Optimal H₂ Input Load Disturbance Rejection Controller Design for Nonminimum Phase Systems Based on Algebraic Theory

This work discusses the issue of input load disturbance rejection (ILDR) for open-loop nonminimum phase (NMP) plants. A novel analytical solution is proposed on the basis of the internal model control (IMC) theory. Differing from other methods, the proposed design is conducted to optimize the ILDR criterion. Optimization of the input disturbance response of the controller is performed under the constraints on robustness. When the input load disturbance is taken into consideration, the proposed controller performs better disturbance rejection capability in terms of 2-norm than most explored IMC-based controllers derived from the conventional criterion. Typical NMP processes are systematically analyzed. Numerical examples are given to illustrate the effectiveness of the novel solution. The quantitative performance specifications and robust stability can be obtained by monotonously tuning the single parameter. Results show that the proposed solution makes the proposed method yield the expected dynamic responses

Robust vehicle detection in high-resolution aerial images with imbalanced data

Vehicle detection in images from unmanned aerial vehicles (UAVs) plays an important role in traffic surveillance and urban planning due to the popularity of UAVs. However, the class imbalance problem is an important factor that restricts the performance of vehicle detectors. There are two types of class imbalance in UAV images, i.e., foreground-background imbalance and foreground-foreground imbalance. For anchor-based single stage detector, as many ground truths cannot be assigned to corresponding anchors because of low intersection over union (IoU), it makes the foreground-background imbalance problem more severe. Therefore, we propose a novel Bag-based Single-Stage Detector which treats each position on the feature map as a bag. A simple and adaptive definition of bags is proposed along with the positive sample definition method which is utilized to ensure more ground truths can be assigned to proper bags. In addition, we utilize online hard example mining (OHEM) method to control the proportion of positive and negative samples during the training process. To address the foreground-foreground imbalance, we propose a novel data augmentation algorithm which allows us to create appropriate visual context for under-represented class. Extensive experiments demonstrate the superiority of the proposed algorithm, compared with other state-of-the-art (SOTA) solutions

ANOVA analysis of nodal betweenness ().

<p>Significance was indicated by *(p<0.05) and **(p<0.001).</p

Global network properties in Middle sub-stage.

<p>Clustering coefficient (<b>A</b>) and characteristic path length (<b>B</b>) with respect to ANGLE were shown. GROUP effect on clustering coefficient (<b>C</b>) and characteristic path length (<b>D</b>) were illustrated. Clustering coefficient in aspect to HAND factor was in (<b>E</b>). Significant difference was indicated by *(p<0.05).</p

Nodal betweenness in three sub-stages.

<p>Nodal betweenness of two groups with respect to angle in Beginning (<b>A</b>), Middle (<b>B</b>), and End sub-stages (<b>C</b>) were illustrated respectively. Gray wafers indicated the channels where stroke patients have larger betweenness than control subjects, while black wafers indicated the channels where patients have lower betweenness than control subjects by t-test (p<0.05). However, the t-test p-values of these channels are larger than the significance threshold estimated by FDR (q<0.05) for multiple comparisons correction.</p

Subjects demography.

<p>M = Male; F = Female; y = years; NIHSS = National Institutes of Health Stroke Scale.</p

Nodal clustering coefficients in three sub-stages.

<p>Nodal clustering coefficients of two groups with respect to angle in Beginning (<b>A</b>), Middle (<b>B</b>), and End sub-stages (<b>C</b>) were illustrated respectively. Gray wafers indicated the channels where stroke patients have larger clustering coefficient than control subjects by t-test (p<0.05). However, the t-test p-values of these channels are greater than the significance threshold estimated by FDR (q<0.05) for multiple comparisons correction.</p

ANOVA analysis of phase synchronization index (PSI).

<p>Significance was indicated by *(p<0.05) and **(p<0.001).</p

ANOVA analysis of nodal clustering coefficient ().

<p>Significance was indicated by *(p<0.05) and **(p<0.001).</p

Global network parameters with respect to different thresholds.

<p>Global clustering coefficients (<b>A</b>), characteristic path lengths (<b>B</b>) and small-worldness indexes (<b>C</b>) of brain networks during whole MRT (0–1200 ms) with respect to different thresholds were illustrated, respectively. Symbols *indicate the cases of significance difference after multiple comparisons correction by FDR (i.e., with p-values less than the significance threshold estimated by FDR, q<0.05).</p