Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions

This paper analyzes the use of artificial neural networks (ANNs) for predicting the received power/path loss in both outdoor and indoor links. The approach followed has been a combined use of ANNs and ray-tracing, the latter allowing the identification and parameterization of the so-called dominant path. A complete description of the process for creating and training an ANN-based model is presented with special emphasis on the training process. More specifically, we will be discussing various techniques to arrive at valid predictions focusing on an optimum selection of the training set. A quantitative analysis based on results from two narrowband measurement campaigns, one outdoors and the other indoors, is also presented.

Enlarging a training set for genomic selction by imputation of un-genotyped animals in populations of varying genetic architecture

Background: The most common application of imputation is to infer genotypes of a high-density panel of markers on animals that are genotyped for a low-density panel. However, the increase in accuracy of genomic predictions resulting from an increase in the number of markers tends to reach a plateau beyond a certain density. Another application of imputation is to increase the size of the training set with un-genotyped animals. This strategy can be particularly successful when a set of closely related individuals are genotyped. ----- Methods: Imputation on completely un-genotyped dams was performed using known genotypes from the sire of each dam, one offspring and the offspring’s sire. Two methods were applied based on either allele or haplotype frequencies to infer genotypes at ambiguous loci. Results of these methods and of two available software packages were compared. Quality of imputation under different population structures was assessed. The impact of using imputed dams to enlarge training sets on the accuracy of genomic predictions was evaluated for different populations, heritabilities and sizes of training sets. ----- Results: Imputation accuracy ranged from 0.52 to 0.93 depending on the population structure and the method used. The method that used allele frequencies performed better than the method based on haplotype frequencies. Accuracy of imputation was higher for populations with higher levels of linkage disequilibrium and with larger proportions of markers with more extreme allele frequencies. Inclusion of imputed dams in the training set increased the accuracy of genomic predictions. Gains in accuracy ranged from close to zero to 37.14%, depending on the simulated scenario. Generally, the larger the accuracy already obtained with the genotyped training set, the lower the increase in accuracy achieved by adding imputed dams. ----- Conclusions: Whenever a reference population resembling the family configuration considered here is available, imputation can be used to achieve an extra increase in accuracy of genomic predictions by enlarging the training set with completely un-genotyped dams. This strategy was shown to be particularly useful for populations with lower levels of linkage disequilibrium, for genomic selection on traits with low heritability, and for species or breeds for which the size of the reference population is limited.

Viola-Jones based detectors: How much affects the training set?

[EN]This paper presents a study on the facial feature detection performance achieved using the Viola-Jones framework. A set of classi- ers using two di erent focuses to gather the training samples is created and tested on four di erent datasets covering a wide range of possibili- ties. The results achieved should serve researchers to choose the classi er that better ts their demands.

Transportability guidance : tube-launched, optically-tracked, wire-guided, heavy antitank/assault weapon system (TOW) : guided missile, surface attack, BGM-71-A (in shipping container) (FSN 1410-087-1521), launcher, tubular, guided missile, M220 (FSN 1440-179-4152), carrier, guided missile equipment ... truck ... training set ... shop equipment ... contact support ....

"September 1973."

Operator and organizational maintenance manual for TOW weapon system training set, guided missile system M70 (6920-00-179-7320) and TOW weapon system training set, guided missile system XM-145 (P/N 13060929) : (TOW heavy antitank assault weapon system).

"January 1980."

DS and GS maintenance manual : guided missile system training set M76 (REDEYE Air Defense Guided Missile System).

Includes index.

Operator's manual : M72A1 simulants chemical agent identification training set (NSN 6910-00-106-4800).

"15 July 1985."

Operator's and organizational maintenance manual : radiac training set, AN/TDQ-T1(V) (NSN 6930-01-060-1627).

"February 1978."

Operator and organizational maintenance manual : guided missile system training set M76 (Redeye Air Defense Guided Missile System).

"August 1969."

Operator's and organizational maintenance manual : training set, guided missile system M134, NSN 6920-01-024-6948, (Stinger Air Defense Guided Missile System).

"February 1982."

Organizational, direct support, and general support maintenance repair parts and special tools list (including depot maintenance repair parts and special tools) for training set, guided missile system M76 6920-00-809-0399 (block III) (Redeye Air Defense Guided Missile System).

Includes index.

Operator and organizational maintenance manual for TOW 2 Weapon System training set, guided missile system M70E2, (TOW 2 Heavy Antitank Assault Weapon System).

"May 1983."