Multi-objective Transmission Planning Paper

2008-2009 > Academic research: refereed > Refereed conference pape

Probabilistic forecasting of wind power generation using extreme learning machine.

Accurate and reliable forecast of wind power is essential to power system operation and control. However, due to the nonstationarity of wind power series, traditional point forecasting can hardly be accurate, leading to increased uncertainties and risks for system operation. This paper proposes an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation. To account for the uncertainties in the forecasting results, several bootstrap methods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified with the best performance. Consequently, a new method for prediction intervals formulation based on the ELM and the pairs bootstrap is developed. Wind power forecasting has been conducted in different seasons using the proposed approach with the historical wind power time series as the inputs alone. The results demonstrate that the proposed method is effective for probabilistic forecasting of wind power generation with a high potential for practical applications in power systems

Advanced Control Strategy of DFIG Wind Turbines for Power System Fault Ride Through

This paper presents an advanced control strategy for the rotor and grid side converters of the doubly fed induction generator (DFIG) based wind turbine (WT) to enhance the low-voltage ride-through (LVRT) capability according to the grid connection requirement. Within the new control strategy, the rotor side controller can convert the imbalanced power into the kinetic energy of the WT by increasing its rotor speed, when a low voltage due to a grid fault occurs at, e.g., the point of common coupling (PCC). The proposed grid side control scheme introduces a compensation term reflecting the instantaneous DC-link current of the rotor side converter in order to smooth the DC-link voltage fluctuations during the grid fault. A major difference from other methods is that the proposed control strategy can absorb the additional kinetic energy during the fault conditions, and significantly reduce the oscillations in the stator and rotor currents and the DC bus voltage. The effectiveness of the proposed control strategy has been demonstrated through various simulation cases. Compared with conventional crowbar protection, the proposed control method can not only improve the LVRT capability of the DFIG WT, but also help maintaining continuous active and reactive power control of the DFIG during the grid faults

Use of low-dose computed tomography to assess pulmonary tuberculosis among healthcare workers in a tuberculosis hospital

BACKGROUND: According to the World Health Organization, China is one of 22 countries with serious tuberculosis (TB) infections and one of the 27 countries with serious multidrug-resistant TB strains. Despite the decline of tuberculosis in the overall population, healthcare workers (HCWs) are still at a high risk of infection. Compared with high-income countries, the TB prevalence among HCWs is higher in low- and middle-income countries. Low-dose computed tomography (LDCT) is becoming more popular due to its superior sensitivity and lower radiation dose. However, there have been no reports about active pulmonary tuberculosis (PTB) among HCWs as assessed with LDCT. The purposes of this study were to examine PTB statuses in HCWs in hospitals specializing in TB treatment and explore the significance of the application of LDCT to these workers. METHODS: This study retrospectively analysed the physical examination data of healthcare workers in the Beijing Chest Hospital from September 2012 to December 2015. Low-dose lung CT examinations were performed in all cases. The comparisons between active and inactive PTB according to the CT findings were made using the Pearson chi-square test or the Fisher’s exact test. Comparisons between the incidences of active PTB in high-risk areas and non-high-risk areas were performed using the Pearson chi-square test. Analyses of active PTB were performed according to different ages, numbers of years on the job, and the risks of the working areas. Active PTB as diagnosed by the LDCT examinations alone was compared with the final comprehensive diagnoses, and the sensitivity and positive predictive value were calculated. RESULTS: A total of 1 012 participants were included in this study. During the 4-year period of medical examinations, active PTB was found in 19 cases, and inactive PTB was found in 109 cases. The prevalence of active PTB in the participants was 1.24%, 0.67%, 0.81%, and 0.53% for years 2012 to 2015. The corresponding incidences of active PTB among the tuberculosis hospital participants were 0.86%, 0.41%, 0.54%, and 0.26%. Most HCWs with active TB (78.9%, 15/19) worked in the high-risk areas of the hospital. There was a significant difference in the incidences of active PTB between the HCWs who worked in the high-risk and non-high-risk areas (odds ratio [OR], 14.415; 95% confidence interval (CI): 4.733 – 43.896). Comparisons of the CT signs between the active and inactive groups via chi-square tests revealed that the tree-in-bud, cavity, fibrous shadow, and calcification signs exhibited significant differences (P = 0.000, 0.021, 0.001, and 0.024, respectively). Tree-in-bud and cavity opacities suggest active pulmonary tuberculosis, whereas fibrous shadow and calcification opacities are the main features of inactive pulmonary tuberculosis. Comparison with the final comprehensive diagnoses revealed that the sensitivity and positive predictive value of the diagnoses of active PTB based on LDCT alone were 100% and 86.4%, respectively. CONCLUSIONS: Healthcare workers in tuberculosis hospitals are a high-risk group for active PTB. Yearly LDCT examinations of such high-risk groups are feasible and necessary. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40249-017-0274-6) contains supplementary material, which is available to authorized users

Oscillatory Stability and Eigenvalue Sensitivity Analysis of A DFIG Wind Turbine System

2010-2011 > Academic research: refereed > Publication in refereed journa

Lifelogging Data Validation Model for Internet of Things enabled Personalized Healthcare

The rapid advance of the Internet of Things (IoT) technology offers opportunities to monitor lifelogging data by a variety of IoT assets, like wearable sensors, mobile apps, etc. But due to heterogeneity of connected devices and diverse life patterns in an IoT environment, lifelogging personal data contains much uncertainty and are hardly used for healthcare studies. Effective validation of lifelogging personal data for longitudinal health assessment is demanded. In this paper, it takes lifelogging physical activity as a target to explore the possibility of improving validity of lifelogging data in an IoT based healthcare environment. A rule based adaptive lifelogging physical activity validation model, LPAV-IoT, is proposed for eliminating irregular uncertainties and estimating data reliability in IoT healthcare environments. In LPAV-IoT, a methodology specifying four layers and three modules is presented for analyzing key factors impacting validity of lifelogging physical activity. A series of validation rules are designed with uncertainty threshold parameters and reliability indicators and evaluated through experimental investigations. Following LPAV-IoT, a case study on an IoT enabled personalized healthcare platform MHA [38] connecting three state-of-the-art wearable devices and mobile apps are carried out. The results reflect that the rules provided by LPAV-IoT enable efficiently filtering at least 75% of irregular uncertainty and adaptively indicating the reliability of lifelogging physical activity data on certain condition of an IoT personalized environment

Improved neonatal outcomes by multidisciplinary simulation—a contemporary practice in the demonstration area of China

BackgroundSimulation-based training improves neonatal resuscitation and decreases perinatal mortality in low- and middle-income countries. Interdisciplinary in-situ simulation may promote quality care in neonatal resuscitation. However, there is limited information regarding the effect of multidisciplinary in-situ simulation training (MIST) on neonatal outcomes. We aimed to investigate the impact of MIST on neonatal resuscitation in reducing the incidence of neonatal asphyxia and related morbidities.MethodsWeekly MIST on neonatal resuscitation has been conducted through neonatal and obstetrical collaboration at the University of Hong Kong-Shenzhen Hospital, China, since 2019. Each simulation was facilitated by two instructors and performed by three health care providers from obstetric and neonatal intensive care units, followed by a debriefing of the participants and several designated observers. The incidence of neonatal asphyxia, severe asphyxia, hypoxic-ischemic encephalopathy (HIE), and meconium aspiration syndrome (MAS) before (2017–2018) and after (2019–2020) the commencement of weekly MIST were analyzed.ResultsThere were 81 simulation cases including the resuscitation of preterm neonates of different gestational ages, perinatal distress, meconium-stained amniotic fluid, and congenital heart disease with 1,503 participant counts (225 active participants). The respective incidence of neonatal asphyxia, severe asphyxia, HIE, and MAS decreased significantly after MIST (0.64%, 0.06%, 0.01%, and 0.09% vs. 0.84%, 0.14%, 0.10%, and 0.19%, respectively, all P < 0.05).ConclusionsWeekly MIST on neonatal resuscitation decreased the incidence of neonatal asphyxia, severe asphyxia, HIE, and MAS. Implementation of regular resuscitation simulation training is feasible and may improve the quality of neonatal resuscitation with better neonatal outcomes in low- and middle-income countries

Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning

The performance of deep learning-based methods strongly relies on the number of datasets used for training. Many efforts have been made to increase the data in the medical image analysis field. However, unlike photography images, it is hard to generate centralized databases to collect medical images because of numerous technical, legal, and privacy issues. In this work, we study the use of federated learning between two institutions in a real-world setting to collaboratively train a model without sharing the raw data across national boundaries. We quantitatively compare the segmentation models obtained with federated learning and local training alone. Our experimental results show that federated learning models have higher generalizability than standalone training.Comment: Accepted by MICCAI DCL Workshop 202

GSWO: A Programming Model for GPU-enabled Parallelization of Sliding Window Operations in Image Processing

Sliding Window Operations (SWOs) are widely used in image processing applications. They often have to be performed repeatedly across the target image, which can demand significant computing resources when processing large images with large windows. In applications in which real-time performance is essential, running these filters on a CPU often fails to deliver results within an acceptable timeframe. The emergence of sophisticated graphic processing units (GPUs) presents an opportunity to address this challenge. However, GPU programming requires a steep learning curve and is error-prone for novices, so the availability of a tool that can produce a GPU implementation automatically from the original CPU source code can provide an attractive means by which the GPU power can be harnessed effectively. This paper presents a GPUenabled programming model, called GSWO, which can assist GPU novices by converting their SWO-based image processing applications from the original C/C++ source code to CUDA code in a highly automated manner. This model includes a new set of simple SWO pragmas to generate GPU kernels and to support effective GPU memory management. We have implemented this programming model based on a CPU-to-GPU translator (C2GPU). Evaluations have been performed on a number of typical SWO image filters and applications. The experimental results show that the GSWO model is capable of efficiently accelerating these applications, with improved applicability and a speed-up of performance compared to several leading CPU-to- GPU source-to-source translators

A Role for the Inflammasome in Spontaneous Labor at Term

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143689/1/aji12440.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143689/2/aji12440_am.pd