DWRSeg: Rethinking Efficient Acquisition of Multi-scale Contextual Information for Real-time Semantic Segmentation

Many current works directly adopt multi-rate depth-wise dilated convolutions to capture multi-scale contextual information simultaneously from one input feature map, thus improving the feature extraction efficiency for real-time semantic segmentation. However, this design may lead to difficult access to multi-scale contextual information because of the unreasonable structure and hyperparameters. To lower the difficulty of drawing multi-scale contextual information, we propose a highly efficient multi-scale feature extraction method, which decomposes the original single-step method into two steps, Region Residualization-Semantic Residualization. In this method, the multi-rate depth-wise dilated convolutions take a simpler role in feature extraction: performing simple semantic-based morphological filtering with one desired receptive field in the second step based on each concise feature map of region form provided by the first step, to improve their efficiency. Moreover, the dilation rates and the capacity of dilated convolutions for each network stage are elaborated to fully utilize all the feature maps of region form that can be achieved.Accordingly, we design a novel Dilation-wise Residual (DWR) module and a Simple Inverted Residual (SIR) module for the high and low level network, respectively, and form a powerful DWR Segmentation (DWRSeg) network. Extensive experiments on the Cityscapes and CamVid datasets demonstrate the effectiveness of our method by achieving a state-of-the-art trade-off between accuracy and inference speed, in addition to being lighter weight. Without pretraining or resorting to any training trick, we achieve an mIoU of 72.7% on the Cityscapes test set at a speed of 319.5 FPS on one NVIDIA GeForce GTX 1080 Ti card, which exceeds the latest methods of a speed of 69.5 FPS and 0.8% mIoU. The code and trained models are publicly available

Fault tolerant control for fixed set-point control nonlinear networked control systems

U radu se predlaže pristup za dobivanje algoritma regulatora koji tolerira grešku za vrstu umreženog upravljačkog sustava koji ima regulator s čvrstom zadanom točkom i nelinearno postrojenje. Najprije opisujemo nelinearni umreženi upravljački sustav te lineariziramo model postrojenja u radnoj točki, zatim konstruiramo spojeni model cijelog sustava koji uključuje sve faktore relevantne za naše istraživanje kao što su kašnjenje inducirano mrežom, gubitak podataka, greške senzora i aktuatora itd. Na temelju ovog modela postignuto je stanje stabilnosti u sustavu konstruiranjem Lyapunove funkcije. Zatim je iz stanja stabilnosti dobiven upravljački algoritam tolerancije greške. Konačno je na numeričkom primjeru dokazana validnost teorije.This paper proposes an approach for achieving fault tolerant control algorithm for a kind of networked control system which is fixed set-point control and with nonlinear plant. We firstly describe the nonlinear networked control system and linearize the model of the plant at the operating point, then construct a synthesis model of the whole system, which includes all relevant factors in our research such as network induced time delay, data packet loss, the faults of sensors and actuators etc. Based on this model, the stability condition in meaning square sense of the system is gained by constructing a Lyapunov function. Then the fault tolerant control algorithm is obtained from the stability condition. Lastly, a numerical example is used to prove the validity of the theory

Travel routes estimation in transportation systems modeled by Petri Nets

This paper develops an algorithm for estimating the route(s) with the least total travel time in transportation systems that are modeled as Petri nets. Each transition in the net is associated with a cost that is related to the travel time from a starting point to a destination. This cost can be computed from the traffic flow and vehicle speed information obtained from the traffic data via an approach called Adaptive Gray Threshold Traffic Parameters Measurement (AGTTPM). Given a transportation system modeled as a Petri net that has cost on each transition, we aim at finding the transition firing sequences (traffic routes) from an initial marking (a starting point) to a final marking (a destination) within a certain time period T and have the least total cost (the least total travel time). In this paper we develop an algorithm that is able to systematically obtain these routes with the least total travel time

Superpixel-Based Attention Graph Neural Network for Semantic Segmentation in Aerial Images

Semantic segmentation is one of the significant tasks in understanding aerial images with high spatial resolution. Recently, Graph Neural Network (GNN) and attention mechanism have achieved excellent performance in semantic segmentation tasks in general images and been applied to aerial images. In this paper, we propose a novel Superpixel-based Attention Graph Neural Network (SAGNN) for semantic segmentation of high spatial resolution aerial images. A K-Nearest Neighbor (KNN) graph is constructed from our network for each image, where each node corresponds to a superpixel in the image and is associated with a hidden representation vector. On this basis, the initialization of the hidden representation vector is the appearance feature extracted by a unary Convolutional Neural Network (CNN) from the image. Moreover, relying on the attention mechanism and recursive functions, each node can update its hidden representation according to the current state and the incoming information from its neighbors. The final representation of each node is used to predict the semantic class of each superpixel. The attention mechanism enables graph nodes to differentially aggregate neighbor information, which can extract higher-quality features. Furthermore, the superpixels not only save computational resources, but also maintain object boundary to achieve more accurate predictions. The accuracy of our model on the Potsdam and Vaihingen public datasets exceeds all benchmark approaches, reaching 90.23% and 89.32%, respectively

Bioactive polysaccharides from lotus as potent food supplements: a review of their preparation, structures, biological features and application prospects

Lotus is a famous plant of the food and medicine continuum for millennia, which possesses unique nutritional and medicinal values. Polysaccharides are the main bioactive component of lotus and have been widely used as health nutritional supplements and therapeutic agents. However, the industrial production and application of lotus polysaccharides (LPs) are hindered by the lack of a deeper understanding of the structure–activity relationship (SAR), structural modification, applications, and safety of LPs. This review comprehensively comments on the extraction and purification methods and structural characteristics of LPs. The SARs, bioactivities, and mechanisms involved are further evaluated. The potential application and safety issues of LPs are discussed. This review provides valuable updated information and inspires deeper insights for the large scale development and application of LPs