A5100323606- Petri Nets in System Modeling
- Distributed systems and fault tolerance
- Catalytic Processes in Materials Science
- Computational Drug Discovery Methods
- Formal Methods in Verification
- Catalysts for Methane Reforming
- Flexible and Reconfigurable Manufacturing Systems
- Network Security and Intrusion Detection
- Catalysis for Biomass Conversion
- Adaptive Control of Nonlinear Systems
- Adaptive Dynamic Programming Control
- Advanced Nanomaterials in Catalysis
- Machine Learning in Materials Science
- Business Process Modeling and Analysis
- E-commerce and Technology Innovations
- Reinforcement Learning in Robotics
- Advanced battery technologies research
- Catalysis and Hydrodesulfurization Studies
- Pharmacological Effects of Natural Compounds
- Stability and Control of Uncertain Systems
- Cancer Mechanisms and Therapy
- Advanced Control Systems Optimization
- Data-Driven Disease Surveillance
- Medicinal Plants and Bioactive Compounds
- Hepatitis B Virus Studies
Northwest Institute of Mechanical and Electrical Engineering
2024
Zhejiang University
2018-2023
University of Illinois Urbana-Champaign
2023
Nanjing Foreign Language School
2023
Shanghai Jiao Tong University
2021-2022
Beijing University of Chinese Medicine
2020-2021
Xidian University
2018-2020
State Key Laboratory of Clean Energy Utilization
2018-2019
Henan Agricultural University
2017-2019
Northeastern University
2010-2019
Yinzhihuang granules (YZHG) is a patented Chinese medicine for the treatment of hepatitis B. This study aimed to investigate intrinsic mechanisms YZHG in B and provide new evidence insights its clinical application. The chemical compounds were searched CNKI PUBMED databases, their putative targets then predicted through search SuperPred Swiss Target Prediction databases. In addition, obtained from TTD, PharmGKB DisGeNET. abovementioned data visualized using Cytoscape 3.7.1, network...
With the development of computer-assisted techniques, research communities, including biochemistry and deep learning, have been devoted into drug discovery field for over a decade. Various applications learning drawn great attention in discovery, such as molecule generation, molecular property prediction, retrosynthesis reaction prediction. While most existing surveys only focus on one applications, limiting view researchers community, this article, we present comprehensive review...
With the development of technology, massive malware become major challenge to current computer security. In our work, we implemented a detection system using deep learning on API calls. By means cuckoo sandbox, extracted calls sequence malicious programs. Through filtering and ordering redundant calls, valid sequences. Compared with GRU, BGRU, LSTM SimpleRNN, evaluated BLSTM datasets including 21,378 samples. The experimental results demonstrate that has best performance for detection,...
Abstract The selective conversion of biomass into high‐value carbon materials has recently been attracting increasing attraction. In this study, a microalga with high nitrogen content, namely Spirulina platensis , was used as both the and precursors in synthesis sponge‐like nitrogen‐enriched through one‐step activation method NaHCO 3 green activator. obtained at different temperatures were systematically characterized by using SEM, N 2 adsorption‐desorption, XRD, Raman, XPS techniques. A...
Summary In this paper, the H ∞ tracking control of linear discrete‐time systems is studied via reinforcement learning. By defining an improved value function, game algebraic Riccati equation with a discount factor obtained, which solved by iteration learning algorithms. particular, Q‐learning based on presented for control, does not require system model information and initial allowable policy. addition, to improve practicability algorithm, convergence analysis proposed algorithm given....
Bio-MCM-41 was produced from pyrolytic rice husk char in a sequential stepwise method and then used to prepare Cu/Bio-MCM-41 catalyst with good performance.
Abstract Invited for this month's cover picture is the group of Professor Shurong Wang from State Key Laboratory Clean Energy Utilization at Zhejiang University (China). The shows sponge‐like nitrogen‐containing carbon materials prepared by using a one‐step activation method nitrogen‐rich microalgae biomass. Read full text Article 10.1002/celc.201801272 .
In this paper we study multi-agent discrete-event systems where the agents can be divided into several groups, and within each group have similar or identical state transition structures. We employ a relabeling map to generate "template structure" for group, synthesize scalable supervisor whose size computational process are independent of number agents. This scalability allows remain invariant (no recomputation reconfiguration needed) if when there removed due failure added increasing...
The traditional Chinese medicine prescription Suhexiang Pill (SHXP), a classic for the treatment of plague, has been recommended in 2019 Guideline coronavirus disease (COVID-19) diagnosis and severe type COVID-19. However, bioactive compounds underlying mechanisms SHXP COVID-19 prevention have not yet elucidated. This study investigates based on network pharmacology molecular docking.First, ingredients corresponding target genes were screened from systems database analysis platform database....
In this article, we propose a new algorithm for supervisor reduction/localisation of discrete-event systems (DES). Supervisor is based on merging pairs states the that are control consistent. Our proposed employs two lists – mergeable list and non-mergeable which store state have been confirmed to be consistent or inconsistent, respectively. With these lists, our eliminates any repeated consistency checks, guarantees every pair will checked exactly once. We prove time complexity O(n2), where...
With the rapid advance of technology, peoples lives are becoming more and diverse. At same time, demand for food is not only limited to having enough eat. Therefore, companies launch a wide range products, which need be marketed consumers through various channels so as bring maximum benefits company. rise big data arrival Internet era, traditional marketing strategies have failed attract attention young people. in urgent innovating their marketing, satisfying consumer groups, following...
This paper studies the problem of stability for discrete-time systems with time-varying delay. By using Lyapunov functional approach, sufficient conditions in term linear matrix inequalities are given. The results analysis less conservativeness than existing results. A numerical example is also presented to illustrate effectiveness developed method.
In this paper, the data-based solution for state tracking control problem of linear discrete-time systems is proposed by value iteration, in which only system and information are required. Instead directly constructing error system, Bellman equation obtained via an augmentation system. Different from previous researches on problem, Q-learning based iteration used to solve augmented control, does not require initial allowable control. Finally, feasibility algorithm verified simulation.
In this paper we propose a fast algorithm for supervisor reduction/localization of discrete-event systems. Supervisor is based on merging pairs states the that are control consistent. Our proposed employs two new lists – mergeable list and non-mergeable which store state have been confirmed to be consistent or inconsistent, respectively. With these lists, our eliminates any repeated consistency checks, guarantees every pair will checked at most once. The time complexity O(n2), where n number...
With the development of computer-assisted techniques, research communities including biochemistry and deep learning have been devoted into drug discovery field for over a decade. Various applications drawn great attention in discovery, such as molecule generation, molecular property prediction, retrosynthesis reaction prediction. While most existing surveys only focus on one applications, limiting view researchers community. In this paper, we present comprehensive review aforementioned four...
Anomaly detection aims at identifying deviant samples from the normal data distribution. Much progress has been made in recent years for anomaly with self-supervised representation learning. However, most existing approaches assume training set contains either only clean or some labeled abnormal samples. With a contaminated unlabeled set, performance is degraded unclear discrimination between and To address above challenge, this paper, we propose novel unsupervised learning framework that...
This paper considers the problem of optimal sensor schedules for remote state estimation discrete-event systems. In this setting, sensors observe information from plant and transmit observable to receiver or estimator selectively. A transmission mechanism decides whether is transmitted not, according an policy, such that has sufficient satisfy purpose decision-making. To construct a mechanism, we first non-deterministic dynamic observer contains all feasible policies. Then, show updating...
In this paper we study multi-agent discrete-event systems where the agents can be divided into several groups, and within each group have similar or identical state transition structures. We employ a relabeling map to generate "template structure" for group, synthesize scalable supervisor whose size computational process are independent of number agents. This scalability allows remain invariant (no recomputation reconfiguration needed) if when there removed due failure added increasing...