LEO Satellite-Enabled Grant-Free Random Access with MIMO-OTFS

This paper investigates joint channel estimation and device activity detection in the LEO satellite-enabled grant-free random access systems with large differential delay and Doppler shift. In addition, the multiple-input multiple-output (MIMO) with orthogonal time-frequency space modulation (OTFS) is utilized to combat the dynamics of the terrestrial-satellite link. To simplify the computation process, we estimate the channel tensor in parallel along the delay dimension. Then, the deep learning and expectation-maximization approach are integrated into the generalized approximate message passing with cross-correlation--based Gaussian prior to capture the channel sparsity in the delay-Doppler-angle domain and learn the hyperparameters. Finally, active devices are detected by computing energy of the estimated channel. Simulation results demonstrate that the proposed algorithms outperform conventional methods.Comment: This paper has been accepted for presentation at the IEEE GLOBECOM 2022. arXiv admin note: text overlap with arXiv:2202.1305

Current ripple reduction for finite control set model predictive control strategy of grid-tied inverter with reference current compensation

In the finite control set model predictive control (FCS-MPC) strategy of the grid-tied inverter, the current ripple (CR) affects the selection of optimal voltage vectors, which leads to the increase of output current ripples. In order to solve this problem, this paper proposes a CR reduction method based on reference current compensation (RCC) for the FCS-MPC strategy of grid-tied inverters. Firstly, the influence of the CR on optimal voltage vector selection is analyzed. The conventional CR prediction method is improved, which uses inverter output voltage and grid voltage to calculate current ripples based on the space state equation. It makes up for the shortcomings that the conventional CR prediction method cannot predict in some switching states. The improved CR method is more suitable for the FCS-MPC strategy. In addition, the differences between the two cost functions are compared through visual analysis. It is found that the sensitivity of the square cost function to small errors is better than that of the absolute value function. Finally, the predicted CR is used to compensate the reference current. The compensated reference current is substituted into the square cost function to reduce the CR. The experimental results show that the proposed method reduces the CR by 47.3%. The total harmonic distortion (THD) of output current is reduced from 3.86% to 2.96%

Joint Device Identification, Channel Estimation, and Signal Detection for LEO Satellite-Enabled Random Access

peer reviewedThis paper investigates joint device identification, channel estimation, and signal detection for LEO satellite-enabled grant-free random access, where a multiple-input multipleoutput (MIMO) system with orthogonal time-frequency space modulation (OTFS) is utilized to combat the dynamics of the terrestrial-satellite link (TSL). We divide the receiver structure into three modules: first, a linear module for identifying active devices, which leverages the generalized approximate message passing (GAMP) algorithm to eliminate inter-user interference in the delay-Doppler domain; second, a non-linear module adopting the message passing algorithm to jointly estimate channel and detect transmit signals; the third aided by Markov random field (MRF) aims to explore the three dimensional block sparsity of channel in the delay-Doppler-angle domain. The soft information is exchanged iteratively between these three modules by careful scheduling. Furthermore, the expectation-maximization algorithm is embedded to learn the hyperparameters in prior distributions. Simulation results demonstrate that the proposed scheme outperforms the conventional methods significantly in terms of activity error rate, channel estimation accuracy, and symbol error rate

The Association Between Diabetic Retinopathy and the Prevalence of Age-Related Macular Degeneration—The Kailuan Eye Study

This study aimed to investigate the prevalence of age-related macular degeneration (AMD) in patients with diabetes mellitus (DM) and diabetic retinopathy (DR) and analyze whether DR is a risk factor for AMD. This population-based epidemiological study included 14,440 people from the Kailuan Eye Study in 2016, of whom 1,618 were patients with type 2 DM aged over 50 years, and 409 had DM with DR. We analyzed whether there were differences in the prevalence of AMD between DM with DR and DM without DR, and conducted a hierarchical statistical analysis according to different stages of DR. Using variable regression analysis, we explored whether DR constituted a risk factor for AMD. In the DM population, the prevalence of wet AMD in patients with DM with and without DR was 0. 3 and 0.2%, respectively, with no significant difference (P = 0.607). Meanwhile, the prevalence of dry AMD in patients with DM with and without DR was 20.8 and 16.0%, respectively, with a significant difference. In the subgroup analysis of dry AMD, the prevalence of early, middle, and late dry AMD in DM with DR was 14.4, 5.9, and 0.5%, respectively. In DM without DR, the prevalence of early, middle, and late dry AMD was 10.5, 4.8, and 0.7%, respectively (P = 0.031). In the subgroup analysis of DR staging, statistical analysis could not be performed because of the limited number of patients with PDR. In the variable regression analysis of risk factors for dry AMD, after adjusting for age, sex, body mass index, hypertension, and dyslipidemia, DR constituted the risk factor for dry AMD. In conclusion, DM did not constitute a risk factor for AMD, and the prevalence of wet AMD and dry AMD in patients with DM and DR was higher than that in patients with DM without DR (among which dry AMD was statistically significant). Multivariate regression analysis confirmed that DR is an independent risk factor for dry AMD. Reasonable control of DM and slowing down the occurrence and development of DR may effectively reduce the prevalence of AMD in patients with DM

Optimization of Fuel Consumption and Emissions for Auxiliary Power Unit Based on Multi-Objective Optimization Model

Auxiliary power units (APUs) are widely used for electric power generation in various types of electric vehicles, improvements in fuel economy and emissions of these vehicles directly depend on the operating point of the APUs. In order to balance the conflicting goals of fuel consumption and emissions reduction in the process of operating point choice, the APU operating point optimization problem is formulated as a constrained multi-objective optimization problem (CMOP) firstly. The four competing objectives of this CMOP are fuel-electricity conversion cost, hydrocarbon (HC) emissions, carbon monoxide (CO) emissions and nitric oxide (NO x ) emissions. Then, the multi-objective particle swarm optimization (MOPSO) algorithm and weighted metric decision making method are employed to solve the APU operating point multi-objective optimization model. Finally, bench experiments under New European driving cycle (NEDC), Federal test procedure (FTP) and high way fuel economy test (HWFET) driving cycles show that, compared with the results of the traditional fuel consumption single-objective optimization approach, the proposed multi-objective optimization approach shows significant improvements in emissions performance, at the expense of a slight drop in fuel efficiency

Electric Vehicle Charging and DischargingCoordination on Distribution Network UsingMulti-Objective Particle Swarm Optimization andFuzzy Decision Making

18 Halama

Analysis and application research of new type online monitoring technologies for substation equipment

At present, a variety of online monitoring technologies for substation equipment including winding temperature by optical fiber, however, the accuracy and stability show great differences among them. This paper focuses on the advancement, feasibility and economics of various new technologies, combined with the construction and operation experience of wisdom substations, in order to select mature, reliable, and effective new online monitoring technologies that reduce the amount of equipment operation and maintenance from a number of new technologies. In the end, the article gives suggestions on the application of various new online monitoring technologies