Active fault tolerant control of an electro-hydraulic driven elevator based on robust adaptive observers

Faults are minor malfunctions that deteriorate the performance of a system. In a safety critical situation such as the control of an airplane, compounding faults may cascade into a catastrophic event if not properly compensated. Active Fault Tolerant Control (AFTC) addresses the fault accommodation problem - the reliability and robustness of the system in faults - beyond the conventional stability and performance requirements for a normally operating plant. This thesis studies the AFTC of an electro-hydraulic driven elevator, which serves as a primary control surface of an airplane. The proposed AFTC system consists of three components: (1) A Fault Detection and Estimation (FOE) component is designed based on two robust adaptive observers. (1). Adaptive Unknown Input Observer: a disturbance decoupled observer utilizing the geometry property and measurement redundancy of the system; (2). H x /H _ adaptive observer: an optimization based observer to maximize the system's response to faults and minimize that to disturbances. The H x /H _ adaptive observer is constructed with the technique of Unitary System, which is defined as a linear system whose singular values of transfer matrix are equal. (2) A fuzzy Proportional-Integral (PI) controller is designed based on the fuzzy Tagaki-Sugeno (TS) model of a nonlinear system, which consists of different linear models at different operating points. (3) The reconfiguration is carried out based on the fault information available from FDE. To reduce the time needed for the online computation, multiple controllers are designed offline for different faults scenarios. A new controller is constructed online as a fuzzy combination of these controllers to meet the post-fault stability and performance requirements. Simulation results show that, with the proposed AFTC, occurring faults are detected promptly and estimated accurately with the FDE component. The performance of the post-fault elevator is quickly restored after the reconfiguratio

The study of friction variation with temperature in a harmonic drive system : modeling and control

This thesis studies the effects of temperature in friction modeling for a harmonic drive. A mathematic model of how friction varies with temperature change is built based on the exponential friction model. The mathematic model is then used in building the nonlinear dynamic model of the harmonic drive. A Cascaded Fuzzy Model of friction with temperature is proposed so that the friction experienced in the harmonic drive can be modeled as a fuzzy combination of linear models at different operating temperature and velocity. The fuzzy model is then integrated to the dynamic model of the harmonic drive to build the fuzzy TS model which is used in the controller design. The parameters in both mathematic model and the fuzzy model are estimated. The estimation is formed into an optimization problem of minimizing the quadratic cost function of estimation errors. Since the cost function is highly nonlinear, a new algorithm of Evolutionary Parallel Gradient Search is applied to help the search escaping from local minima. The optimal controller for fuzzy TS system is proposed in the form of LMI based optimization with new constraints and applied to the harmonic drive. In simulation, the closed loop system shows the capability of accurately tracking large range of reference signals even when the temperature is changing, either continuously or drasticall

Climate Change Drives the Adaptive Distribution and Habitat Fragmentation of Betula albosinensis Forests in China

Betula albosinensis serves as an important constructive and afforestation tree species in mountainous areas. Its suitable habitat and habitat quality are highly vulnerable to the climate. However, few studies have centered on the shrinkage, expansion, and habitat fragmentation of B. albosinensis forests under climate change. In this study, the Random Forest model was employed to predict current and future trends of shrinking and expanding of B. albosinensis, while a composite landscape index was utilized to evaluate the habitat fragmentation in the highly suitable habitats of B. albosinensis. The results indicated that suitable habitats for B. albosinensis were primarily concentrated in the vicinities of the Qinling, Qilian, and Hengduan Mountains, situated in western China. The most influential factor affecting the distribution of B. albosinensis was temperature seasonality (Bio4). In future scenarios, the center of distribution of B. albosinensis was projected to shift towards the west and higher altitudes. The total suitable habitats of B. albosinensis were anticipated to expand under the scenarios of SSP370 and SSP585 in the 2090s, while they were expected to contract under the remaining scenarios. Although these results indicated that the suitable areas of habitat for B. albosinensis were relatively intact on the whole, fragmentation increased with climate change, with the highest degree of fragmentation observed under the SSP585 scenario in the 2090s. The findings of this study provide a foundation for the protection of montane vegetation, the maintenance of montane biodiversity, and the evaluation of species’ habitat fragmentation.</p

Ultralow Loss Coupling Tuning of Photonic Accelerators

By leveraging the high propagation speed and inherent parallelism of light, hardware accelerators based on photonic integrated circuits enable high‐speed, low‐power computing, positioning them as promising solutions to meet the rapidly increasing computational demands driven by advancements in artificial intelligence (AI). Within photonic accelerators directional couplers are crucial components for splitting and combining light, facilitating parallel computation and addition operations. However, fabrication imperfections frequently cause deviations in the coupling ratio from its intended value, significantly impairing accelerator performance. This study demonstrates a scalable and nonvolatile approach to flexibly adjust the coupling ratio of fabricated directional couplers by strategically placing polymer patches around their waveguides. This method introduces exceptionally low insertion loss of ≈0.01 dB and can effectively adapt directional couplers with varying initial coupling ratios. This method is applied to a photonic crossbar array, substantially reducing the fabrication‐induced power discrepancy among output ports from 389% to just 8%. This approach presents an innovative strategy for efficiently compensating fabrication errors in integrated photonic circuits

Preparation of graphene oxide–stabilized Pickering emulsion adjuvant for Pgp3 recombinant vaccine and enhanced immunoprotection against Chlamydia Trachomatis infection

BackgroundTraditional emulsion adjuvants are limited in clinical application because of their surfactant dependence. Graphene oxide (GO) has unique amphiphilic properties and therefore has potential to be used as a surfactant substitute to stabilize Pickering emulsions.MethodsIn this study, GO–stabilized Pickering emulsion (GPE) was prepared and used as an adjuvant to facilitate an enhanced immune response to the Chlamydia trachomatis (Ct) Pgp3 recombinant vaccine. Firstly, GPE was prepared by optimizing the sonication conditions, pH, salinity, GO concentration, and water/oil ratio. GPE with small-size droplets was characterized and chosen as the candidate. Subsequently, controlled-release antigen delivery by GPE was explored. Cellular uptake behaviors, M1 polarization, and cytokine stimulation by GPE + Pgp3 was considered in terms of the production of macrophages. Finally, GPE’s adjuvant effect was evaluated by vaccination with Pgp3 recombinant in BALB/c mouse models.ResultsGPE with the smallest droplet sizes was prepared by sonication under 163 W for 2 min at 1 mg/mL GO in natural salinity with a pH of 2 when the water/oil ratio was 10:1 (w/w). The optimized average GPE droplet size was 1.8 μm and the zeta potential was –25.0 ± 1.3 mv. GPE delivered antigens by adsorption onto the droplet surface, demonstrating the controlled release of antigens both in vitro and in vivo. In addition, GPE promoted antigen uptake, which stimulated proinflammatory tumor necrosis factor alpha (TNF-α), enhancing the M1 polarization of macrophages in vitro. Macrophage recruitment was also significantly promoted by GPE at the injection site. In the GPE + Pgp3 treatment group, higher levels of immunoglobin (IgG), immunoglobin G1 (IgG1), immunoglobin G2a (IgG2a) sera, and immunoglobin A (IgA) were detected in vaginal fluid, and higher levels of IFN-γ and IL-2 secretion were stimulated, than in the Pgp3 group, showing a significant type 1 T helper (Th1)-type cellular immune response. Chlamydia muridarum challenging showed that GPE enhanced Pgp3’s immunoprotection through its advanced clearance of bacterial burden and alleviation of chronic pathological damage in the genital tract.ConclusionThis study enabled the rational design of small-size GPE, shedding light on antigen adsorption and control release, macrophage uptake, polarization and recruitment, which enhanced augmented humoral and cellular immunity and ameliorated chlamydial-induced tissue damage in the genital tract

Natural products and dietary interventions on liver enzymes: an umbrella review and evidence map

BackgroundThe association between natural products and dietary interventions on liver enzymes is unclear; therefore, this study aimed to examine their effects on liver enzymes in adults.MethodsPubMed, Embase, and Cochrane Library of Systematic Reviews databases were searched from inception until March 2023. The Assessment of Multiple Systematic Reviews-2 (AMSTAR-2) and Grading of Recommendations Assessment, Development, and Evaluation (GRADE) systems were used to assess the methodological and evidence quality, and the therapeutic effects were summarized in a narrative form.ResultsA total of 40 meta-analyses on natural products (n = 25), dietary supplements (n = 10), and dietary patterns (n = 5) were evaluated, and results were presented in a narrative form. The overall methodological quality of the included studies was relatively poor. The results indicated that positive effects were observed for nigella sativa, garlic, artichoke, curcumin, silymarin, vitamin E, vitamin D, L-carnitine, propolis, and polyunsaturated fatty acids on certain liver enzymes. The dietary patterns, including high-protein, Mediterranean, and calorie-restriction diets and evening snacks, may reduce liver enzymes; however, other supplements and herbs did not reduce liver enzyme levels or have minimal effects. The evidence quality was generally weak given the risk of bias, heterogeneity, and imprecision.ConclusionThis umbrella review suggests that natural products and dietary interventions have beneficial therapeutic effects on liver enzymes levels. Further clinical trials are necessary to establish the effectiveness of supplements that reduce liver enzymes

Optimization of Flash Extraction Process and Antioxidant Activity of American Ginseng Flower Polysaccharides

Objective: To study the optimal process conditions of flash extraction and antioxidant activity of American ginseng flower polysaccharides (AGFPs). Methods: American ginseng flower (AGF) as the raw material, the effects of extraction voltage, liquid-material ratio, and extraction time on the yield of AGFPs were explored. The flash extraction process of AGFPs was improved by response surface methodology. The scavenging effect on DPPH and hydroxy free radicals and total reduction capacity of AGFPs were determined to assess the antioxidant activity of AGFPs. Results: The optimal extraction conditions were determined as 130 V of extraction voltage, 30:1 mL/g of liquid-material ratio, and 100 s of extraction time, resulting in an AGFPs yield of 11.12%±0.23%, which agreed with the model prediction. The AGFPs exhibited significant scavenging effects on DPPH and hydroxyl radicals, with IC50 values of 1.34 mg/mL and 1.42 mg/mL, respectively, and had a certain reducing power. Conclusion: These results suggested that flash extraction was an efficient and rapid method for obtaining AGFPs from AGF, and that AGFPs had promising antioxidant potential for further applications. This study can provide a theoretical basis for the development and application of AGF

Valley: Video Assistant with Large Language model Enhanced abilitY

Large language models (LLMs), with their remarkable conversational capabilities, have demonstrated impressive performance across various applications and have emerged as formidable AI assistants. In view of this, it raises an intuitive question: Can we harness the power of LLMs to build multimodal AI assistants for visual applications? Recently, several multi-modal models have been developed for this purpose. They typically pre-train an adaptation module to align the semantics of the vision encoder and language model, followed by fine-tuning on instruction-following data. However, despite the success of this pipeline in image and language understanding, its effectiveness in joint video and language understanding has not been widely explored. In this paper, we aim to develop a novel multi-modal foundation model capable of comprehending video, image, and language within a general framework. To achieve this goal, we introduce Valley, a Video Assistant with Large Language model Enhanced abilitY. The Valley consists of a LLM, a temporal modeling module, a visual encoder, and a simple projection module designed to bridge visual and textual modes. To empower Valley with video comprehension and instruction-following capabilities, we construct a video instruction dataset and adopt a two-stage tuning procedure to train it. Specifically, we employ ChatGPT to facilitate the construction of task-oriented conversation data encompassing various tasks, including multi-shot captions, long video descriptions, action recognition, causal relationship inference, etc. Subsequently, we adopt a pre-training-then-instructions-tuned pipeline to align visual and textual modalities and improve the instruction-following capability of Valley. Qualitative experiments demonstrate that Valley has the potential to function as a highly effective video assistant that can make complex video understanding scenarios easy

Ultrasound Nodule Segmentation Using Asymmetric Learning with Simple Clinical Annotation

Recent advances in deep learning have greatly facilitated the automated segmentation of ultrasound images, which is essential for nodule morphological analysis. Nevertheless, most existing methods depend on extensive and precise annotations by domain experts, which are labor-intensive and time-consuming. In this study, we suggest using simple aspect ratio annotations directly from ultrasound clinical diagnoses for automated nodule segmentation. Especially, an asymmetric learning framework is developed by extending the aspect ratio annotations with two types of pseudo labels, i.e., conservative labels and radical labels, to train two asymmetric segmentation networks simultaneously. Subsequently, a conservative-radical-balance strategy (CRBS) strategy is proposed to complementally combine radical and conservative labels. An inconsistency-aware dynamically mixed pseudo-labels supervision (IDMPS) module is introduced to address the challenges of over-segmentation and under-segmentation caused by the two types of labels. To further leverage the spatial prior knowledge provided by clinical annotations, we also present a novel loss function namely the clinical anatomy prior loss. Extensive experiments on two clinically collected ultrasound datasets (thyroid and breast) demonstrate the superior performance of our proposed method, which can achieve comparable and even better performance than fully supervised methods using ground truth annotations.Comment: Accepted by TCSV