Stabilisation of descriptor Markovian jump systems with partially unknown transition probabilities

This paper is concerned with the stability and stabilisation problems for continuous-time descriptor Markovian jump systems with partially unknown transition probabilities. In terms of a set of coupled linear matrix inequalities (LMIs), a necessary and sufficient condition is firstly proposed, which ensures the systems to be regular, impulse-free and stochastically stable. Moreover, the corresponding necessary and sufficient condition on the existence of a mode-dependent state-feedback controller, which guarantees the closed-loop systems stochastically admissible by employing the LMI technique, is derived; the stabilizing state-feedback gain can also be expressed via solutions of the LMIs. Finally, numerical examples are given to demonstrate the validity of the proposed methods

Mitigation of speckle noise in laser Doppler vibrometry by using a scanning average method

We present a scanning average method used in laser Doppler vibrometry systems for mitigating the noise induced by dynamic speckles. In this method, the measurement beam is scanned over the target surface within the area of interest at a relatively high frequency. Then an averaging operation (e.g., low-pass filtering) is applied to the acquired photocurrent signals to remove the impacts of the scan. Movement signals recovered from the averaged photocurrents turn out to have lower speckle-induced noise. We report the experimental demonstration of this technique through the use of a silicon-based photonic integrated circuit. (C) 2019 Optical Society of Americ

Efficient Real-time Path Planning with Self-evolving Particle Swarm Optimization in Dynamic Scenarios

Particle Swarm Optimization (PSO) has demonstrated efficacy in addressing static path planning problems. Nevertheless, such application on dynamic scenarios has been severely precluded by PSO's low computational efficiency and premature convergence downsides. To address these limitations, we proposed a Tensor Operation Form (TOF) that converts particle-wise manipulations to tensor operations, thereby enhancing computational efficiency. Harnessing the computational advantage of TOF, a variant of PSO, designated as Self-Evolving Particle Swarm Optimization (SEPSO) was developed. The SEPSO is underpinned by a novel Hierarchical Self-Evolving Framework (HSEF) that enables autonomous optimization of its own hyper-parameters to evade premature convergence. Additionally, a Priori Initialization (PI) mechanism and an Auto Truncation (AT) mechanism that substantially elevates the real-time performance of SEPSO on dynamic path planning problems were introduced. Comprehensive experiments on four widely used benchmark optimization functions have been initially conducted to corroborate the validity of SEPSO. Following this, a dynamic simulation environment that encompasses moving start/target points and dynamic/static obstacles was employed to assess the effectiveness of SEPSO on the dynamic path planning problem. Simulation results exhibit that the proposed SEPSO is capable of generating superior paths with considerably better real-time performance (67 path planning computations per second in a regular desktop computer) in contrast to alternative methods. The code of this paper can be accessed here.Comment: 10 pages, 7 figures, 10 table

Simplified analytical model and balanced design approach for light-weight wood-based structural panel in bending

AbstractThis paper presents a simplified analytical model and balanced design approach for modeling light-weight wood-based structural panels in bending. Because many design parameters are required to input for the model of finite element analysis (FEA) during the preliminary design process and optimization, the equivalent method was developed to analyze the mechanical performance of panels based on experimental results. The bending deflection, normal strain and shear strain of the panels with various configurations were investigated using four point bending test. The results from the analytical model matched well with the experimental data, especially, the prediction for maximum deflection of the panels under failure load. The normal strain and shear strain calculated by the model also agreed with the experimental data. The failure criterion was determined by the failure modes using a 3-dimensional diagram with apparent normal and shear strain. For demonstration, panels 1 and 2 with a fixed core were modeled using the balanced design approach for optimal face thickness. The results showed that both the 3-dimensional diagram and analytical model provided similar thickness results, which were verified by the FEA for wood-based structural panels

Effect of different rice transplanting patterns on microbial community in water, sediment, and Procambarus clarkii intestine in rice-crayfish system

Although the microbial ecology of integrated rice-crayfish farming systems is receiving increasing attention with the expanding application area in China, the effects of rice transplanting patterns on the microbial community of water, sediment and Procambarus clarkii intestine in rice-crayfish system has yet to be determined. This study explored the microbial community present in water, sediment and intestine samples from three transplant patterns (rice crayfish with wide-narrow row transplanting, rice-crayfish with normal transplanting and pond-crayfish, abbreviated as RC-W, RC, and PC, respectively) using high-throughput sequencing. The results showed that the dominant microbial taxa from sediment, surrounding water, and intestine at phylum level were Proteobacteria, Chloroflexi, Cyanobacteria, Actinobacteria, Bacteroidetes. The patterns of rice transplanting had significant effects on microbial biodiversity and species composition in surrounding water. The OTUs community richness of water under RC group was significantly higher than that of PC group and RC-W group. The OTU relative abundance of top 10 operational taxonomic units had significantly different (p < 0.05) in the water samples from the three groups. The intestinal OTU community richness of Procambarus clarkii in the three groups was positively correlated with the community richness of water. The proximity between intestinal and water samples in PCA diagram indicated that their species composition was more similar. The results also showed that rice transplanting patterns can affect intestinal microbial biodiversity of Procambarus clarkii and the intestinal microbial biodiversity correlated with water bodies. Although the intestinal microbial diversity of crayfish in RC-W group was lower than that in RC group, the relative abundance of potential pathogenic bacteria, such as Vibrio, Aeromonas, in intestine of the crayfish in the RC-W group was significantly decreased under rice wide-narrow row transplanting model. Redundancy analysis revealed that environmental parameters, such as pH, DO, nitrate, which regulate the composition of microbial community structures. This study provides an understanding for microbial response to different rice transplanting pattern in rice-crayfish farming system

Decomposition Ascribed Synergistic Learning for Unified Image Restoration

Learning to restore multiple image degradations within a single model is quite beneficial for real-world applications. Nevertheless, existing works typically concentrate on regarding each degradation independently, while their relationship has been less exploited to ensure the synergistic learning. To this end, we revisit the diverse degradations through the lens of singular value decomposition, with the observation that the decomposed singular vectors and singular values naturally undertake the different types of degradation information, dividing various restoration tasks into two groups,\ie, singular vector dominated and singular value dominated. The above analysis renders a more unified perspective to ascribe the diverse degradations, compared to previous task-level independent learning. The dedicated optimization of degraded singular vectors and singular values inherently utilizes the potential relationship among diverse restoration tasks, attributing to the Decomposition Ascribed Synergistic Learning (DASL). Specifically, DASL comprises two effective operators, namely, Singular VEctor Operator (SVEO) and Singular VAlue Operator (SVAO), to favor the decomposed optimization, which can be lightly integrated into existing convolutional image restoration backbone. Moreover, the congruous decomposition loss has been devised for auxiliary. Extensive experiments on blended five image restoration tasks demonstrate the effectiveness of our method, including image deraining, image dehazing, image denoising, image deblurring, and low-light image enhancement.Comment: 13 page