This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes. 相似文献
Semiconductors - The performance of InGaN/GaN multiple quantum well (MQW) solar cells with five different Si-doping concentrations, namely 0, 4 × 1017 cm–3, 1 × 1018 cm–3, 3... 相似文献
Breast cancer is one of the most common female malignancies, as well as the second leading cause of mortality for women. Early detection and treatment can dramatically decrease the mortality rate. Recently, automated breast volume scanner (ABVS) has become one of the most frequently used diagnose methods for breast tumor screening because of its operator-independent and reproducible advantages. However, it is a challenging job to obtain the tumors’ accurate locations and shapes by reviewing hundreds of ABVS slices. In this paper, a novel computer-aided detection (CADe) system is developed to reduce clinicians’ reading time and improve the efficiency. The CADe system mainly contains three parts: tumor candidate acquisition, false-positive reduction and tumor segmentation. Firstly, a local phase-based approach is built to obtain breast tumor candidates for further recognition. Subsequently, a convolutional neural network (CNN) is applied to reduce false positives (FPs). The introduction of CNN can help to avoid complicated feature extraction as well as elevate the accuracy and efficiency. Finally, superpixel-based segmentation is used to outline the breast tumor. Here, superpixel-based local binary pattern (SLBP) is proposed to assist the segmentation, which improves the performance. The methods were evaluated on a clinical ABVS dataset whose abnormal cases were manually labeled by an experienced radiologist. The experiment results were mainly composed of two parts. At the FP reduction stage, the proposed CNN achieved 100% and 78.12% sensitivity with FPs/case of 2.16 and 0. At the segmentation stage, our SLBP obtained 82.34% true positive, 15.79% false positive and 83.59% Dice similarity. In summary, the proposed CADe system demonstrated promising potential to detect and outline breast tumors in ABVS images.
Cervical cancer (CESC) is one of the most common cancers and affects the female genital tract. Consistent HPV
infection status has been determined to be a vital cause of tumorigenesis. HPV infection may induce changes to the
immune system and limit the host’s immune response. Immunotherapy is therefore essential to improving the overall
survival of both locally advanced and recurrent CESC patients. Using 304 relevant samples from TCGA, we assessed
immune cell function in CESC patients to better understand the status of both tumor micro-environment cells and
immune cells in CESC. Functional enrichment analysis, pathway enrichment analysis, and PPI network construction
were performed to explore the differentially expressed genes (DEGs). The analysis identified 425 DEGs, which
included 295 up-regulated genes and 130 down-regulated genes. We established that upregulation of CCL5 was
correlated with significantly better survival, meaning that CCL5 expression could serve as a novel prognostic
biomarker for CESC patients. We further focused on CCL5 as a hub gene in CESC, as it had significant correlations
with increased numbers of several types of immune cells. Cell-type fractions of M1 macrophages were significantly
higher in the high-immune-scores group, which was associated with better overall survival. Finally, we concluded that
CCL5 is a promising prognostic biomarker for CESC, as well as a novel chemotherapeutic target. 相似文献