Signal-to-noise ratio aware minimaxity and its asymptotic expansion

Since its development, the minimax framework has been one of the corner stones of theoretical statistics, and has contributed to the popularity of many well-known estimators, such as the regularized M-estimators for high-dimensional problems. In this thesis, we will first show through the example of sparse Gaussian sequence model, that the theoretical results under the classical minimax framework are insufficient for explaining empirical observations. In particular, both hard and soft thresholding estimators are (asymptotically) minimax, however, in practice they often exhibit sub-optimal performances at various signal-to-noise ratio (SNR) levels. To alleviate the descrepancy, we first demonstrate that this issue can be resolved if the signal-to-noise ratio is taken into account in the construction of the parameter space. We call the resulting minimax framework the signal-to-noise ratio aware minimaxity. Then, we showcase how one can use higher-order asymptotics to obtain accurate approximations of the SNR-aware minimax risk and discover minimax estimators. Theoretical findings obtained from this refined minimax framework provide new insights and practical guidance for the estimation of sparse signals. In a broader context, we investigated the same problem for sparse linear regression. We assume the random design and allow the feature matrix to be high dimensional as ∈ R^{ x } and ⪢ . This adds an extra layer of challenge to the estimation of coefficients. Previous studies have largely relied on results expressed in rate-minimaxity, where estimators are compared based on minimax risk with order-wise accuracy, without specifying the precise constant in the approximation. This lack of precision contributes to the notable gap between theoretical conclusions of the asymptotic minimax estimators and empirical findings of the sub-optimality. This thesis addresses this gap by initially refining the classical minimax result, providing a characterization of the constant in the first-order approximation. Subsequently, by following the framework of SNR-aware minimaxity we introduced before, we derived improved approximations of minimax risks under different SNR levels. Notably, these refined results demonstrated better alignment with empirical findings compared to classical minimax outcomes. As showcased in the thesis, our enhanced SNR-aware minimax framework not only offers a more accurate depiction of sparse estimation but also unveils the crucial role of SNR in the problem. This insight emerges as a pivotal factor in assessing the optimality of estimators

Improved Inner Approximation for Aggregating Power Flexibility in Active Distribution Networks and its Applications

Concise and reliable modeling for aggregating power flexibility of distributed energy resources in active distribution networks (ADNs) is a crucial technique for coordinating transmission and distribution networks. Our recent research has successfully derived an explicit expression for the exact aggregation model (EAM) of power flexibility at the substation level under linearized distribution network constraints. The EAM, however, is impractical for decision-making purposes due to its exponential complexity. In this paper, we propose an inner approximation method for aggregating flexibility in ADNs that utilizes the properties of the EAM to improve performance. Specifically, the geometric prototype of the inner approximation model is defined according to a subset of the coefficient vector set of the EAM, which enhances the accuracy. On the other hand, the computation efficiency of the inner approximation is also significantly improved by exploiting the regularity of coefficient vectors in the EAM in the parameter calculation process. The inner approximated flexibility model of ADNs is further incorporated into the security-constrained unit commitment problem as an application. Numerical simulations verify the effectiveness of the proposed method.Comment: 10 page

Particle Swarm Algorithm to Optimize LSTM Short-Term Load Forecasting

Accurate load forecasting is of great significance for national and grid planning and management. In order to improve the accuracy of short-term load forecasting, an LSTM prediction model based on particle swarm optimization (PSO)algorithm is proposed. LSTM has the characteristics of avoiding gradient disappearance and gradient explosion, but there is a problem that parameters are difficult to select. Therefore, particle swarm optimization algorithm is used to help it select parameters. The experimental results show that the optimized LSTM has higher prediction accuracy

Flexible torque control for wind turbines considering frequency response under wind speed crossing region

The operational range of a wind turbine is typically divided into two regions based on wind speed: below and above the rated wind speed. The turbine switches between these two regions depending on the prevailing wind speed; however, during the transition, the generator may undergo transient shocks in torque, which can negatively impact both the mechanical load of the turbine and the reliability of the power system. This article presents a flexible torque control method for wind turbines, specifically designed to handle the transition between wind speed regions when the turbine is participating in frequency regulation. First, the anomalies in generator torque caused by traditional torque control methods during frequency response scenarios are analyzed. Next, two methods—dynamic deloading and flexible torque control—are developed to address these issues. The developed methods set transition regions based on generator speed, which helps to reduce the impact of transient changes in generator torque. Importantly, the addition of transition regions does not require additional feedback, making the controller easy to implement. The response characteristics of the proposed methods are then analyzed under different deloading factors and wind speeds using model linearization. Simulation studies are presented to verify the effectiveness of the proposed methods. Overall, this study demonstrates the potential value of flexible torque control methods for wind turbines, which can help to mitigate the negative impact of torque shocks and improve the reliability and efficiency of wind power systems

Visual Atlas Analysis of Acceleration Sensor Research Literature Based on CiteSpace Bibliometrics

This paper uses CiteSpace information visualization software to visualize the acceleration sensor research literature based on more than 1100 literatures in the field of acceleration sensor research and application from 2010 to 2018. From the point of view of bibliometrics, the paper analyzes the visualization map of hot spot distribution, such as the country, discipline, research institution and funded status, the co-citation literature and the research frontier. Moreover, this paper compares and analyzes literature information on research and application fields of acceleration sensors at home and abroad in recent years. Information is used to evaluate the research progress and development trend of acceleration sensors, in order to provide literature reference for the relevant personnel engaged in the research of acceleration sensors

Characterization of four vaccine-related polioviruses including two intertypic type 3/type 2 recombinants associated with aseptic encephalitis

Temperature sensitivity of 4 poliovirus type 3 isolates. (DOC 31 kb

Immunological characterization of stroke-heart syndrome and identification of inflammatory therapeutic targets

Acute cardiac dysfunction caused by stroke-heart syndrome (SHS) is the second leading cause of stroke-related death. The inflammatory response plays a significant role in the pathophysiological process of cardiac damage. However, the mechanisms underlying the brain–heart interaction are poorly understood. Therefore, we aimed to analysis the immunological characterization and identify inflammation therapeutic targets of SHS. We analyzed gene expression data of heart tissue 24 hours after induction of ischemia stoke by MCAO or sham surgery in a publicly available dataset (GSE102558) from Gene Expression Omnibus (GEO). Bioinformatics analysis revealed 138 differentially expressed genes (DEGs) in myocardium of MCAO-treated compared with sham-treated mice, among which, immune and inflammatory pathways were enriched. Analysis of the immune cells infiltration showed that the natural killer cell populations were significantly different between the two groups. We identified five DIREGs, Aplnr, Ccrl2, Cdkn1a, Irak2, and Serpine1 and found that their expression correlated with specific populations of infiltrating immune cells in the cardiac tissue. RT–qPCR and Western blot methods confirmed significant changes in the expression levels of Aplnr, Cdkn1a, Irak2, and Serpine1 after MCAO, which may serve as therapeutic targets to prevent cardiovascular complications after stroke

Effect of acupuncture in the acute phase of intracerebral hemorrhage on the prognosis and serum BDNF: a randomized controlled trial

BackgroundIntracerebral hemorrhage (ICH) is a common cerebrovascular disease, with a high rate of disability. In the literature on Chinese traditional medicine, there is increasing evidence that acupuncture can help hematoma absorption and improve neurological deficits after cerebral hemorrhage. Brain-derived neurotrophic factor (BDNF), one of the most studied neurotrophic factors, is involved in a variety of neurological functions and plays an important role in brain injury recovery. We investigated the effect of acupuncture intervention in the acute phase of ICH on the prognosis and serum BDNF levels of several patient groups.ObjectiveTo investigate the influence of acupuncture on the prognosis and brain-derived neurotrophic factor (BDNF) levels in patients in the acute phase of ICH.MethodsFrom November 2021 to May 2022, 109 subjects were consecutively enrolled, including patients with ICH, who were randomized into the acupuncture group (AG) and sham acupuncture group (SAG), and a control group (CG). The CG received the same acupuncture intervention as the AG, and the SAG received sham acupuncture, with 14 interventions in each group. The level of consciousness of patients with ICH was assessed and serum BDNF levels were measured in all three groups before the intervention and at 3 weeks after onset, and the level of consciousness and outcomes were assessed at 12 weeks after onset.ResultsAfter the intervention, the level of consciousness of the AG improved significantly (P < 0.05); the BDNF level of only the AG increased significantly (P < 0.05); the changes in Glasgow Coma Scale (GCS) score and BDNF level were significantly greater in the AG than in the SAG (P < 0.05), especially for locomotion (P < 0.05). At 12 weeks post-onset, the AG showed better outcomes and recovery of consciousness than the SAG (P < 0.05)