BPMLL is a package for training multi-label BP neural networks. The package includes the MATLAB code of the algorithm BP-MLL, which is designed to deal with multi-label learning. It is in particular useful when a real-world object is associated with multiple labels simultaneously
CCE is a multi-instance learning method solving multi-instance problems through adapting multi-instance representation to single-instance algorithms, which is quite different from existing multi-instance learning algorithms which attempt to adapt single-instance algorithms to multi-instance representation
This toolbox contains re-implementations of four different multi-instance learners, i.e. Diverse Density, Citation-kNN, Iterated-discrim APR, and EM-DD. Ensembles of these single multi-instance learners can be built with this toolbox
The TMS320C54x, TMS320LC54x, and TMS320VC54x fixed-point, digital signal processor (DSP) families
(hereafter referred to as the ’54x unless otherwise specified) are based on an advanced modified Harvard
architecture that has one program memory bus and three data memory buses. These processors also provide
an arithmetic logic unit (ALU) that has a high degree of parallelism, application-specific hardware logic, on-chip
memory, and additional on-chip peripherals. These DSP families also provide a highly specialized instruction
set, which is the basis of the operational flexibility and speed of these DSPs.
提出了利用精密單點定位(precise point positioning,PPP)技術進行海嘯預警的方法,并利用TriP軟件對實測浮標數據進行了處理,將得出的海面高數據和海嘯波模型疊加進行了模擬分析。仿真結果表明,利用精密單點定位技術進行海嘯預警,能夠監測判斷海嘯的發生,并獲得海嘯波到達海岸的波高和時間,提供一定的預警信息。
