Showing papers by "University of Electronic Science and Technology of China published in 2022"

Adaptive Digital Twin and Multiagent Deep Reinforcement Learning for Vehicular Edge Computing and Networks

Abstract: Technological advancements of urban informatics and vehicular intelligence have enabled connected smart vehicles as pervasive edge computing platforms for a plethora of powerful applications. However, varies types of smart vehicles with distinct capacities, diverse applications with different resource demands as well as unpredictive vehicular topology, pose significant challenges on realizing efficient edge computing services. To cope with these challenges, we incorporate digital twin technology and artificial intelligence into the design of a vehicular edge computing network. It centrally exploits potential edge service matching through evaluating cooperation gains in a mirrored edge computing system, while distributively scheduling computation task offloading and edge resource allocation in an multiagent deep reinforcement learning approach. We further propose a coordination graph driven vehicular task offloading scheme, which minimizes offloading costs through efficiently integrating service matching exploitation and intelligent offloading scheduling in both digital twin and physical networks. Numerical results based on real urban traffic datasets demonstrate the efficiency of our proposed schemes.

Spatial and temporal distribution characteristics of haze and pollution particles in China based on spatial statistics

Abstract: In recent years, the occurrence and frequency of haze have constantly been increasing, bringing severe threats to people's daily lives. To this end, this paper discusses the spatial and temporal distribution characteristics of severe haze in China, analyzes the interaction between haze pollution and the influence of economy and energy structure on haze in 31 provinces of China. It provides references for the treatment of haze weather and the prevention and control of air pollution in China. This paper mainly adopts the spatial autocorrelation method. The data processed mainly includes API (Air Pollution Index) and meteorological station data. Combined with the statistical yearbook data, this paper conducts multi-aspect research and exploration. By using statistical methods to study the haze distribution in China, we found that the haze and PM2.5 concentrations were mainly distributed in Beijing-Tianjin-Hebei, Shandong Province, the northern northwest, southeastern Sichuan, and Chongqing. Haze distribution has obvious seasonality, more in winter and less in summer. There are also regional differences in the concentration distribution of urban pollutants. The concentration of SO2 and absorbable particles are relatively high in northern cities. In contrast, that of southern cities is relatively low and changes with seasonal changes.

Spatial and temporal distribution characteristics of haze and pollution particles in China based on spatial statistics

Abstract: In recent years, the occurrence and frequency of haze have constantly been increasing, bringing severe threats to people's daily lives. To this end, this paper discusses the spatial and temporal distribution characteristics of severe haze in China, analyzes the interaction between haze pollution and the influence of economy and energy structure on haze in 31 provinces of China. It provides references for the treatment of haze weather and the prevention and control of air pollution in China. This paper mainly adopts the spatial autocorrelation method. The data processed mainly includes API (Air Pollution Index) and meteorological station data. Combined with the statistical yearbook data, this paper conducts multi-aspect research and exploration. By using statistical methods to study the haze distribution in China, we found that the haze and PM 2.5 concentrations were mainly distributed in Beijing-Tianjin-Hebei, Shandong Province, the northern northwest, southeastern Sichuan, and Chongqing. Haze distribution has obvious seasonality, more in winter and less in summer. There are also regional differences in the concentration distribution of urban pollutants. The concentration of SO 2 and absorbable particles are relatively high in northern cities. In contrast, that of southern cities is relatively low and changes with seasonal changes. • Focuses on the spatial and temporal distribution characteristics of severe haze in China based on pollution-related data from the Ministry of Environmental Protection. • Analyzes the interaction between haze and the economy and energy structure in 31 provinces of China, and provides reference for the treatment and the prevention and control of air pollution in China. • The haze were concentrated in Beijing-Tianjin-Hebei region, Shandong Province, the northern northwest region, southeastern Sichuan province, and Chongqing. • According to the spatial autocorrelation, haze distribution has obvious seasonality, more in winter and less in summer. • The concentration of SO 2 and absorbable particles is relatively high in northern cities, while that of southern cities is relatively low, and changes with seasonal changes

Adaptive control of time delay teleoperation system with uncertain dynamics

Abstract: A bilateral adaptive control method based on PEB control structure is designed for a class of time-delay force feedback teleoperation system without external interference and internal friction to study the uncertainty of dynamic parameters and time delay. The stability and tracking performances of the closed-loop constant time delay teleoperation system are analyzed by Lyapunov stability theory. Finally, the controller designed in this paper is successfully applied to the teleoperation system composed of a two-degree of freedom rotating manipulator as the master robot and the slave robot. The simulation is carried out in no operator and environment force or with operator and environment force. The adaptive bilateral control method's control performance is compared with that of the traditional time-delay teleoperation system. Finally, it is verified that the method has good control performance.

N-doped carbon nanotubes supported CoSe 2 nanoparticles: A highly efficient and stable catalyst for H 2 O 2 electrosynthesis in acidic media.

Abstract: Electrocatalytic oxygen reduction reaction (ORR) provides an attractive alternative to anthraquinone process for H2O2 synthesis. Rational design of earth-abundant electrocatalysts for H2O2 synthesis via a two-electron ORR process in acids is attractive but still very challenging. In this work, we report that nitrogen-doped carbon nanotubes as a multi-functional support for CoSe2 nanoparticles not only keep CoSe2 nanoparticles well dispersed but alter the crystal structure, which in turn improves the overall catalytic behaviors and thereby renders high O2-to-H2O2 conversion efficiency. In 0.1 M HClO4, such CoSe2@NCNTs hybrid delivers a high H2O2 selectivity of 93.2% and a large H2O2 yield rate of 172 ppm·h−1 with excellent durability up to 24 h. Moreover, CoSe2@NCNTs performs effectively for organic dye degradation via electro-Fenton process.

Improving the intrinsic electronic conductivity of NiMoO 4 anodes by phosphorous doping for high lithium storage

Abstract: Heteroatom doping is one of the most promising strategies toward regulating intrinsically sluggish electronic conductivity and kinetic reaction of transition metal oxides for enhancing their lithium storage. Herein, we designed phosphorus-doped NiMoO4 nanorods (P-NiMoO4) by using a facile hydrothermal method and subsequent low-temperature phosphorization treatment. Phosphorus doping played an indispensable role in significantly improving electronic conductivity and the Li+ diffusion kinetics of NiMoO4 materials. Experimental investigation and density functional theory calculation demonstrated that phosphorus doping can expand the interplanar spacing and alter electronic structures of NiMoO4 nanorods. Meanwhile, the introduced phosphorus dopant can generate some oxygen vacancies on the surface of NiMoO4, which can accelerate Li+ diffusion kinetics and provide more active site for lithium storage. As excepted, P-NiMoO4 electrode delivered a high specific capacity (1,130 mAh·g−1 at 100 mA·g−1 after 100 cycles), outstanding cycling durability (945 mAh·g−1 at 500 mA·g−1 over 200 cycles), and impressive rate performance (640 mAh·g−1 at 2,000 mA·g−1) for lithium ion batteries (LIBs). This work could provide a potential strategy for improving intrinsic conductivity of transition metal oxides as high-performance anodes for LIBs.

DeepM6ASeq-EL: prediction of human N6-methyladenosine (m6A) sites with LSTM and ensemble learning

Abstract: N6-methyladenosine (m6A) is a prevalent methylation modification and plays a vital role in various biological processes, such as metabolism, mRNA processing, synthesis, and transport. Recent studies have suggested that m6A modification is related to common diseases such as cancer, tumours, and obesity. Therefore, accurate prediction of methylation sites in RNA sequences has emerged as a critical issue in the area of bioinformatics. However, traditional high-throughput sequencing and wet bench experimental techniques have the disadvantages of high costs, significant time requirements and inaccurate identification of sites. But through the use of traditional experimental methods, researchers have produced many large databases of m6A sites. With the support of these basic databases and existing deep learning methods, we developed an m6A site predictor named DeepM6ASeq-EL, which integrates an ensemble of five LSTM and CNN classifiers with the combined strategy of hard voting. Compared to the state-of-the-art prediction method WHISTLE (average AUC 0.948 and 0.880), the DeepM6ASeq-EL had a lower accuracy in m6A site prediction (average AUC: 0.861 for the full transcript models and 0.809 for the mature messenger RNA models) when tested on six independent datasets.

Smog prediction based on the deep belief - BP neural network model (DBN-BP)

Abstract: Smog pollution is becoming a significant problem for people worldwide, becoming an essential threat to the global environment. Many studies on haze already exist, which still need to continue in-depth research to better deal with haze problems. Due to its unique geographical environment, Sichuan has become one of the areas with severe smog pollution. Therefore, the research and prediction of smog pollution in Sichuan has become an urgent need. This paper proposes a deep learning technology based on a Deep Belief-Back Propagation neural network. It makes in-depth prediction research by using the air pollution data of PM2.5, PM10, O 3 , CO NO 2, and SO 2 in Sichuan smog to provide a decision-making basis for predicting and preventing smog polluted weather. According to the prediction results of the model, the concentrations of PM2.5 and PM10 in Chengdu were predicted. The analysis shows that the larger the number of hidden layers in the belief network, the higher the prediction accuracy. Under the same network, the prediction accuracy of PM2.5 is significantly higher than that of PM10. Compared with the traditional Back Propagation neural network, the prediction effect of the Deep Belief-Back Propagation neural network is better. • Based on the air pollution data of PM2.5, PM10, O3, Co, NO2 and SO2 in Sichuan smog, this paper makes an prediction research for predicting and preventing smog polluted weather. • Aiming at the problem of PM2.5 and PM10 concentration prediction, this paper constructs a prediction model based on Deep Belief -Back Propagation neural network. • The results show that the prediction effect of Deep Belief - BP neural network is better than BP neural network, and can quickly extract the concentration characteristics in the first 24 times.

Hiplot: a comprehensive and easy-to-use web service for boosting publication-ready biomedical data visualization

Abstract: Abstract Complex biomedical data generated during clinical, omics and mechanism-based experiments have increasingly been exploited through cloud- and visualization-based data mining techniques. However, the scientific community still lacks an easy-to-use web service for the comprehensive visualization of biomedical data, particularly high-quality and publication-ready graphics that allow easy scaling and updatability according to user demands. Therefore, we propose a community-driven modern web service, Hiplot (https://hiplot.org), with concise and top-quality data visualization applications for the life sciences and biomedical fields. This web service permits users to conveniently and interactively complete a few specialized visualization tasks that previously could only be conducted by senior bioinformatics or biostatistics researchers. It covers most of the daily demands of biomedical researchers with its equipped 240+ biomedical data visualization functions, involving basic statistics, multi-omics, regression, clustering, dimensional reduction, meta-analysis, survival analysis, risk modelling, etc. Moreover, to improve the efficiency in use and development of plugins, we introduced some core advantages on the client-/server-side of the website, such as spreadsheet-based data importing, cross-platform command-line controller (Hctl), multi-user plumber workers, JavaScript Object Notation-based plugin system, easy data/parameters, results and errors reproduction and real-time updates mode. Meanwhile, using demo/real data sets and benchmark tests, we explored statistical parameters, cancer genomic landscapes, disease risk factors and the performance of website based on selected native plugins. The statistics of visits and user numbers could further reflect the potential impact of this web service on relevant fields. Thus, researchers devoted to life and data sciences would benefit from this emerging and free web service.