Measurement-based correlation approach for power system dynamic response estimation

Understanding power system dynamics is essential for online stability assessment and control applications. Global positioning system-synchronised phasor measurement units and frequency disturbance recorders (FDRs) make power system dynamics visible and deliver an accurate picture of the overall operation condition to system operators. However, in the actual field implementations, some measurement data can be inaccessible for various reasons, for example, most notably failure of communication. In this study, a measurement-based approach is proposed to estimate the missing power system dynamics. Specifically, a correlation coefficient index is proposed to describe the correlation relationship between different measurements. Then, the auto-regressive with exogenous input identification model is employed to estimate the missing system dynamic response. The US Eastern Interconnection is utilised in this study as a case study. The robustness of the correlation approach is verified by a wide variety of case studies as well. Finally, the proposed correlation approach is applied to the real FDR data for power system dynamic response estimation. The results indicate that the correlation approach could help select better input locations and thus improve the response estimation accuracy

Photolithographic Approaches for Fabricating Highly Ordered Nanopatterned Arrays

In this work, we report that large area metal nanowire and polymer nanotube arrays were successfully patterned by photolithographic approach using anodic aluminum oxide (AAO) templates. Nanowires were produced by electrochemical deposition, and nanotubes by solution-wetting. The highly ordered patterns of nanowire and nanotube arrays were observed using scanning electron microscopy (SEM) and found to stand free on the substrate. The method is expected to play an important role in the application of microdevices in the future

Household, community, sub-national and country-level predictors of primary cooking fuel switching in nine countries from the PURE study

Introduction. Switchingfrom polluting (e.g. wood, crop waste, coal)to clean (e.g. gas, electricity) cooking fuels can reduce household air pollution exposures and climate-forcing emissions.While studies have evaluated specific interventions and assessed fuel-switching in repeated cross-sectional surveys, the role of different multilevel factors in household fuel switching, outside of interventions and across diverse community settings, is not well understood. Methods.We examined longitudinal survey data from 24 172 households in 177 rural communities across nine countries within the Prospective Urban and Rural Epidemiology study.We assessed household-level primary cooking fuel switching during a median of 10 years offollow up (∼2005–2015).We used hierarchical logistic regression models to examine the relative importance of household, community, sub-national and national-level factors contributing to primary fuel switching. Results. One-half of study households(12 369)reported changing their primary cookingfuels between baseline andfollow up surveys. Of these, 61% (7582) switchedfrom polluting (wood, dung, agricultural waste, charcoal, coal, kerosene)to clean (gas, electricity)fuels, 26% (3109)switched between different polluting fuels, 10% (1164)switched from clean to polluting fuels and 3% (522)switched between different clean fuels

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Optimal control of networked systems using reinforcement learning

The trend of using wireless communication channel in network control system increases a lot, because of its flexibility and mobility. Improving system performance with simple devices, such as low storage capacity sensors and low transmission power channel, is very important to ensure long life time. Hence, there is interest in system communication and controller design to optimize the information used by devices, so as to maintain overall system performance. This thesis explores an approach to co-design of communication and control. First of all, the design of encoder and controller pair for feedback control systems over binary symmetric channels is concerned. An iterative design method based on Q-learning is proposed to obtain a pair of encoder and controller that can optimize a finite-horizon linear quadratic cost function. Three encoder strategies, memoryless encoder, memory encoder and predictive encoder, are considered. The proposed design can be implemented online, and has the potential to provide better performance. Compared with traditional control optimization method, the proposed design method is model-free, only data measured along with the system trajectories is utilized. Simulations are provided to show the effectiveness and the merits of the proposed method. Only finite channel inputs and finite outputs is considered in previous work, while there are some infinite channel output models in practical. Hence, we studies how the generalization to infinite-output channels affected the optimization of the encoder-controller, theoretically and practically, by studying one special type of infinite output channels, namely, Gaussian channel. Since the infinite-channel outputs mainly affect the controller design, we devote to controller design, which are soft controller design, hard controller design and the combination. From above considerations, all the research works are based on iterative design method, which means the encoder is optimized with fixed controller and the controller is optimized with fixed encoder. However, only local optimal solutions can be got by iterative design. Therefore, distributed encoder and controller design is proposed. Both encoder and controller learn independently with their own local information, and both of them can be optimized simultaneously. Obviously, the system performance is better than iterative design. In addition, distributed Qlearning can be applied into complex networked control systems

Parameterised function ILC with application to stroke rehabilitation

Functional electrical stimulation (FES) is a popular assistive technology that uses electrical impulses to artificially stimulate muscles to help paralysed or impaired subjects regain their lost movement after stroke. A large number of FES elements can be combined to form FES arrays which are capable of activating the multiple muscles needed to perform functional arm movements. However, the control of FES arrays is challenging since high precision is required but there is little time available in a clinical or home setting to identify a model. To date, by far the highest accuracy has been achieved using iterative learning control (ILC), a technique that mirrors the repeated nature of rehabilitation task practice. In particular, high accuracy has been achieved using a well-known ILC law for a general class of nonlinear systems which computes the updated control input using a linearised plant model. Since a global system model is unavailable, this is identified on every ILC trial by running an identification test. This adds many time-consuming identification tests, making it infeasible for clinical deployment. To solve this problem, an approach is developed that can deliver high accuracy with minimal identification overhead. It introduces a parameterised plant model that is updated in parallel with the ILC using all available data, and then applied to replace identification tests. Rigorous conditions are derived to ensure convergence is preserved while minimising identification time. Numerical results show that four references can be tracked using only 10.8% of the experimental tests required by standard ILC algorithms. The approach is then applied experimentally to six unimpaired subjects using a realistic rehabilitation scenario. In particular, a novel stereo camera system is used to measure hand joint angles in a manner that can transfer to home use. Results show mean joint angle tracking accuracy within 5°, while requiring only between 25% and 64.9% of the experimental tests of standard ILC.</p

Neural network based ILC with application to FES electrode arrays

Functional electrical stimulation (FES) is a technology that can help paralysed or impaired subjects regain their lost movement after stroke. A large number of FES elements can be combined to form FES arrays which are capable of activating the multiple muscles needed to perform functional arm movement. However, the control of FES arrays is challenging since high precision is required but there is little time available in a clinical or home setting to identify a model. To addressthis problem, an approach is developed that can deliver high accuracy with minimal identification overhead. It is based on iterative learning control (ILC), a technique that exploits the repeated nature of rehabilitation training. The method uses a parameterised model form that adapts to new data and replaces identification tests while maintaining accuracy. Conditions are derived to ensure convergence is preserved while minimising identification time. Numerical results show that four references can be tracked using only 10.8% of the experimental tests required by conventional ILC algorithms. The approach is then applied experimentally to four unimpaired subjects using a realistic rehabilitation scenario, with results showing mean tracking accuracy within 5, while requiring only between 25% and 64:9% of the experimental tests of conventional ILC