MixDehazeNet : Mix Structure Block For Image Dehazing Network

Image dehazing is a typical task in the low-level vision field. Previous studies verified the effectiveness of the large convolutional kernel and attention mechanism in dehazing. However, there are two drawbacks: the multi-scale properties of an image are readily ignored when a large convolutional kernel is introduced, and the standard series connection of an attention module does not sufficiently consider an uneven hazy distribution. In this paper, we propose a novel framework named Mix Structure Image Dehazing Network (MixDehazeNet), which solves two issues mentioned above. Specifically, it mainly consists of two parts: the multi-scale parallel large convolution kernel module and the enhanced parallel attention module. Compared with a single large kernel, parallel large kernels with multi-scale are more capable of taking partial texture into account during the dehazing phase. In addition, an enhanced parallel attention module is developed, in which parallel connections of attention perform better at dehazing uneven hazy distribution. Extensive experiments on three benchmarks demonstrate the effectiveness of our proposed methods. For example, compared with the previous state-of-the-art methods, MixDehazeNet achieves a significant improvement (42.62dB PSNR) on the SOTS indoor dataset. The code is released in https://github.com/AmeryXiong/MixDehazeNet

Study on the general dynamic model of biomass drying processes

[EN] Nowadays most studies of drying processes dynamics are established on empirical models without clear physical meanings, which could not predict the drying characteristic on different dryers. In order to describe the change of temperature and water content in the cut tobacco in different dryers, a mathematical model based on heat and mass transfer phenomena was developed, and the model employed the relationship of equilibrium moisture content and air humidity as basis, the difference of moisture between biomass and wet air as mass transfer driver, and the difference of temperature between biomass and wet air as heat transfer driver. The drying experiments under different air temperature and humidity are carried out on the batch rotary dryer, and the variance of temperature and moisture content in the biomass is obtained by using infrared thermometer and oven. The model is validated by two parameters with experiment data under each condition of air temperature and humidity. The results show that the drying dynamic model is well on accuracy and universality, and it could be applied on different drying device to predict the characteristic of kinds of drying processes.Wang, L.; Li, X.; Li, Q.; Lu, D.; Li, B.; Zhu, W.; Zhang, M.... (2018). Study on the general dynamic model of biomass drying processes. En IDS 2018. 21st International Drying Symposium Proceedings. Editorial Universitat Politècnica de València. 1671-1678. https://doi.org/10.4995/IDS2018.2018.7641OCS1671167

Smooth Sliding Mode Control for Vehicle Rollover Prevention Using Active Antiroll Suspension

The rollover accidents induced by severe maneuvers are very dangerous and mostly happen to vehicles with elevated center of gravity, such as heavy-duty trucks and pickup trucks. Unfortunately, it is hard for drivers of those vehicles to predict and prevent the trend of the maneuver-induced (untripped) rollover ahead of time. In this study, a lateral load transfer ratio which reflects the load distribution of left and right tires is used to indicate the rollover criticality. An antiroll controller is designed with smooth sliding mode control technique for vehicles, in which an active antiroll suspension is installed. A simplified second order roll dynamic model with additive sector bounded uncertainties is used for control design, followed by robust stability analysis. Combined with the vehicle dynamics simulation package TruckSim, MATLAB/Simulink is used for simulating experiment. The results show that the applied controller can improve the roll stability under some typical steering maneuvers, such as Fishhook and J-turn. This direct antiroll control method could be more effective for untripped rollover prevention when driver deceleration or steering is too late. It could also be extended to handle tripped rollovers

A recognition model of driving risk based on Belief Rule-Base methodology

This paper aims to recognize driving risks in individual vehicles online based on a data-driven methodology. Existing advanced driver assistance systems (ADAS) have difficulties in effectively processing multi-source heterogeneous driving data. Furthermore, parameters adopted for evaluating the driving risk are limited in these systems. The approach of data-driven modeling is investigated in this study for utilizing the accumulation of on-road driving data. A recognition model of driving risk based on belief rule-base (BRB) methodology is built, predicting driving safety as a function of driver characteristics, vehicle state and road environment conditions. The BRB model was calibrated and validated using on-road data from 30 drivers. The test results show that the recognition accuracy of our proposed model can reach about 90% in all situations with three levels (none, medium, large) of driving risks. Furthermore, the proposed simplified model, which provides real-time operation, is implemented in a vehicle driving simulator as a reference for future ADAS and belongs to research on artificial intelligence (AI) in the automotive field.</p

A recognition model of driving risk based on Belief Rule-Base methodology

This paper aims to recognize driving risks in individual vehicles online based on a data-driven methodology. Existing advanced driver assistance systems (ADAS) have difficulties in effectively processing multi-source heterogeneous driving data. Furthermore, parameters adopted for evaluating the driving risk are limited in these systems. The approach of data-driven modeling is investigated in this study for utilizing the accumulation of on-road driving data. A recognition model of driving risk based on belief rule-base (BRB) methodology is built, predicting driving safety as a function of driver characteristics, vehicle state and road environment conditions. The BRB model was calibrated and validated using on-road data from 30 drivers. The test results show that the recognition accuracy of our proposed model can reach about 90% in all situations with three levels (none, medium, large) of driving risks. Furthermore, the proposed simplified model, which provides real-time operation, is implemented in a vehicle driving simulator as a reference for future ADAS and belongs to research on artificial intelligence (AI) in the automotive field.Intelligent Vehicle

Alteration of gut microbiome in goslings infected with goose astrovirus

ABSTRACT: Goose astrovirus (GoAstV) is an emerging avian pathogen that induces gout in goslings with a mortality of up to 50%. Organ damage caused by GoAstV infection was considered the cause of gout, but it is still unclear whether other factors are involved. Human and murine studies have linked the gut microbiome-derived urate and gout, thus we hypothesized that gut microbiome may also play an important role in gout induced by GoAstV infection. This study tested the pathogenicity of our isolated GoAstV genotype 2 strain on goslings, while the appearance of clinical signs, histopathological changes, viral distribution and the blood level of cytokines were monitored for 18 d postinfection (dpi). The dynamics in the gut microbiome were profiled by 16S sequencing and then correlated with GoAstV infection. Results showed that this study successfully developed an experimental infection model for studying the pathogenicity of the GoAstV infection which induces typical symptoms of gout. GoAstV infection significantly altered the gut microbiome of goslings with the enrichment of potential proinflammatory bacteria and depletion of beneficial bacteria that can produce short-chain fatty acids. More importantly, the microbial pathway involved in urate production was significantly increased in goslings infected with GoAstV, suggesting that gut microbiome-derived urate may also contribute to the gout symptoms. Overall, this study demonstrated the role of gut microbiome in the pathogenesis of GoAstV infection, highlighting the potential of gut microbiome-based therapeutics against gout symptoms