An unsatisfactory property of particle filters is that they
may become inefficient when the observation noise is low.
In this paper we consider a simple-to-implement particle filter,
called ‘LIS-based particle filter’, whose aim is to overcome
the above mentioned weakness. LIS-based particle
filters sample the particles in a two-stage process that uses
information of the most recent observation, too. Experiments
with the standard bearings-only tracking problem indicate
that the proposed new particle filter method is indeed
a viable alternative to other methods.
The package includes 3 Matlab-interfaces to the c-code:
1. inference.m
An interface to the full inference package, includes several methods for
approximate inference: Loopy Belief Propagation, Generalized Belief
Propagation, Mean-Field approximation, and 4 monte-carlo sampling methods
(Metropolis, Gibbs, Wolff, Swendsen-Wang).
Use "help inference" from Matlab to see all options for usage.
2. gbp_preprocess.m and gbp.m
These 2 interfaces split Generalized Belief Propagation into the pre-process
stage (gbp_preprocess.m) and the inference stage (gbp.m), so the user may use
only one of them, or changing some parameters in between.
Use "help gbp_preprocess" and "help gbp" from Matlab.
3. simulatedAnnealing.m
An interface to the simulated-annealing c-code. This code uses Metropolis
sampling method, the same one used for inference.
Use "help simulatedAnnealing" from Matlab.
Program helping you to remember the route.
It cab be route from conference room to coffee-room, it can be tourist trip, it can be pathway in labyrinth. during first traversal you make notes in you phone, specifying direction of movement and target of each step. Phone remembers how much time each steps takes. Then you can just inspect information about this trip and check duration of each stage and the whole trip. You can also replay it in forward and backward direction. So if somebody show you the shortest way to coffee machine, you can easily find the way back and can repeat this trip in future.
In this paper we present a classifier called bi-density twin support vector machines (BDTWSVMs) for data classification. In the training stage, BDTWSVMs first compute the relative density degrees for all training points using the intra-class graph whose weights are determined by a local scaling heuristic strategy, then optimize a pair of nonparallel hyperplanes through two smaller sized support vector machine (SVM)-typed problems. In the prediction stage, BDTWSVMs assign to the class label depending
on the kernel density degree-based distances from each test point to the two hyperplanes. BDTWSVMs not only inherit good properties from twin support vector machines (TWSVMs) but also give good description for data points. The experimental results on toy as well as publicly available datasets
indicate that BDTWSVMs compare favorably with classical SVMs and TWSVMs in terms of generalization
The continuous progress in modern power device technology is increasingly
supported by power-specific modeling methodologies and dedicated simulation
tools. These enable the detailed analysis of operational principles on the the device
and on the system level; in particular, they allow the designer to perform trade-
off studies by investigating the operation of competing design variants in a very
early stage of the development process. Furthermore, using predictive computer
simulation makes it possible to analyze the device and system behavior not only
under regularoperatingconditions, but also at the rim of the safe-operatingarea and
beyond of it, where destructive processes occur that limit the lifetime of a power
system.
ABSTRACTThe flyback power stage is a popular choice for single and multiple output dc-to-dc converters at powerlevels of 150 Watts or less. Without the output inductor required in buck derived topologies, such as theforward or push-pull converter, the component count and cost are reduced. This application note will reviewthe design procedure for the power stage and control electronics of a flyback converter. In these isolatedconverters, the error signal from the secondary still needs to cross the isolation boundary to achieveregulation. By using the UC3965 Precision Reference with Low Offset Error Amplifier on the secondaryside to drive an optocoupler and the UCC3809 Economy Primary Side Controller on the primary side, asimple and low cost 50 Watt isolated power supply is realized.
