Discrete-time Robust PD Controlled System with DOB/CDOB Compensation for High Speed Autonomous Vehicle Path Following

Autonomous vehicle path following performance is one of significant consideration. This paper presents discrete time design of robust PD controlled system with disturbance observer (DOB) and communication disturbance observer (CDOB) compensation to enhance autonomous vehicle path following performance. Although always implemented on digital devices, DOB and CDOB structure are usually designed in continuous time in the literature and also in our previous work. However, it requires high sampling rate for continuous-time design block diagram to automatically convert to corresponding discrete-time controller using rapid controller prototyping systems. In this paper, direct discrete time design is carried out. Digital PD feedback controller is designed based on the nominal plant using the proposed parameter space approach. Zero order hold method is applied to discretize the nominal plant, DOB and CDOB structure in continuous domain. Discrete time DOB is embedded into the steering to path following error loop for model regulation in the presence of uncertainty in vehicle parameters such as vehicle mass, vehicle speed and road-tire friction coefficient and rejecting external disturbance like crosswind force. On the other hand, time delay from CAN bus based sensor and actuator command interfaces results in degradation of system performance since large negative phase angles are added to the plant frequency response. Discrete time CDOB compensated control system can be used for time delay compensation where the accurate knowledge of delay time value is not necessary. A validated model of our lab Ford Fusion hybrid automated driving research vehicle is used for the simulation analysis while the vehicle is driving at high speed. Simulation results successfully demonstrate the improvement of autonomous vehicle path following performance with the proposed discrete time DOB and CDOB structure

Real time implementation of socially acceptable collision avoidance of a low speed autonomous shuttle using the elastic band method

This paper presents the real time implementation of socially acceptable collision avoidance using the elastic band method for low speed autonomous shuttles operating in high pedestrian density environments. The modeling and validation of the research autonomous vehicle used in the experimental implementation is presented first, followed by the details of the Hardware-In-the-Loop connected and autonomous vehicle simulator used. The socially acceptable collision avoidance algorithm is formulated using the elastic band method as an online, local path modification algorithm. Parameter space based robust feedback plus feedforward steering controller design is used. Model-in-the-loop, Hardware-In-the-Loop and road testing in a proving ground are used to demonstrate the effectiveness of the real time implementation of the elastic band based socially acceptable collision avoidance method of this paper

AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRI

Multi-slice magnetic resonance images of the fetal brain are usually contaminated by severe and arbitrary fetal and maternal motion. Hence, stable and robust motion correction is necessary to reconstruct high-resolution 3D fetal brain volume for clinical diagnosis and quantitative analysis. However, the conventional registration-based correction has a limited capture range and is insufficient for detecting relatively large motions. Here, we present a novel Affinity Fusion-based Framework for Iteratively Random Motion (AFFIRM) correction of the multi-slice fetal brain MRI. It learns the sequential motion from multiple stacks of slices and integrates the features between 2D slices and reconstructed 3D volume using affinity fusion, which resembles the iterations between slice-to-volume registration and volumetric reconstruction in the regular pipeline. The method accurately estimates the motion regardless of brain orientations and outperforms other state-of-the-art learning-based methods on the simulated motion-corrupted data, with a 48.4% reduction of mean absolute error for rotation and 61.3% for displacement. We then incorporated AFFIRM into the multi-resolution slice-to-volume registration and tested it on the real-world fetal MRI scans at different gestation stages. The results indicated that adding AFFIRM to the conventional pipeline improved the success rate of fetal brain super-resolution reconstruction from 77.2% to 91.9%

Triple Feature Disentanglement for One-Stage Adaptive Object Detection

In recent advancements concerning Domain Adaptive Object Detection (DAOD), unsupervised domain adaptation techniques have proven instrumental. These methods enable enhanced detection capabilities within unlabeled target domains by mitigating distribution differences between source and target domains. A subset of DAOD methods employs disentangled learning to segregate Domain-Specific Representations (DSR) and Domain-Invariant Representations (DIR), with ultimate predictions relying on the latter. Current practices in disentanglement, however, often lead to DIR containing residual domain-specific information. To address this, we introduce the Multi-level Disentanglement Module (MDM) that progressively disentangles DIR, enhancing comprehensive disentanglement. Additionally, our proposed Cyclic Disentanglement Module (CDM) facilitates DSR separation. To refine the process further, we employ the Categorical Features Disentanglement Module (CFDM) to isolate DIR and DSR, coupled with category alignment across scales for improved source-target domain alignment. Given its practical suitability, our model is constructed upon the foundational framework of the Single Shot MultiBox Detector (SSD), which is a one-stage object detection approach. Experimental validation highlights the effectiveness of our method, demonstrating its state-of-the-art performance across three benchmark datasets

Temperature Matters More than Fertilization for Straw Decomposition in the Soil of Greenhouse Vegetable Field

As the largest organic carbon input to agroecosystems, crop straw can solve the problem of soil quality degradation in greenhouse vegetable fields, harmonize the balance between soil nutrients and energy, and improve soil quality to maintain the sustainable production of greenhouse vegetables. However, the microbial mechanism of the straw decomposition process under different temperatures and fertilization treatments in greenhouse vegetable soils has not been clarified. Soil samples were used to investigate the biology of straw decomposition in the soil at three incubation temperatures (15, 25, and 35 °C) through a soil incubation experiment (60 d) under different fertilization treatments. Fertilization treatments for this long-term field experiment included chemical fertilizer (CF), substitution of half of the chemical N fertilizer with manure (CM), straw (CS), or combined manure and straw (CMS). The results showed that soil hydrolase activities tended to decrease with increasing temperature during straw decomposition. Compared with the CF, organic substitutions (CM, CMS, and CS) increased soil β-glucosidase, β-cellobiosidase, N-acetyl-glucosaminidase, and β-xylosidase activities during straw decomposition. Soil CO2 emission rates were the highest at each incubation temperature on the first day, rapidly declining at 25 °C and 35 °C and slowly declining at 15 °C. The soil CO2 cumulative emissions tended to increase with increasing temperature under different fertilization treatments. PCA showed that the responses of soil enzyme activities to temperature at 7, 15, and 30 d of straw decomposition were stronger than those of fertilization. In summary, both fertilization treatment and incubation temperature could influence soil CO2 emissions by affecting soil physicochemical properties and enzyme activities during straw decomposition, whereas incubation temperature had a stronger effect on straw decomposition than fertilization, as indicated by PLS-PM and three-way ANOVA. Considering the influence for fertilization on the straw decomposition process at different incubation temperatures, the straw applications (CMS and CS) were more suitable to temperature changes

Transcriptomics and metabolomics provide insight into the anti-browning mechanism of selenium in freshly cut apples

Enzymatic browning has a considerable negative impact on the acceptability and marketability of freshly cut apples. However, the molecular mechanism by which selenium (Se) positively affects freshly cut apples in this regard is not yet clear. In this study, 0.75 kg/plant of Se-enriched organic fertilizer was applied to “Fuji” apple trees during the young fruit stage (M5, May 25), the early fruit enlargement stage (M6, June 25), and the fruit enlargement stage (M7, July 25), respectively. The same amount of Se-free organic fertilizer was applied as a control. Herein, the regulatory mechanism by which exogenous Se exerts its anti-browning effect in freshly cut apples was investigated. The results showed that the M7 treatment applied in Se-reinforced apples could remarkably inhibit their browning at 1 h after being freshly cut. Additionally, the expression of polyphenol oxidase (PPO) and peroxidase (POD) genes treated with exogenous Se was significantly reduced compared to controls. Moreover, the lipoxygenase (LOX) and phospholipase D (PLD) genes, which are involved in membrane lipid oxidation, were expressed at higher levels in the control. The gene expression levels of the antioxidant enzymes catalase (CAT), superoxide dismutase (SOD), glutathione S-transferase (GST), and ascorbate peroxidase (APX) were upregulated in the different exogenous Se treatment groups. Similarly, the main metabolites measured during the browning process were phenols and lipids; thus, it could be speculated that the mechanism by which exogenous Se produces its anti-browning effect may be by reducing phenolase activity, improving the antioxidant capacity of the fruits, and alleviating membrane lipid peroxidation. In summary, this study provides evidence regarding and insight into the response mechanism employed by exogenous Se to inhibit browning in freshly cut apples

<i>Schisandra chinensis</i> Bee Pollen Ameliorates Colitis in Mice by Modulating Gut Microbiota and Regulating Treg/Th17 Balance

Colitis is a chronic disease associated with alterations in the composition of gut microbiota. Schisandra chinensis bee pollen extract (SCPE) has been proved to be rich in phenolic compounds and effective in modulating gut microbiota, but its effect on colitis and the underlying mechanism remains unclear. This study investigates the relationship between colitis amelioration and the gut microbiota regulation of SCPE via fecal microbial transplantation (FMT). The results showed that administration of 20.4 g/kg BW of SCPE could primely ameliorate colitis induced by dextran sulfate sodium (DSS) in mice, showing as more integration of colon tissue structure and the colonic epithelial barrier, as well as lower oxidative stress and inflammation levels compared with colitis mice. Moreover, SCPE supplement restored the balance of T regulatory (Treg) cells and T helper 17 (Th17) cells. Gut microbiota analysis showed SCPE treatment could reshape the gut microbiota balance and improve the abundance of gut microbiota, especially the beneficial bacteria (Akkermansia and Lactobacillus) related to the production of short-chain fatty acids and the regulation of immunity. Most importantly, the protection of 20.4 g/kg BW of SCPE on colitis can be perfectly transmitted by fecal microbiota. Therefore, the gut microbiota–SCFAS–Treg/Th17 axis can be the main mechanism for SCPE to ameliorate colitis. This study suggests that SCPE can be a new promising functional food for prevention and treatment of colitis by reshaping gut microbiota and regulating gut immunity

A Temperature-Independent Methodology for Polymer Bitumen Modification Evaluation Based on DSR Measurement

Owing to the continuous increase of traffic loads, bitumen modification has been manifested as an efficient methodology to enhance asphaltic pavement performance. Currently, the modification index, defined as the ratio of mechanical properties (e.g., complex modulus) before and after bitumen modification, is extensively adopted to evaluate the modification degree. However, bituminous materials behave as temperature-dependent, which indicates that the mechanical property varies with measured temperatures. As a result, the calculated modification index also shows temperature-dependent property, which inhibits the use of modification index. For this reason, this study introduced a method to eliminate the temperature-dependency of the modification index. In specific, a mathematical model considering the properties of modifiers was firstly established to predict the modification index-temperature curve (MI-T curve). In what follows, the temperature-dependency of modification index was analyzed to verify the proposed model on three types of modifiers, which were graphene, Styrene-Butadiene-Styrene (SBS), and Ethyl-Vinyl-Acetate (EVA), respectively. The results indicated that the developed model could efficiently predict the MI-T curves. Besides, the effective modification area (EMA) and optimal modification index (OMI) were two reasonable indicators that evaluate the bitumen modification without considering the temperature-dependency.Pavement Engineerin