReLU’ (m-QReLU). The proposed AFs were validated when coupled with a CNN using seven image datasets on classification tasks involving the detection of COVID-19 and Parkinson’s Disease (PD). The out-of-sample/test classification accuracy and reliability (precision, recall and F1-score) of the CNN were compared against those of the same classifier when using nine classical AFs, including ReLU-based variations. Findings indicate higher accuracy and reliability for the CNN when using either QReLU or m-QReLU on five of the seven datasets evaluated. Whilst retaining the best classification accuracy and reliability for handwritten digits recognition on the MNIST dataset (ACC = 99%, F1-score = 99%), avoiding the ‘dying ReLU’ problem via the proposed quantum AFs improved recognition of PD-related patterns from spiral drawings with the QReLU especially, which achieved the highest classification accuracy and reliability (ACC = 92%, F1-score = 93%). Therefore, with these increased accuracy and reliability, QReLU and m-QReLU can aid critical image classification tasks, such as diagnoses of COVID-19 and PD.HEIF 2020 University of Bradford COVID-19 response-funded project ‘Quantum ReLU-based COVID-19 Detector: A Quantum Activation Function for Deep Learning to Improve Diagnostics and Prognostics of COVID-19 from Non-ionising Medical Imaging’. However, the funding source was not involved in conducting the study and/or preparing the article
Sustainable energy for national climate change, food security and employment opportunities: Implications for Nigeria
No full textYesThis paper looks into the issues around renewable energy with a view to identify the opportunities for Nigeria
and critically review the nation’s renewable energy policy vis-`a-vis the efforts and achievement of governments
and indigenous practitioners. It identified the inherent opportunities of renewable energy resources at ameliorating
the incidents of climate change and global warming and also surveyed international statistics on the relationships
between energy and renewable energy adoption, national development, population explosion, job
creation and rural-urban integration. It found out that for Nigeria to sustain economic growth especially as it
relates to agriculture and food security, renewable energy for power generation must be included in the nation’s
rural development plan. It also demonstrated that renewable energy poses an opportunity for mitigating the
nation’s contributions to anthropogenic climate change.This work was supported by the British Academy Global Challenges Research Fund under Grant number GCRFNGR3\1541 and Covenant University, Ota, Nigeria
Self-aeration development and fully cross-sectional air diffusion in high-speed open channel flows
No full textYesSelf-aeration in open channel flows occurs owing to free surface air entrainment. Self-aeration development and fully cross-sectional distribution of air concentration are not thoroughly understood. In the present study, an analytical solution for the averaged cross-sectional air concentration in the gradually varying region is established using a simplified mechanism of free surface air entrainment. For a fully cross-sectional distribution of air concentration affected by the channel bottom, a model of a diffusion region without wall restraint is proposed, and two situations are classified based on averaged cross-sectional air concentration. Good agreement between measured data and calculations is obtained, and the computational accuracy of the air concentration distribution near the wall is improved. The results reveal that the channel slope determines the air entrainment quantity, while water flow discharge determines the self-aeration evolution distance. The solutions for the averaged cross-sectional air concentration and the effect of the bottom wall on air diffusion promote air–water flow applications in hydraulic engineering practices.This work was supported by National Natural Science Foundation of China [grant numbers 51939007, 51979183]; Sichuan Province Science and Technology support program [grant number 2019JDTD0007]