Nonlocal q-fractional boundary value problem with Stieltjes integral conditions

In this paper, we are dedicated to investigating a new class of one-dimensional lower-order fractional q-differential equations involving integral boundary conditions supplemented with Stieltjes integral. This condition is more general as it contains an arbitrary order derivative. It should be pointed out that the problem discussed in the current setting provides further insight into the research on nonlocal and integral boundary value problems. We first give the Green's functions of the boundary value problem and then develop some properties of the Green's functions that are conductive to our main results. Our main aim is to present two results: one considering the uniqueness of nontrivial solutions is given by virtue of contraction mapping principle associated with properties of u0-positive linear operator in which Lipschitz constant is associated with the first eigenvalue corresponding to related linear operator, while the other one aims to obtain the existence of multiple positive solutions under some appropriate conditions via standard fixed point theorems due to Krasnoselskii and Leggett–Williams. Finally, we give an example to illustrate the main results. &nbsp

The Optimization of Power Dispatch for Hydro-thermal Power Systems

AbstractA model in power market for hydro-thermal-nuclear power system has been proposed in this paper. Nuclear units, hydropower units and coal-fired power units are considered to have the renewable energy best used. The model contains two sub-models: Model1 and Model2. Model1 is used to solve the problem of allocating hydro loads and thermal loads, while Model2 is used to solve the problem of optimal power dispatch within hydro units and coal-fired units. Simulation and sensitivity analysis have been done in a case study. The results reveil that the proposed model is correct and the solution approach is effective

FUNCTIONAL ROLE OF RNA EDITING OF CAV1.3 IQ DOMAIN

Ph.DDOCTOR OF PHILOSOPH

STAR: An Efficient Softmax Engine for Attention Model with RRAM Crossbar

RRAM crossbars have been studied to construct in-memory accelerators for neural network applications due to their in-situ computing capability. However, prior RRAM-based accelerators show efficiency degradation when executing the popular attention models. We observed that the frequent softmax operations arise as the efficiency bottleneck and also are insensitive to computing precision. Thus, we propose STAR, which boosts the computing efficiency with an efficient RRAM-based softmax engine and a fine-grained global pipeline for the attention models. Specifically, STAR exploits the versatility and flexibility of RRAM crossbars to trade off the model accuracy and hardware efficiency. The experimental results evaluated on several datasets show STAR achieves up to 30.63x and 1.31x computing efficiency improvements over the GPU and the state-of-the-art RRAM-based attention accelerators, respectively