University of Texas at Arlington

About: University of Texas at Arlington is a education organization based out in Arlington, Texas, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 11758 authors who have published 28598 publications receiving 801626 citations. The organization is also known as: UT Arlington & University of Texas-Arlington.

Optimal Power Flow Based Upon P-Q Decomposition

Abstract: The optimal power flow problem is formulated based upon the decoupling principle well recognized in bulk power transmission loadflow. This principle is exploited by decomposing the OPF formulation into a P-Problem (P-?real power model) and a Q-Problem (Q-V reactive power model); which simplifies the formulation, improves computation time and permits a certain flexibility in the types of calculations desired (i.e., P-Problem, Q-Problem or both).

Robust Adaptive Fault-Tolerant Control of Multiagent Systems With Uncertain Nonidentical Dynamics and Undetectable Actuation Failures

Abstract: This paper studies the distributed consensus problem of multiagent systems (MASs) in the presence of nonidentical unknown nonlinear dynamics and undetectable actuation failures. Of particular interest is the development of a robust adaptive fault-tolerant consensus protocol capable of compensating uncertain dynamics/disturbances and time-varying yet unpredictable actuation failures simultaneously. By introducing the virtual parameter estimation error into the artfully chosen Lyapunov function, the consensus problem is solved with a robust adaptive fault-tolerant control scheme based upon local (neighboring) agent state information. It is shown that the proposed method is user friendly in that there is no need for detail dynamic information of the agent or costly detection/diagnosis of the actuation faults in control design and implementation, resulting in a structurally simple and computationally inexpensive solution for the leaderless consensus problem of MAS. Simulation results illustrate and verify the benefits and effectiveness of the proposed scheme.

SMILES-BERT: Large Scale Unsupervised Pre-Training for Molecular Property Prediction

Abstract: With the rapid progress of AI in both academia and industry, Deep Learning has been widely introduced into various areas in drug discovery to accelerate its pace and cut R&D costs. Among all the problems in drug discovery, molecular property prediction has been one of the most important problems. Unlike general Deep Learning applications, the scale of labeled data is limited in molecular property prediction. To better solve this problem, Deep Learning methods have started focusing on how to utilize tremendous unlabeled data to improve the prediction performance on small-scale labeled data. In this paper, we propose a semi-supervised model named SMILES-BERT, which consists of attention mechanism based Transformer Layer. A large-scale unlabeled data has been used to pre-train the model through a Masked SMILES Recovery task. Then the pre-trained model could easily be generalized into different molecular property prediction tasks via fine-tuning. In the experiments, the proposed SMILES-BERT outperforms the state-of-the-art methods on all three datasets, showing the effectiveness of our unsupervised pre-training and great generalization capability of the pre-trained model.

Nanoporous TiO2 and WO3 films by anodization of titanium and tungsten substrates: influence of process variables on morphology and photoelectrochemical response.

Abstract: The photoelectrochemical response of nanoporous films, obtained by anodization of Ti and W substrates in a variety of corrosive media and at preselected voltages in the range from 10 to 60 V, was studied. The as-deposited films were subjected to thermal anneal and characterized by scanning electron microscopy and X-ray diffraction. Along with the anodization media developed by previous authors, the effect of poly(ethylene glycol) (PEG 400) or d-mannitol as a modifier to the NH4F electrolyte and glycerol addition to the oxalic acid electrolyte was studied for TiO2 and WO3, respectively. In general, intermediate anodization voltages and film growth times yielded excellent-quality photoelectrochemical response for both TiO2 and WO3 as assessed by linear-sweep photovoltammetry and photoaction spectra. The photooxidation of water and formate species was used as reaction probes to assess the photoresponse quality of the nanoporous oxide semiconductor films. In the presence of formate as an electron donor, the i...