Design and Optimization of Printed Circuit Board Inductors for Wireless Power Transfer System

Wireless power transfer via inductive link is becoming a popular choice as an alternate powering scheme for biomedical sensor electronics. Spiral printed circuit board (PCB) inductors are gaining attractions for wireless power transfer applications due to their various advantages over conventional inductors such as low-cost, batch fabrication, durability, manufacturability on flexible substrates, etc. In this work, design of a multi-spiral stacked solenoidal inductor for biomedical application in 13.56 MHz band is presented. Proposed stacking method enhances the inductance density of the inductor for a given area. This paper reports an optimization technique for design and implementation of the PCB inductors. The proposed scheme shows higher inductance and better figure-of-merit values compared to PCB inductors reported in literature, which are desirable for wireless power transfer system. DOI: 10.4236/cs.2013.4203

Two-Stage Approach for the Assessment of Distributed Generation Capacity Mixture in Active Distribution Networks

Distribution networks are limited with spare capacities to integrate increased volumes of distributed generation (DG). Network constraints and congestion, dynamic thermal limits, intermittent outputs, and the need for reduction in greenhouse gas emission increase the complexity of capturing optimal DG mixture that can safely permit the optimal operation. This paper investigates this problem in detail and proposes a two-stage approach for the quantification of optimal DG capacity mixture in an active distribution network. The approach is aimed at operational planning and takes into account dynamic thermal limits, network internal benefit, and network external benefit and then optimizes samples of DG mixtures through sequential simulation. A case study is performed incorporating wind and photovoltaic generation as intermittent DG and diesel units as standing reserve units. Results suggest that specific operating conditions in an active distribution network can dominate the optimal DG mixture. Wind and diesel hybrid operation can be the most beneficial DG mixture compared to any other DG combination. Dynamic thermal limits of assets can potentially control the type of DG of the optimized mixture

A new fuzzy logic approach for consistent interpretation of dissolved gas-in-oil analysis

Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results however all these techniques rely on personnel experience more than analytical formulation. As a result, various interpretation techniques do not necessarily lead to the same conclusion for the same oil sample. Furthermore, significant number of DGA results fall outside the proposed codes of the current based-ratio interpretation techniques and cannot be diagnosed by these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced gases. To overcome these limitations, this paper introduces a new fuzzy logic approach to reduce dependency on expert personnel and to aid in standardizing DGA interpretation techniques. The approach relies on incorporating all existing DGA interpretation techniques into one expert model. DGA results of 2000 oil samples that were collected from different transformers of different rating and different life span are used to establish the model. Traditional DGA interpretation techniques are used to analyze the collected DGA results to evaluate the consistency and accuracy of each interpretation technique. Results of this analysis were then used to develop the proposed fuzzy logic model

Online optimal variable charge-rate coordination of plug-in electric vehicles to maximize customer satisfaction and improve grid performance

© 2016 Elsevier B.V. Participation of plug-in electric vehicles (PEVs) is expected to grow in emerging smart grids. A strategy to overcome potential grid overloading caused by large penetrations of PEVs is to optimize their battery charge-rates to fully explore grid capacity and maximize the customer satisfaction for all PEV owners. This paper proposes an online dynamically optimized algorithm for optimal variable charge-rate scheduling of PEVs based on coordinated aggregated particle swarm optimization (CAPSO). The online algorithm is updated at regular intervals of Δt = 5 min to maximize the customers’ satisfactions for all PEV owners based on their requested plug-out times, requested battery state of charges (SOCReq) and willingness to pay the higher charging energy prices. The algorithm also ensures that the distribution transformer is not overloaded while grid losses and node voltage deviations are minimized. Simulation results for uncoordinated PEV charging as well as CAPSO with fixed charge-rate coordination (FCC) and variable charge-rate coordination (VCC) strategies are compared for a 449-node network with different levels of PEV penetrations. The key contributions are optimal VCC of PEVs considering battery modeling, chargers’ efficiencies and customer satisfaction based on requested plug-out times, driving pattern, desired final SOCs and their interest to pay for energy at a higher rate

A novel augmented deep transfer learning for classification of COVID-19 and other thoracic diseases from X-rays

Deep learning has provided numerous breakthroughs in natural imaging tasks. However, its successful application to medical images is severely handicapped with the limited amount of annotated training data. Transfer learning is commonly adopted for the medical imaging tasks. However, a large covariant shift between the source domain of natural images and target domain of medical images results in poor transfer learning. Moreover, scarcity of annotated data for the medical imaging tasks causes further problems for effective transfer learning. To address these problems, we develop an augmented ensemble transfer learning technique that leads to significant performance gain over the conventional transfer learning. Our technique uses an ensemble of deep learning models, where the architecture of each network is modified with extra layers to account for dimensionality change between the images of source and target data domains. Moreover, the model is hierarchically tuned to the target domain with augmented training data. Along with the network ensemble, we also utilize an ensemble of dictionaries that are based on features extracted from the augmented models. The dictionary ensemble provides an additional performance boost to our method. We first establish the effectiveness of our technique with the challenging ChestXray-14 radiography data set. Our experimental results show more than 50% reduction in the error rate with our method as compared to the baseline transfer learning technique. We then apply our technique to a recent COVID-19 data set for binary and multi-class classification tasks. Our technique achieves 99.49% accuracy for the binary classification, and 99.24% for multi-class classification

Structure-aware image translation-based long future prediction for enhancement of ground robotic vehicle teleoperation

Predicting future frames through image-to-image translation and using these synthetically generated frames for high-speed ground vehicle teleoperation is a new concept to address latency and enhance operational performance. In the immediate previous work, the image quality of the predicted frames was low and a lot of scene detail was lost. To preserve the structural details of objects and improve overall image quality in the predicted frames, several novel ideas are proposed herein. A filter has been designed to remove noise from dense optical flow components resulting from frame rate inconsistencies. The Pix2Pix base network has been modified and a structure-aware SSIM-based perpetual loss function has been implemented. A new dataset of 20 000 training input images and 2000 test input images with a 500 ms delay between the target and input frames has been created. Without any additional video transformation steps, the proposed improved model achieved PSNR of 23.1; SSIM of 0.65; and MS-SSIM of 0.80, a substantial improvement over our previous work. A Fleiss’ kappa score of \u3e 0.40 (0.48 for the modified network and 0.46 for the perpetual loss function) proves the reliability of the model

Long future frame prediction using optical flow informed deep neural networks for enhancement of robotic teleoperation in high latency environments

High latency in teleoperation has a significant negative impact on operator performance. While deep learning has revolutionized many domains recently, it has not previously been applied to teleoperation enhancement. We propose a novel approach to predict video frames deep into the future using neural networks informed by synthetically generated optical flow information. This can be employed in teleoperated robotic systems that rely on video feeds for operator situational awareness. We have used the image-to-image translation technique as a basis for the prediction of future frames. The Pix2Pix conditional generative adversarial network (cGAN) has been selected as a base network. Optical flow components reflecting real-time control inputs are added to the standard RGB channels of the input image. We have experimented with three data sets of 20,000 input images each that were generated using our custom-designed teleoperation simulator with a 500-ms delay added between the input and target frames. Structural Similarity Index Measures (SSIMs) of 0.60 and Multi-SSIMs of 0.68 were achieved when training the cGAN with three-channel RGB image data. With the five-channel input data (incorporating optical flow) these values improved to 0.67 and 0.74, respectively. Applying Fleiss\u27 κ gave a score of 0.40 for three-channel RGB data, and 0.55 for five-channel optical flow-added data. We are confident the predicted synthetic frames are of sufficient quality and reliability to be presented to teleoperators as a video feed that will enhance teleoperation. To the best of our knowledge, we are the first to attempt to reduce the impacts of latency through future frame prediction using deep neural networks

Standardization of DGA interpretation techniques using fuzzy logic approach

Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition monitoring tools. There are many interpretation techniques for DGA results. However, all of these techniques rely on personnel experience more than standard mathematical formulation. As a result, various DGA interpretation techniques do not necessarily lead to the same conclusion for the same oil sample. DGA interpretation is yet a challenge in the power transformer condition monitoring research area. To alleviate this issue, this paper introduces a fuzzy logic approach to help in standardizing DGA results quantification and classification using various interpretation techniques such as key gas, Rogers ratio, IEC ratio, Doernenburg and Duval triangle methods. In this context, DGA results for 2000 oil samples have been collected from different transformers of different ratings, life span and operating conditions. Traditional DGA interpretation techniques are used to analyze the results which are then compared with the results of the fuzzy logic models. Results show that the fuzzy logic models enhance the consistency among all current interpretation techniques and can eliminate the need for expert personal to interpret DGA results

Pre-text Representation Transfer for Deep Learning with Limited Imbalanced Data : Application to CT-based COVID-19 Detection

Annotating medical images for disease detection is often tedious and expensive. Moreover, the available training samples for a given task are generally scarce and imbalanced. These conditions are not conducive for learning effective deep neural models. Hence, it is common to 'transfer' neural networks trained on natural images to the medical image domain. However, this paradigm lacks in performance due to the large domain gap between the natural and medical image data. To address that, we propose a novel concept of Pre-text Representation Transfer (PRT). In contrast to the conventional transfer learning, which fine-tunes a source model after replacing its classification layers, PRT retains the original classification layers and updates the representation layers through an unsupervised pre-text task. The task is performed with (original, not synthetic) medical images, without utilizing any annotations. This enables representation transfer with a large amount of training data. This high-fidelity representation transfer allows us to use the resulting model as a more effective feature extractor. Moreover, we can also subsequently perform the traditional transfer learning with this model. We devise a collaborative representation based classification layer for the case when we leverage the model as a feature extractor. We fuse the output of this layer with the predictions of a model induced with the traditional transfer learning performed over our pre-text transferred model. The utility of our technique for limited and imbalanced data classification problem is demonstrated with an extensive five-fold evaluation for three large-scale models, tested for five different class-imbalance ratios for CT based COVID-19 detection. Our results show a consistent gain over the conventional transfer learning with the proposed method.Comment: Best paper at IVCN