On-line extensible bin packing with unequal bin sizes

Analysis of Algorithm

Dynamic Model and Analysis of a Sucker-rod Pump Injection-production System

Injection and production at the same oil well is an effective way to achieve stable production and control water-cut. A sucker-rod pump injection-production system is composed of a production pump, piston seal, injection pump, and so on. It separates oil and water down the well hole using gravity, then injects the water directly into the formation while the concentrated oil is brought to the surface. To explore the complex forces involved in the system, static and dynamic models of the injection pump, sealed piston and production pump are established from the bottom up based on the mechanical method, and a solution is established using the difference method. Finally, a dynamic simulation of the system is compiled in Visual Basic V6.0, and the correctness and practicality of the simulation are verified by field measurements

A meta-deep-learning framework for spatio-temporal underwater SSP inversion

Sound speed distribution, represented by a sound speed profile (SSP), is of great significance because the nonuniform distribution of sound speed will cause signal propagation path bending with Snell effect, which brings difficulties in precise underwater localization such as emergency rescue. Compared with conventional SSP measurement methods via the conductivity-temperature-depth (CTD) or sound-velocity profiler (SVP), SSP inversion methods leveraging measured sound field information have better real-time performance, such as matched field process (MFP), compressed sensing (CS) and artificial neural networks (ANN). Due to the difficulty in measuring empirical SSP data, these methods face with over-fitting problem in few-shot learning that decreases the inversion accuracy. To rapidly obtain accurate SSP, we propose a task-driven meta-deep-learning (TDML) framework for spatio-temporal SSP inversion. The common features of SSPs are learned through multiple base learners to accelerate the convergence of the model on new tasks, and the model’s sensitivity to the change of sound field data is enhanced via meta training, so as to weaken the over-fitting effect and improve the inversion accuracy. Experiment results show that fast and accurate SSP inversion can be achieved by the proposed TDML method

On-line extensible bin packing with unequal bin sizes

Analysis of AlgorithmsIn the extensible bin packing problem we are asked to pack a set of items into a given number of bins, each with an original size. However, the original bin sizes can be extended if necessary. The goal is to minimize the total size of the bins. We consider the problem with unequal (original) bin sizes and give the complete analysis on a list scheduling algorithm (LS). Namely we present tight bounds of LS for every collection of original bin sizes and every number of bins. We further show better on-line algorithms for the two-bin case and the three-bin case. Interestingly, it is proved that the on-line algorithms have better competitive ratios for unequal bins than for equal bins. Some variants of the problem are also discussed