Fault Detection for Wireless Network Control Systems with Stochastic Uncertainties and Time Delays

The fault detection problem is investigated for a class of wireless network control systems which has stochastic uncertainties in the state-space matrices, combined with time delays and nonlinear disturbance. First, the system error observer is proposed. Then, by constructing proper Lyapunov-Krasovskii functional, we acquire sufficient conditions to guarantee the stability of the fault detection observer for the discrete system, and observer gain is also derived by solving linear matrix inequalities. Finally, a simulation example shows that when a fault happens, the observer residual rises rapidly and fault can be quickly detected, which demonstrates the effectiveness of the proposed method

Study on the relationship between expression patterns of cocaine-and amphetamine regulated transcript and hormones secretion in porcine ovarian follicles

Background: Cocaine-and amphetamine regulated transcript (CART) is an endogenous neuropeptide, which is widespread in animals, plays a key role in regulation of follicular atresia in cattle and sheep. Among animal ovaries, CART mRNA was firstly found in the cattle ovaries. CART was localized in the antral follicles oocytes, granulosa and cumulus cells by immunohistochemistry and in situ hybridization. Further research found that secretion of E2 was inhibited in granulosa cells with a certain dose of CART, the effect depends on the stage of cell differentiation, sug- gesting that CART could play a crucial role in regulating follicle atresia. The objective of this study was to character- ize the CART expression model and hormones secretion in vivo and vitro in pig follicle granulosa cells, preliminarily studied whether CART have an effect on granulosa cells proliferation and hormones secretion in multiparous animals such as pigs. Methods: The expression levels of CART mRNA in granulosa cells of different follicles were analyzed using qRT-PCR technology. Immunohistochemistry technology was used to localize CART peptide. Granulosa cells were cultured in medium supplemented with different concentrations of CART and FSH for 168 h using Long-term culture system, and observed using a microscope. The concentration of Estradiol (E2) and progesterone (P) in follicular fluids of different test groups were detected by enzyme linked immunosorbent assay (ELISA). Results: Results showed that expression level of CART mRNA was highest in medium follicles, and significantly higher than that in large and small follicles (P \u3c 0.05). Immunohistochemical results showed that CART were expressed both in granulosa cells and theca cells of large follicles, while CART were detected only in theca cells of medium and small follicles. After the granulosa cells were cultured for 168 h, and found that concentrations of E2 increase with concen- trations of follicle-stimulating hormone (FSH) increase when the CART concentration was 0 μM. And the concentra- tion of FSH reached 25 ng/mL, the concentration of E2 is greatest. It shows that the production of E2 needs induction of FSH in granulosa cells of pig ovarian follicles. With the increasing of CART concentrations (0.01, 0.1, 1 μM), E2 con- centration has a declining trend, when the FSH concentrations were 25 and 50 ng/mL in the medium, respectively. Conclusions: These results suggested that CART plays a role to inhibit granulosa cells proliferation and E2 production, which induced by FSH in porcine ovarian follicular granulosa cells in vitro, but the inhibition effect is not significant. So we hypothesis CART maybe not a main local negative regulatory factor during porcine follicular development, which is different from the single fetal animals

Expression of cocaine- and amphetamine-regulated transcript (CART) in hen ovary

Cocaine- and amphetamine-regulated transcript (CART), discovered initially by via differential display RT-PCR analysis of brains of rats administered cocaine, is expressed mainly in central nervous system or neuronal origin cells, and is involved in a wide range of behaviors, such as regulation of food intake, energy homeostasis, and reproduction. The hens egg-laying rate mainly depends on the developmental status of follicles, expression of CART have not been identified from hen follicles, the regulatory mechanisms of CART biological activities are still unknown. The objective of this study was to characterize the mRNA expression of CART in hen follicular granulosa cells and determine CART peptide localization and regulatory role during follicular development

Speech2Lip: High-fidelity Speech to Lip Generation by Learning from a Short Video

Synthesizing realistic videos according to a given speech is still an open challenge. Previous works have been plagued by issues such as inaccurate lip shape generation and poor image quality. The key reason is that only motions and appearances on limited facial areas (e.g., lip area) are mainly driven by the input speech. Therefore, directly learning a mapping function from speech to the entire head image is prone to ambiguity, particularly when using a short video for training. We thus propose a decomposition-synthesis-composition framework named Speech to Lip (Speech2Lip) that disentangles speech-sensitive and speech-insensitive motion/appearance to facilitate effective learning from limited training data, resulting in the generation of natural-looking videos. First, given a fixed head pose (i.e., canonical space), we present a speech-driven implicit model for lip image generation which concentrates on learning speech-sensitive motion and appearance. Next, to model the major speech-insensitive motion (i.e., head movement), we introduce a geometry-aware mutual explicit mapping (GAMEM) module that establishes geometric mappings between different head poses. This allows us to paste generated lip images at the canonical space onto head images with arbitrary poses and synthesize talking videos with natural head movements. In addition, a Blend-Net and a contrastive sync loss are introduced to enhance the overall synthesis performance. Quantitative and qualitative results on three benchmarks demonstrate that our model can be trained by a video of just a few minutes in length and achieve state-of-the-art performance in both visual quality and speech-visual synchronization. Code: https://github.com/CVMI-Lab/Speech2Lip

Robust ∞ Filtering for a Class of Complex Networks with Stochastic Packet Dropouts and Time Delays

The robust ∞ filtering problem is investigated for a class of complex network systems which has stochastic packet dropouts and time delays, combined with disturbance inputs. The packet dropout phenomenon occurs in a random way and the occurrence probability for each measurement output node is governed by an individual random variable. Besides, the time delay phenomenon is assumed to occur in a nonlinear vector-valued function. We aim to design a filter such that the estimation error converges to zero exponentially in the mean square, while the disturbance rejection attenuation is constrained to a given level by means of the ∞ performance index. By constructing the proper Lyapunov-Krasovskii functional, we acquire sufficient conditions to guarantee the stability of the state detection observer for the discrete systems, and the observer gain is also derived by solving linear matrix inequalities. Finally, an illustrative example is provided to show the usefulness and effectiveness of the proposed design method

PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Network

The reading of arbitrarily-shaped text has received increasing research attention. However, existing text spotters are mostly built on two-stage frameworks or character-based methods, which suffer from either Non-Maximum Suppression (NMS), Region-of-Interest (RoI) operations, or character-level annotations. In this paper, to address the above problems, we propose a novel fully convolutional Point Gathering Network (PGNet) for reading arbitrarily-shaped text in real-time. The PGNet is a single-shot text spotter, where the pixel-level character classification map is learned with proposed PG-CTC loss avoiding the usage of character-level annotations. With a PG-CTC decoder, we gather high-level character classification vectors from two-dimensional space and decode them into text symbols without NMS and RoI operations involved, which guarantees high efficiency. Additionally, reasoning the relations between each character and its neighbors, a graph refinement module (GRM) is proposed to optimize the coarse recognition and improve the end-to-end performance. Experiments prove that the proposed method achieves competitive accuracy, meanwhile significantly improving the running speed. In particular, in Total-Text, it runs at 46.7 FPS, surpassing the previous spotters with a large margin.Comment: 10 pages, 8 figures, AAAI 202