This paper addresses a stochastic-#ow network in which each arc or node has several capacities and may
fail. Given the demand d, we try to evaluate the system reliability that the maximum #ow of the network is
not less than d. A simple algorithm is proposed "rstly to generate all lower boundary points for d, and then
the system reliability can be calculated in terms of such points. One computer example is shown to illustrate
the solution procedure.
This paper addresses a stochastic-#ow network in which each arc or node has several capacities and may
fail. Given the demand d, we try to evaluate the system reliability that the maximum #ow of the network is
not less than d. A simple algorithm is proposed "rstly to generate all lower boundary points for d, and then
the system reliability can be calculated in terms of such points. One computer example is shown to illustrate
the solution procedure.
This paper addresses a stochastic-#ow network in which each arc or node has several capacities and may
fail. Given the demand d, we try to evaluate the system reliability that the maximum #ow of the network is
not less than d. A simple algorithm is proposed "rstly to generate all lower boundary points for d, and then
the system reliability can be calculated in terms of such points. One computer example is shown to illustrate
the solution procedure.
This paper addresses a stochastic-#ow network in which each arc or node has several capacities and may
fail. Given the demand d, we try to evaluate the system reliability that the maximum #ow of the network is
not less than d. A simple algorithm is proposed "rstly to generate all lower boundary points for d, and then
the system reliability can be calculated in terms of such points. One computer example is shown to illustrate
the solution procedure.
Parking Lot Simulation：
Parking lot attendants often park cars bumper-to-bumper, several cars deep. This maximizes the number of cars they can fit into a parking lot at the expense of complicating the process of retrieving someone s car when they want to leave. Consider the case of a person wanting to leave the parking lot but their car is parked in the back of a row of cars. In this case, all the cars parked in front of this person s car must be temporarily moved to allow this person to leave.
Adaptive Filter. This script shows the BER performance of several types of equalizers in a static channel with a null in the passband. The script constructs and implements a linear equalizer object and a decision feedback equalizer (DFE) object. It also initializes and invokes a maximum likelihood sequence estimation (MLSE) equalizer. The MLSE equalizer is first invoked with perfect channel knowledge, then with a straightforward but imperfect channel estimation technique.
The algorithms are coded in a way that makes it trivial to apply them to other problems. several generic routines for resampling are provided. The derivation and details are presented in: Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas and Eric Wan. The Unscented Particle Filter. Technical report CUED/F-INFENG/TR 380, Cambridge University Department of Engineering, May 2000. After downloading the file, type "tar -xf upf_demos.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "demo_MC" for the demo.
