As the share of power converter-based renewable energy sources (RESs) is high, a microgrid, in islanded mode, is more vulnerable to frequency instability due to (1) sudden power imbalance and (2) low ...inertia. One of the most common approaches to address this issue is to provide virtual inertia to the system by appropriately controlling the grid-side converter of the RESs. However, the primary frequency controller (PFC) presented in this paper focuses on the fast compensation of power imbalance without adding inertia to the system. The proposed method is based on estimating the real-time power imbalance caused by a disturbance and compensating it using multiple small-scale distributed battery energy storage systems (BESSs). The power imbalance is estimated by observing the initial rate of change of frequency (RoCoF) following a disturbance. Based on the estimated power imbalance and the rating of the BESSs, the reference power for the BESSs is determined. The BESSs are controlled in grid-following mode to compensate for the power imbalance. The performance of the proposed PFC is verified using a Typhoon real-time simulator for various scenarios and is compared with the conventional virtual synchronous generator (VSG) controller.
While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently, there has been a rising trend of ...employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services, such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The growing interest in applying unsupervised learning techniques in networking stems from their great success in other fields, such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). In addition, unsupervised learning can unconstrain us from the need for labeled data and manual handcrafted feature engineering, thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting recent advancements in unsupervised learning techniques, and describe their applications in various learning tasks, in the context of networking. We also provide a discussion on future directions and open research issues, while identifying potential pitfalls. While a few survey papers focusing on applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in the literature. Through this timely review, we aim to advance the current state of knowledge, by carefully synthesizing insights from previous survey papers, while providing contemporary coverage of the recent advances and innovations.
Abstract
The problem of Lip-reading has become an important research challenge in recent years. The goal is to recognise speech from lip movements. Most of the Lip-reading technologies developed so ...far are camera-based, which require video recording of the target. However, these technologies have well-known limitations of occlusion and ambient lighting with serious privacy concerns. Furthermore, vision-based technologies are not useful for multi-modal hearing aids in the coronavirus (COVID-19) environment, where face masks have become a norm. This paper aims to solve the fundamental limitations of camera-based systems by proposing a radio frequency (RF) based Lip-reading framework, having an ability to read lips under face masks. The framework employs Wi-Fi and radar technologies as enablers of RF sensing based Lip-reading. A dataset comprising of vowels A, E, I, O, U and empty (static/closed lips) is collected using both technologies, with a face mask. The collected data is used to train machine learning (ML) and deep learning (DL) models. A high classification accuracy of 95% is achieved on the Wi-Fi data utilising neural network (NN) models. Moreover, similar accuracy is achieved by VGG16 deep learning model on the collected radar-based dataset.
Islanded microgrids have low inertia due to a large penetration of non-inertial inverter based power sources. In such systems, the primary frequency controller (PFC) faces the issue of a higher rate ...of change of frequency (RoCoF) and large peak frequency deviation in case of a sudden change in load or generation loss. The frequency control becomes more challenging as the variation in the frequency of different sources is not synchronized. This paper proposes a model for frequency dynamics in an islanded microgrid comprised of both inverter based distributed generators (DGs) and synchronous generators (SGs). The model is developed considering the asynchronous variation of frequency among the SGs. Based on the developed model, a novel disturbance compensation-based PFC is proposed. The PFC controls the reference power of a battery energy storage system (BESS) which is operated in grid-following mode and compensates for the power imbalance in the microgrid. The performance of the proposed model and the PFC is verified using Typhoon real-time hardware-in-the-loop simulation.
Customer retention is a major issue for various service-based organizations particularly telecom industry, wherein predictive models for observing the behavior of customers are one of the great ...instruments in customer retention process and inferring the future behavior of the customers. However, the performances of predictive models are greatly affected when the real-world data set is highly imbalanced. A data set is called imbalanced if the samples size from one class is very much smaller or larger than the other classes. The most commonly used technique is over/under sampling for handling the class-imbalance problem (CIP) in various domains. In this paper, we survey six well-known sampling techniques and compare the performances of these key techniques, i.e., mega-trend diffusion function (MTDF), synthetic minority oversampling technique, adaptive synthetic sampling approach, couples top-N reverse k-nearest neighbor, majority weighted minority oversampling technique, and immune centroids oversampling technique. Moreover, this paper also reveals the evaluation of four rules-generation algorithms (the learning from example module, version 2 (LEM2), covering, exhaustive, and genetic algorithms) using publicly available data sets. The empirical results demonstrate that the overall predictive performance of MTDF and rules-generation based on genetic algorithms performed the best as compared with the rest of the evaluated oversampling methods and rule-generation algorithms.
Predictive control offers many advantages such as simple design and a systematic way to handle constraints. Model predictive control (MPC) belongs to predictive control, which uses a model of the ...system for predictions used in predictive control. A major drawback of MPC is the dependence of its performance on the model of the system. Any discrepancy between the system model and actual plant behavior will greatly affect the performance of the MPC. Recently, model-free approaches have been gaining attention because they are not dependent on the system model parameters. To obtain the advantages of both a model-free approach and predictive control, model-free predictive control (MFPC) is being explored and reported in the literature for different applications such as power electronics and electric drives. This paper presents an overview of model-free predictive control. A comprehensive review of the application of MFPC in power converters, electric drives, power systems, and microgrids is presented in this paper. Moreover, challenges, opportunities, and emerging trends in MFPC are also discussed in this paper.
The lucrative features of cloud computing such as pay-as-you-go pricing model and dynamic resource provisioning (elasticity) attract clients to host their applications over the cloud to save up-front ...capital expenditure and to reduce the operational cost of the system. However, the efficient management of hired computational resources is a challenging task. Over the last decade, researchers and practitioners made use of various techniques to propose new methods to address cloud elasticity. Amongst many such techniques, control theory emerges as one of the popular methods to implement elasticity. A plethora of research has been undertaken on cloud elasticity including several review papers that summarise various aspects of elasticity. However, the scope of the existing review articles is broad and focused mostly on the high-level view of the overall research works rather than on the specific details of a particular implementation technique. While considering the importance, suitability and abundance of control theoretical approaches, this paper is a step forward towards a stand-alone review of control theoretic aspects of cloud elasticity. This paper provides a detailed taxonomy comprising of relevant attributes defining the following two perspectives, i.e., control-theory as an implementation technique as well as cloud elasticity as a target application domain. We carry out an exhaustive review of the literature by classifying the existing elasticity solutions using the attributes of control theoretic perspective. The summarized results are further presented by clustering them with respect to the type of control solutions, thus helping in comparison of the related control solutions. In last, a discussion summarizing the pros and cons of each type of control solutions are presented. This discussion is followed by the detail description of various open research challenges in the field.
The manufacturing of nanomaterials by the electrospinning process requires accurate and meticulous inspection of related scanning electron microscope ( SEM ) images of the electrospun ...nanofiber, to ensure that no structural defects are produced. The presence of anomalies prevents practical application of the electrospun nanofibrous material in nanotechnology. Hence, the automatic monitoring and quality control of nanomaterials is a relevant challenge in the context of Industry 4.0. In this paper, a novel automatic classification system for homogenous ( anomaly-free ) and non-homogenous ( with defects ) nanofibers is proposed. The inspection procedure aims at avoiding direct processing of the redundant full SEM image. Specifically, the image to be analyzed is first partitioned into sub-images ( nanopatches ) that are then used as input to a hybrid unsupervised and supervised machine learning system. In the first step, an autoencoder ( AE ) is trained with unsupervised learning to generate a code representing the input image with a vector of relevant features. Next, a multilayer perceptron ( MLP ) , trained with supervised learning, uses the extracted features to classify non-homogenous nanofiber ( NH-NF ) and homogenous nanofiber ( H-NF ) patches. The resulting novel AE-MLP system is shown to outperform other standard machine learning models and other recent state-of-the-art techniques, reporting accuracy rate up to 92.5% . In addition, the proposed approach leads to model complexity reduction with respect to other deep learning strategies such as convolutional neural networks ( CNN ) . The encouraging performance achieved in this benchmark study can stimulate the application of the proposed scheme in other challenging industrial manufacturing tasks.
Cenani-Lenz syndrome (CLS) is a rare autosomal-recessive congenital disorder affecting development of distal limbs. It is characterized mainly by syndactyly and/or oligodactyly, renal anomalies, and ...characteristic facial features. Mutations in the LRP4 gene, located on human chromosome 11p11.2-q13.1, causes the CLS. The gene LRP4 encodes a low-density lipoprotein receptor-related protein-4, which mediates SOST-dependent inhibition of bone formation and Wnt signaling. In the study, presented here, three families of Pakistani origin, segregating CLS in the autosomal recessive manner were clinically and genetically characterized. In two families (A and B), microsatellite-based homozygosity mapping followed by Sanger sequencing identified a novel homozygous missense variant NM_002334.3: c.295G>C; p.(Asp99His) in the LRP4 gene. In the third family C, exome sequencing revealed a second novel homozygous missense variant NM_002334.3: c.1633C>T; p.(Arg545Trp) in the same gene. To determine the functional relevance of these variants, we tested their ability to inhibit canonical WNT signaling in a luciferase assay. Wild type LRP4 was able to inhibit LRP6-dependent WNT signaling robustly. The two mutants p.(Asp99His) and p.(Arg545Trp) inhibited WNT signaling less effectively, suggesting they reduced LRP4 function.
Object detection has wide applications in intelligent systems and sensor applications. Compared with two stage detectors, recent one stage counterparts are capable of running more efficiently with ...comparable accuracy, which satisfy the requirement of real-time processing. To further improve the accuracy of one stage single shot detector (SSD), we propose a novel Multi-Path fusion Single Shot Detector (MPSSD). Different from other feature fusion methods, we exploit the connection among different scale representations in a pyramid manner. We propose feature fusion module to generate new feature pyramids based on multiscale features in SSD, and these pyramids are sent to our pyramid aggregation module for generating final features. These enhanced features have both localization and semantics information, thus improving the detection performance with little computation cost. A series of experiments on three benchmark datasets PASCAL VOC2007, VOC2012, and MS COCO demonstrate that our approach outperforms many state-of-the-art detectors both qualitatively and quantitatively. In particular, for input images with size 512 × 512, our method attains mean Average Precision (mAP) of 81.8% on VOC2007 test, 80.3% on VOC2012 test, and 33.1% mAP on COCO test-dev 2015.
PDF
