Patent
Atlas literature
Patent
US 12,442,951 B2Patent drawings and their descriptions. Click a drawing to enlarge it.
FIG. 1 is a schematic block diagram of a regional pre- cipitation nowcasting system based on cycle-GAN exten- sion according to an embodiment of the present …
FIG. 2 is a schematic diagram illustrating learning of a cycle-GAN according to the present invention. For clear and simple description, forward generators …
FIG. 3 is a schematic diagram illustrating generation of composite hybrid surface rainfall (HSR) data according to the present invention. 30
FIG. 4. The generator network 410 includes an encoder 421, a plurality of squeeze-and-excitation (SE)-residual blocks 423, and a decoder 424. The encoder 421 …
FIG. 5 is a block diagram illustrating a network architec- ture of a forward discriminator and a backward discrimina- tor according to the present invention. …
FIG. 6 is a schematic flowchart of a regional precipitation nowcasting method based on cycle-GAN extension accord- ing to an embodiment of the present …
FIG. 7, the present invention is compared with two conventional models for qualitative evaluation. The first model is a McGill algorithm for precipitation …
FIG. 8, peak signal-to-noise ratio (PSNR) 5 and structural similarity index measure (SSIM) are used as metrics for quantitative evaluation. As shown in Table …
FIG. 9. The computing device 900 may include at least one processor 910, a bus 950, a communication interface 970, a memory 930 for loading a computer program …
Claims define the patent's legal scope. Independent claims stand alone; dependent claims (nested) narrow them. Click a claim to expand its dependents.
A regional precipitation nowcasting system based on cycle-generative adversarial network (GAN) extension, the system comprising: an input processor configured to receive an input com-posite hybrid surface rainfall (HSR) image including precipitation information of a region of interest corre-sponding to a first time; a cycle-GAN configured to generate a resultant composite HSR image including precipitation information of the region of interest corresponding to a second time which is later than the first time, based on the input composite HSR image, wherein the cycle-GAN comprises a first cycle-GAN and a second cycle-GAN which is complementary to the first cycle-GAN, and wherein the first cycle-GAN and the second cycle-GAN are configured to perform forward image map-ping, which temporally goes forward, and backward image mapping, which temporally goes backward, respectively; and an output processor configured to output the resultant composite HSR image as a nowcasting image of the region of interest.
The regional precipitation nowcasting system of claim 1, wherein the first cycle-GAN comprises: a forward generator configured to learn predictive map-ping of a first predictive composite HSR image of the second time based on the input composite HSR image of the first time; a backward generator configured to learn predictive map-ping of a first cycle predictive composite HSR image of the first time based on the first predictive composite HSR image of the second time; a forward discriminator configured to evaluate an accu-racy of the predictive mapping of the forward generator and discriminate between the input composite HSR image and the first predictive composite HSR image; and a backward discriminator configured to evaluate an accu-racy of the predictive mapping of the backward gen-erator and discriminate between the first predictive composite HSR image and the first cycle predictive composite HSR image.
A regional precipitation nowcasting method based on cycle-generative adversarial network (GAN) extension, the method comprising: receiving an input composite hybrid surface rainfall (HSR) image including precipitation information of a region of interest corresponding to a first time; generating, by a cycle-GAN, a resultant composite HSR image including precipitation information of the region of interest corresponding to a second time which is later than the first time, based on the input composite HSR image, wherein the cycle-GAN comprises a first cycle-GAN and a second cycle-GAN which is complementary to the first cycle-GAN, and wherein the first cycle-GAN and the second cycle-GAN are configured to perform forward image map-ping, which temporally goes forward, and backward image mapping, which temporally goes backward, respectively; and outputting the resultant composite HSR image as a now-casting image of the region of interest.
The regional precipitation nowcasting method of claim 9, wherein the first cycle-GAN is configured to perform: learning predictive mapping of a first predictive compos-ite HSR image of the second time based on the input composite HSR image of the first time using a forward generator; learning predictive mapping of a first cycle predictive composite HSR image of the first time based on the first predictive composite HSR image of the first time based on the first predictive composite HSR image of the second time using a backward generator; evaluating an accuracy of the predictive mapping of the forward generator and discriminating between the input composite HSR image and the first predictive compos-ite HSR image using a forward discriminator; and evaluating an accuracy of the predictive mapping of the backward generator and discriminating between the first predictive composite HSR image and the first cycle predictive composite HSR image using a backward discriminator.
Layer stacks claimed or described, ordered top of device to substrate.
regional precipitation nowcasting system based on cycle-GAN extension
No layer stack recorded.
Patents and literature cited by this patent (applicant and examiner references).
Cited patents · 11
Patent
Atlas literature
Patent
US 12,442,951 B2Patent drawings and their descriptions. Click a drawing to enlarge it.
FIG. 1 is a schematic block diagram of a regional pre- cipitation nowcasting system based on cycle-GAN exten- sion according to an embodiment of the present …
FIG. 2 is a schematic diagram illustrating learning of a cycle-GAN according to the present invention. For clear and simple description, forward generators …
FIG. 3 is a schematic diagram illustrating generation of composite hybrid surface rainfall (HSR) data according to the present invention. 30
FIG. 4. The generator network 410 includes an encoder 421, a plurality of squeeze-and-excitation (SE)-residual blocks 423, and a decoder 424. The encoder 421 …
FIG. 5 is a block diagram illustrating a network architec- ture of a forward discriminator and a backward discrimina- tor according to the present invention. …
FIG. 6 is a schematic flowchart of a regional precipitation nowcasting method based on cycle-GAN extension accord- ing to an embodiment of the present …
FIG. 7, the present invention is compared with two conventional models for qualitative evaluation. The first model is a McGill algorithm for precipitation …
FIG. 8, peak signal-to-noise ratio (PSNR) 5 and structural similarity index measure (SSIM) are used as metrics for quantitative evaluation. As shown in Table …
FIG. 9. The computing device 900 may include at least one processor 910, a bus 950, a communication interface 970, a memory 930 for loading a computer program …
Claims define the patent's legal scope. Independent claims stand alone; dependent claims (nested) narrow them. Click a claim to expand its dependents.
A regional precipitation nowcasting system based on cycle-generative adversarial network (GAN) extension, the system comprising: an input processor configured to receive an input com-posite hybrid surface rainfall (HSR) image including precipitation information of a region of interest corre-sponding to a first time; a cycle-GAN configured to generate a resultant composite HSR image including precipitation information of the region of interest corresponding to a second time which is later than the first time, based on the input composite HSR image, wherein the cycle-GAN comprises a first cycle-GAN and a second cycle-GAN which is complementary to the first cycle-GAN, and wherein the first cycle-GAN and the second cycle-GAN are configured to perform forward image map-ping, which temporally goes forward, and backward image mapping, which temporally goes backward, respectively; and an output processor configured to output the resultant composite HSR image as a nowcasting image of the region of interest.
The regional precipitation nowcasting system of claim 1, wherein the first cycle-GAN comprises: a forward generator configured to learn predictive map-ping of a first predictive composite HSR image of the second time based on the input composite HSR image of the first time; a backward generator configured to learn predictive map-ping of a first cycle predictive composite HSR image of the first time based on the first predictive composite HSR image of the second time; a forward discriminator configured to evaluate an accu-racy of the predictive mapping of the forward generator and discriminate between the input composite HSR image and the first predictive composite HSR image; and a backward discriminator configured to evaluate an accu-racy of the predictive mapping of the backward gen-erator and discriminate between the first predictive composite HSR image and the first cycle predictive composite HSR image.
A regional precipitation nowcasting method based on cycle-generative adversarial network (GAN) extension, the method comprising: receiving an input composite hybrid surface rainfall (HSR) image including precipitation information of a region of interest corresponding to a first time; generating, by a cycle-GAN, a resultant composite HSR image including precipitation information of the region of interest corresponding to a second time which is later than the first time, based on the input composite HSR image, wherein the cycle-GAN comprises a first cycle-GAN and a second cycle-GAN which is complementary to the first cycle-GAN, and wherein the first cycle-GAN and the second cycle-GAN are configured to perform forward image map-ping, which temporally goes forward, and backward image mapping, which temporally goes backward, respectively; and outputting the resultant composite HSR image as a now-casting image of the region of interest.
The regional precipitation nowcasting method of claim 9, wherein the first cycle-GAN is configured to perform: learning predictive mapping of a first predictive compos-ite HSR image of the second time based on the input composite HSR image of the first time using a forward generator; learning predictive mapping of a first cycle predictive composite HSR image of the first time based on the first predictive composite HSR image of the first time based on the first predictive composite HSR image of the second time using a backward generator; evaluating an accuracy of the predictive mapping of the forward generator and discriminating between the input composite HSR image and the first predictive compos-ite HSR image using a forward discriminator; and evaluating an accuracy of the predictive mapping of the backward generator and discriminating between the first predictive composite HSR image and the first cycle predictive composite HSR image using a backward discriminator.
Layer stacks claimed or described, ordered top of device to substrate.
regional precipitation nowcasting system based on cycle-GAN extension
No layer stack recorded.
Patents and literature cited by this patent (applicant and examiner references).
Cited patents · 11
Patent
Atlas literature
Patent
US 12,442,951 B2Patent drawings and their descriptions. Click a drawing to enlarge it.
FIG. 1 is a schematic block diagram of a regional pre- cipitation nowcasting system based on cycle-GAN exten- sion according to an embodiment of the present …
FIG. 2 is a schematic diagram illustrating learning of a cycle-GAN according to the present invention. For clear and simple description, forward generators …
FIG. 3 is a schematic diagram illustrating generation of composite hybrid surface rainfall (HSR) data according to the present invention. 30
FIG. 4. The generator network 410 includes an encoder 421, a plurality of squeeze-and-excitation (SE)-residual blocks 423, and a decoder 424. The encoder 421 …
FIG. 5 is a block diagram illustrating a network architec- ture of a forward discriminator and a backward discrimina- tor according to the present invention. …
FIG. 6 is a schematic flowchart of a regional precipitation nowcasting method based on cycle-GAN extension accord- ing to an embodiment of the present …
FIG. 7, the present invention is compared with two conventional models for qualitative evaluation. The first model is a McGill algorithm for precipitation …
FIG. 8, peak signal-to-noise ratio (PSNR) 5 and structural similarity index measure (SSIM) are used as metrics for quantitative evaluation. As shown in Table …
FIG. 9. The computing device 900 may include at least one processor 910, a bus 950, a communication interface 970, a memory 930 for loading a computer program …
Claims define the patent's legal scope. Independent claims stand alone; dependent claims (nested) narrow them. Click a claim to expand its dependents.
A regional precipitation nowcasting system based on cycle-generative adversarial network (GAN) extension, the system comprising: an input processor configured to receive an input com-posite hybrid surface rainfall (HSR) image including precipitation information of a region of interest corre-sponding to a first time; a cycle-GAN configured to generate a resultant composite HSR image including precipitation information of the region of interest corresponding to a second time which is later than the first time, based on the input composite HSR image, wherein the cycle-GAN comprises a first cycle-GAN and a second cycle-GAN which is complementary to the first cycle-GAN, and wherein the first cycle-GAN and the second cycle-GAN are configured to perform forward image map-ping, which temporally goes forward, and backward image mapping, which temporally goes backward, respectively; and an output processor configured to output the resultant composite HSR image as a nowcasting image of the region of interest.
The regional precipitation nowcasting system of claim 1, wherein the first cycle-GAN comprises: a forward generator configured to learn predictive map-ping of a first predictive composite HSR image of the second time based on the input composite HSR image of the first time; a backward generator configured to learn predictive map-ping of a first cycle predictive composite HSR image of the first time based on the first predictive composite HSR image of the second time; a forward discriminator configured to evaluate an accu-racy of the predictive mapping of the forward generator and discriminate between the input composite HSR image and the first predictive composite HSR image; and a backward discriminator configured to evaluate an accu-racy of the predictive mapping of the backward gen-erator and discriminate between the first predictive composite HSR image and the first cycle predictive composite HSR image.
A regional precipitation nowcasting method based on cycle-generative adversarial network (GAN) extension, the method comprising: receiving an input composite hybrid surface rainfall (HSR) image including precipitation information of a region of interest corresponding to a first time; generating, by a cycle-GAN, a resultant composite HSR image including precipitation information of the region of interest corresponding to a second time which is later than the first time, based on the input composite HSR image, wherein the cycle-GAN comprises a first cycle-GAN and a second cycle-GAN which is complementary to the first cycle-GAN, and wherein the first cycle-GAN and the second cycle-GAN are configured to perform forward image map-ping, which temporally goes forward, and backward image mapping, which temporally goes backward, respectively; and outputting the resultant composite HSR image as a now-casting image of the region of interest.
The regional precipitation nowcasting method of claim 9, wherein the first cycle-GAN is configured to perform: learning predictive mapping of a first predictive compos-ite HSR image of the second time based on the input composite HSR image of the first time using a forward generator; learning predictive mapping of a first cycle predictive composite HSR image of the first time based on the first predictive composite HSR image of the first time based on the first predictive composite HSR image of the second time using a backward generator; evaluating an accuracy of the predictive mapping of the forward generator and discriminating between the input composite HSR image and the first predictive compos-ite HSR image using a forward discriminator; and evaluating an accuracy of the predictive mapping of the backward generator and discriminating between the first predictive composite HSR image and the first cycle predictive composite HSR image using a backward discriminator.
Layer stacks claimed or described, ordered top of device to substrate.
regional precipitation nowcasting system based on cycle-GAN extension
No layer stack recorded.
Patents and literature cited by this patent (applicant and examiner references).
Cited patents · 11
Patent
Atlas literature
Patent
US 12,442,951 B2Patent drawings and their descriptions. Click a drawing to enlarge it.
FIG. 1 is a schematic block diagram of a regional pre- cipitation nowcasting system based on cycle-GAN exten- sion according to an embodiment of the present …
FIG. 2 is a schematic diagram illustrating learning of a cycle-GAN according to the present invention. For clear and simple description, forward generators …
FIG. 3 is a schematic diagram illustrating generation of composite hybrid surface rainfall (HSR) data according to the present invention. 30
FIG. 4. The generator network 410 includes an encoder 421, a plurality of squeeze-and-excitation (SE)-residual blocks 423, and a decoder 424. The encoder 421 …
FIG. 5 is a block diagram illustrating a network architec- ture of a forward discriminator and a backward discrimina- tor according to the present invention. …
FIG. 6 is a schematic flowchart of a regional precipitation nowcasting method based on cycle-GAN extension accord- ing to an embodiment of the present …
FIG. 7, the present invention is compared with two conventional models for qualitative evaluation. The first model is a McGill algorithm for precipitation …
FIG. 8, peak signal-to-noise ratio (PSNR) 5 and structural similarity index measure (SSIM) are used as metrics for quantitative evaluation. As shown in Table …
FIG. 9. The computing device 900 may include at least one processor 910, a bus 950, a communication interface 970, a memory 930 for loading a computer program …
Claims define the patent's legal scope. Independent claims stand alone; dependent claims (nested) narrow them. Click a claim to expand its dependents.
A regional precipitation nowcasting system based on cycle-generative adversarial network (GAN) extension, the system comprising: an input processor configured to receive an input com-posite hybrid surface rainfall (HSR) image including precipitation information of a region of interest corre-sponding to a first time; a cycle-GAN configured to generate a resultant composite HSR image including precipitation information of the region of interest corresponding to a second time which is later than the first time, based on the input composite HSR image, wherein the cycle-GAN comprises a first cycle-GAN and a second cycle-GAN which is complementary to the first cycle-GAN, and wherein the first cycle-GAN and the second cycle-GAN are configured to perform forward image map-ping, which temporally goes forward, and backward image mapping, which temporally goes backward, respectively; and an output processor configured to output the resultant composite HSR image as a nowcasting image of the region of interest.
The regional precipitation nowcasting system of claim 1, wherein the first cycle-GAN comprises: a forward generator configured to learn predictive map-ping of a first predictive composite HSR image of the second time based on the input composite HSR image of the first time; a backward generator configured to learn predictive map-ping of a first cycle predictive composite HSR image of the first time based on the first predictive composite HSR image of the second time; a forward discriminator configured to evaluate an accu-racy of the predictive mapping of the forward generator and discriminate between the input composite HSR image and the first predictive composite HSR image; and a backward discriminator configured to evaluate an accu-racy of the predictive mapping of the backward gen-erator and discriminate between the first predictive composite HSR image and the first cycle predictive composite HSR image.
A regional precipitation nowcasting method based on cycle-generative adversarial network (GAN) extension, the method comprising: receiving an input composite hybrid surface rainfall (HSR) image including precipitation information of a region of interest corresponding to a first time; generating, by a cycle-GAN, a resultant composite HSR image including precipitation information of the region of interest corresponding to a second time which is later than the first time, based on the input composite HSR image, wherein the cycle-GAN comprises a first cycle-GAN and a second cycle-GAN which is complementary to the first cycle-GAN, and wherein the first cycle-GAN and the second cycle-GAN are configured to perform forward image map-ping, which temporally goes forward, and backward image mapping, which temporally goes backward, respectively; and outputting the resultant composite HSR image as a now-casting image of the region of interest.
The regional precipitation nowcasting method of claim 9, wherein the first cycle-GAN is configured to perform: learning predictive mapping of a first predictive compos-ite HSR image of the second time based on the input composite HSR image of the first time using a forward generator; learning predictive mapping of a first cycle predictive composite HSR image of the first time based on the first predictive composite HSR image of the first time based on the first predictive composite HSR image of the second time using a backward generator; evaluating an accuracy of the predictive mapping of the forward generator and discriminating between the input composite HSR image and the first predictive compos-ite HSR image using a forward discriminator; and evaluating an accuracy of the predictive mapping of the backward generator and discriminating between the first predictive composite HSR image and the first cycle predictive composite HSR image using a backward discriminator.
Layer stacks claimed or described, ordered top of device to substrate.
regional precipitation nowcasting system based on cycle-GAN extension
No layer stack recorded.
Patents and literature cited by this patent (applicant and examiner references).
Cited patents · 11
Cited non-patent literature · 5
Cited non-patent literature · 5
Cited non-patent literature · 5
Cited non-patent literature · 5
