Author: claimsbot

1-6. (canceled)
7. A computer-implemented face recognition process for identifying a person depicted in an input image, comprising:
creating means for creating a database of a plurality of model image characterizations, each of which represents the face of a known person that it is desired to identify in the input image as well as the person’s face pose;
training means for training a neural network ensemble to identify a person and their face pose from a region which has been extracted from said input image and characterized in a manner similar to the plurality of model images, wherein the network ensemble comprises, a first stage having a plurality of classifiers each of which has input and output units and is dedicated to a particular pose range and outputs a measure of the similarity indicative of the similarity between said characterized input image region and each of said model image characterizations associated with the particular pose range of the classifier, and a fusing neural network as its second stage which combines the outputs of the classifiers to generate an output indicative of the person associated with the characterized input image region and the face pose of that person and which has at least enough output units to allow a different output to represent each person it is desired to identify at each of the pose ranges, and wherein training the neural network ensemble comprises,
preparing each model image characterization from a model image depicting the face of a known person that it is desired to identify in the input image by,
extracting the portion of the model image depicting said face,
normalizing the extracted portion of the model image by resizing it to a prescribed scale if not already at the prescribed scale and adjusting the region so that the eye locations of the depicted subject fall within a prescribed area, and
cropping the extracted portion of the model image by eliminating unneeded portions of the image not specifically depicting part of the face of the subject to create a model face image,

categorizing the model face images by assigning each to one of a set of pose ranges into which its associated face pose falls, and
for each pose range,
choosing a prescribed number of the model face images of each person being modeled which have been assigned to the selected pose range,
concatenating each of the chosen model face images to create a respective dimensional column vector (DCV) for each,
computing a covariance matrix from the DCVs,
calculating eigenvectors and corresponding eigenvalues from the covariance matrix,
ranking the eigenvalues in descending order,
identifying a prescribed number of the top eigenvalues,
using the eigenvectors corresponding to the identified eigenvalues to form the rows of a basis vector matrix (BVM) for the pose range, and
multiplying each DCV by each BVM to Produce a set of principal components analysis (PCA) coefficient vectors for each model face image, and

for each face recognition neural network, inputting, one at a time, each of the PCA coefficient vectors associated with the pose range of the face recognition neural network into the inputs of the network until the outputs of the network stabilize,
initializing the fusing neural network for training,
for each DCV, simultaneously inputting the PCA coefficient vectors generated from the DCV into the respective face recognition neural network associated the vector’s particular pose range group until all the PCA coefficient vectors of every DCV have been input, and repeating until the outputs of the fusing neural network stabilize, and
for each DCV, simultaneously inputting the PCA coefficient vectors generated from the DCV into the respective face recognition neural network associated the vector’s particular pose range group and assigning the active output of the fusing neural network as corresponding to the particular person and pose associated with the model image used to create the set of PCA coefficient vectors; and

identifying means for employing the network ensemble to identify the person associated with the characterized input image region and the face pose of that person.
8. The process of claim 7, wherein the identifying means comprises:
preparing means for preparing the face region extracted from an input image by normalizing and cropping the extracted regions, wherein said normalizing comprises resizing the extracted face region to the same prescribed scale if not already at the prescribed scale and adjusting the region so that the eye locations of the depicted subject fall within a prescribed area, and wherein the cropping comprises eliminated unneeded portions of the image not specifically depicting part of the face of the subject;
concatenating means for concatenating the prepared face region to create a DCV;
multiplying means for multiplying the DCV by each BVM to produce a set of PCA coefficient vectors for the extracted face region;
inputting means for inputting each PCA coefficient vector in the set of PCA coefficient vectors into the respective face recognition neural network associated that vector’s particular pose range group; and
identifying means for identifying the active unit of the output of the fusing neural network and designating the person and pose previously assigned to that unit as the person and pose associated with the extracted face region.
9-31. (canceled)
32. A computer-implemented face recognition process for identifying a person depicted in an input image:
creating means for creating a database of a plurality of model image characterizations, each of which represents the face of a known person that it is desired to identify in the input image as well as the person’s face pose;
training means for training a neural network ensemble to identify a person and their face pose from a region which has been extracted from said input image and characterized in a manner similar to the plurality of model images, wherein the network ensemble comprises, a first stage having a plurality of classifiers each of which has input and output units and is dedicated to a particular pose range and outputs a measure of the similarity indicative of the similarity between said characterized input image region and each of said model image characterizations associated with the particular pose range of the classifier, and a fusing neural network as its second stage which combines the outputs of the classifiers to generate an output indicative of the person associated with the characterized input image region and the face pose of that person, and wherein training the network ensemble comprises,
deriving each model image characterization from a set of model images of people where each model image of the same person shows that person at a different face pose, said deriving comprising,
extracting the portion of each model image depicting a face,
normalizing the extracted portion of each model image by resizing it to a prescribed scale if not already at the prescribed scale and adjusting the region so that the eye locations of the depicted subject fall within a prescribed area,
cropping the extracted portion of each model image by eliminating unneeded portions of the image not specifically depicting part of the face of the subject to create a model face image,
concatenating each of the model face images to create a respective model dimensional column vector (DCV) for each,
categorizing the model DCVs by assigning each to one of a set of pose ranges into which its associated face pose falls, and
inputting the model DCV of the each model face image falling in a particular pose range, one at a time, to a pre-selected classifier dedicated to the particular pose range,

initializing the fusing neural network for training,
simultaneously inputting the respective DCV of each model face image into all classifiers, until the DCV of every model image has been input, and repeating until the outputs of the neural network stabilize, and
simultaneously inputting the respective DCV of each model face image into all classifiers, and assigning the active output the neural network as corresponding to the particular person and pose associated with the model image used to create the DCV; and

identifying means for employing the network ensemble to identify the person associated with the characterized input image region and the face pose of that person.
33. The process of claim 32, wherein the identifying means comprises:
preparing means for preparing the face region extracted from an input image by normalizing and cropping the extracted regions, wherein said normalizing comprises resizing the extracted face region to the same prescribed scale if not already at the prescribed scale and adjusting the region so that the eye locations of the depicted subject fall within a prescribed area, and wherein the cropping comprises eliminated unneeded portions of the image not specifically depicting part of the face of the subject;
concatenating means for concatenating the prepared face region to create a DCV;
inputting means for inputting the DCV of the face region into all classifiers; and
identifying means for identifying the active output of the neural network and designating the person previously assigned to that unit as the person associated with the extracted face region.
34. The process of claim 33, further comprising specifying means for specifying that the person designated as associated with the extracted face region has the face pose previously assigned to the identified active output.
35-42. (canceled)

The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.

What is claimed is:

1. A method for scanning a mobile wireless device for malware, comprising:
calling a platform abstraction layer of a mobile wireless device; and
initiating an anti-malware scanner-related function module on the mobile wireless device utilizing the platform abstraction layer;
wherein the platform abstraction layer enables the anti-malware scanner-related function module to be initiated on any of a plurality of different mobile wireless devices.
2. The method as recited in claim 1, wherein the platform abstraction layer includes storage IO interfaces.
3. The method as recited in claim 1, wherein the platform abstraction layer includes dynamic memory allocation interfaces.
4. The method as recited in claim 1, wherein the platform abstraction layer includes process control call interfaces.
5. The method as recited in claim 1, wherein the platform abstraction layer includes network IO call interfaces.
6. The method as recited in claim 1, wherein the platform abstraction layer includes system event handler interfaces.
7. The method as recited in claim 1, wherein the platform abstraction layer includes system information routine interfaces.
8. The method as recited in claim 1, wherein the platform abstraction layer includes storage IO interfaces, dynamic memory allocation interfaces, process control call interfaces, network IO call interfaces, system event handler interfaces, and system information routine interfaces.
9. The method as recited in claim 1, wherein the platform abstraction layer provides a common view of an operating system of the mobile wireless device.
10. A computer program product for scanning a mobile wireless device for malware, comprising:
computer code for calling a platform abstraction layer of a mobile wireless device; and
computer code for initiating an anti-malware scanner-related function module on the mobile wireless device utilizing the platform abstraction layer;
wherein the platform abstraction layer enables the anti-malware scanner-related function module to be initiated on any of a plurality of different mobile wireless devices.
11. The computer program product as recited in claim 10, wherein the platform abstraction layer includes storage IO interfaces.
12. The computer program product as recited in claim 10, wherein the platform abstraction layer includes dynamic memory allocation interfaces.
13. The computer program product as recited in claim 10, wherein the platform abstraction layer includes process control calls.
14. The computer program product as recited in claim 10, wherein the platform abstraction layer includes network IO calls.
15. The computer program product as recited in claim 10, wherein the platform abstraction layer includes system event handler interfaces.
16. The computer program product as recited in claim 10, wherein the platform abstraction layer includes system information routines.
17. The computer program product as recited in claim 10, wherein the platform abstraction layer includes storage IO interfaces, dynamic memory allocation interfaces, process control calls, network IO calls, system event handler interfaces, and system information routines.
18. The computer program product as recited in claim 10, wherein the platform abstraction layer provides a common view of an operating system of the mobile wireless device.
19. A system for scanning a mobile wireless device for malware, comprising:
logic for calling a platform abstraction layer of a mobile wireless device; and
logic for initiating an anti-malware scanner-related function module on the mobile wireless device utilizing the platform abstraction layer;
wherein the platform abstraction layer enables the anti-malware scanner-related function module to be initiated on any of a plurality of different mobile wireless devices.
20. A system for scanning a mobile wireless device for malware, comprising:
means for calling a platform abstraction layer of a mobile wireless device; and
means for initiating an anti-malware scanner-related function module on the mobile wireless device utilizing the platform abstraction layer;
wherein the platform abstraction layer enables the anti-malware scanner-related function module to be initiated on any of a plurality of different mobile wireless devices.
21. A method for scanning a mobile wireless device for malware, comprising:
calling a platform abstraction layer of a mobile wireless device; and
initiating an anti-malware scanner-related function module on the mobile wireless device utilizing the platform abstraction layer;
wherein the platform abstraction layer includes entities selected from the group consisting of storage IO interfaces, dynamic memory allocation interfaces, process control call interfaces, network IO call interfaces, system event handler interfaces, and system information routine interfaces.

1. A wood adhesive composition obtained by simultaneous mixing at least one isocyanate, at least one polyether and at least one amino resin, wherein said polyether comprises at least one ethylene oxide moiety and at least one isocyanate reactive group selected from the group comprising hydroxyl, amino, epoxy, and thiol, and wherein said amino resin is the condensation product of an aldehyde with a compound selected from the group comprising urea, melamine, benzoguanamine, glycoluril, acetoguanamine and mixtures thereof.
2. The composition according to claim 1, wherein said amino resin is the condensation product of a compound as defined in claim 1 with an aldehyde selected from the group comprising formaldehyde, acetaldehyde, crotonaldehyde, acrolein, benzaldehyde, and furfural.
3. The composition according to claim 1, wherein the composition has an ethylene oxide content of at least 1% by weight based on 100% by weight of the at least one isocyanate and the at least one polyether combined.
4. The composition according to claim 1, wherein the at least one polyether comprises the reaction product obtained by the polymerization of ethylene oxide or the reaction product obtained by the copolymerisation of ethylene oxide with at least one other cyclic oxide, in the presence of at least one polyfunctional initiator selected from the group comprising ethylene glycol, propylene glycol, diethylene glycol, dipropylene glycol, cyclohexane dimethanol, resorcinol, bisphenol A, glycerol, trimethylolopropane, 1,2,6-hexantriol, pentaerythritol and mixtures thereof.
5. The composition according to claim 1, wherein the at least one polyether has a weight average molecular weight ranging from 62 to 40000.
6. The composition according to claim 1, wherein the at least one isocyanate is selected from the group comprising hexamethylene diisocyanate, m- and p-phenylene diisocyanate, tolylene-2,4- and tolylene-2,6-diisocyanate, diphenylmethane diisocyanate in the form of its 2,4\u2032-, 2,2\u2032- and 4,4\u2032-isomers and mixtures thereof, the mixtures of diphenylinethane diisocyanates and oligomers thereof, chlorophenylene-2,4-diisocyanate, naphthylene-1,5-diisocyanate, diphenylene-4,4\u2032-diisocyanate, 4,4\u2032-diisocyanate-3,3\u2032-dimethyl-diphenyl, 3-methyl-diphenylmethane-4,4\u2032-diisocyanate, diphenyl ether diisocyanate, cyclohexane-2,4- and -2,3-diisocyanate, 1-methylcyclohexyl-2,4- and -2,6-diisocyanate, bis-(isocyana-tocyclohexyl)methane, 2,4,6-triisocyanatotoluene, 2,4,4-triisocyanatodiphenylether, isophorone diisocyanate, butylene diisocyanate, trimethylhexamethylene diisocyanate, isocyanatomethyl-1,8-octane diisocyanate, tetramethylxylene diisocyanate, 1,4-cyclohexanediisocyanate, tolidine diisocyanate, and mixtures thereof.
7. The composition according to claim 1, wherein the at least one amino resin is present in an amount of at least 30% by weight based on 100% by weight of the total composition.
8. The composition according to claim 1, wherein the at least one isocyanate is present in an amount of at least 0.5% by weight based on 100% by weight of the total composition.
9. The composition according to claim 1, wherein the at least one polyether is present in an amount of at least 0.01% by weight based on 100% by weight of the total composition.
10. The composition according to claim 1, wherein the composition comprises at least one additive selected from the group comprising a hardener, a surfactant, a release agent, a wax, and a pigment.
11. (canceled)
12. (canceled)
13. (canceled)
14. A method for preparing a composition according to claim 1, comprising the step of mixing at least one amino resin, at least one polyether and at least one isocyanate as described in claim 1, thereby obtaining a composition according to claim 1.
15. A composition according to claim 4, wherein the one other cyclic oxide is propylene oxide.

The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.

1. A combined cycle power generating plant, comprising:
a gas turbine power generating section that is provided with a gas turbine;
a steam turbine power generating section that is provided with a steam turbine;
a boiler that supplies steam to the gas turbine and the steam turbine;
a steam supplying section that supplies steam to the gas turbine;
a first steam pipe that directs steam from the steam supplying section to the gas turbine;
a second steam pipe that directs steam from the gas turbine to the steam turbine;
a first release section that carries out control such that the supply destination of the steam that is directed to the first steam pipe is to one of either the gas turbine or an outside of the combined cycle power generating plant;
a second release section that carries out control such that the supply destination of the steam that is directed to the second steam pipe is to one of either the steam turbine or an outside of the combined cycle power generating plant; and
a bypass pipe that directs at least a portion of the steam inside the first steam pipe to the second steam pipe between the gas turbine and the second release section,
wherein the first release section carries out control such that the supply destination of the steam that is directed to the first steam pipe is to the outside of the combined cycle power generating plant when the combined cycle power generating plant starts up, and
wherein the second release section carries out control such that the supply destination of the steam that is directed from the first steam pipe to the second steam pipe via the bypass pipe is to the outside of the combined cycle power generating plant when the combined cycle power generating plant starts up.
2. The combined cycle power generating plant according to claim 1, wherein the bypass pipe directs steam from the first steam pipe between the steam supplying section and the first release section.
3. The combined cycle power generating plant according to claim 2, wherein a separating section that separates steam and condensed water is provided on the bypass pipe.
4. The combined cycle power generating plant according to claim 1, wherein the bypass pipe directs steam that has been released to the outside of the first steam pipe by the first release section to the second steam pipe.
5. The combined cycle power generating plant according to claim 4, wherein a separating section that separates steam and condensed water is provided on the bypass pipe.
6. The combined cycle power generating plant according to claim 1, further comprising:
a condenser that is provided on the steam turbine power generating section and into which steam that has driven the steam turbine is channeled; and
a third release section that carries out control such that the supply destination of steam that is directed to the second steam pipe is to one of either the steam turbine or the condenser.
7. The combined cycle power generating plant according to claim 1, further comprising:
a first control section that controls the channeling of steam from the first steam pipe into the gas turbine;
a second control section that controls the channeling of steam out of the gas turbine into the second steam pipe; and
a fluid supply section that introduces a fluid that cools the gas turbine to the first steam pipe between the first control section and the gas turbine.
8. The combined cycle power generating plant according to claim 1, wherein the boiler is a heat recovery steam generator that generates steam by using the exhaust heat of the gas turbine power generating section.
9. The combined cycle power generating plant according to claim 1, wherein the steam supplying section is another boiler.
10. The combined cycle power generating plant according to claim 1, wherein the steam supplying section is an auxiliary boiler.
11. The combined cycle power generating plant according to claim 1, wherein the steam supplying section is another heat recovery steam generator that generates steam by using the exhaust heat of a separate gas turbine power generating section.