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365 results on '"Zhang, Y.D."'

4. Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

Author

Górriz, J.M., Álvarez-Illán, I., Álvarez-Marquina, A., Arco, J.E., Atzmueller, M., Ballarini, F., Barakova, E., Bologna, G., Bonomini, P., Castellanos-Dominguez, G., Castillo-Barnes, D., Cho, S.B., Contreras, R., Cuadra, J.M., Domínguez, E., Domínguez-Mateos, F., Duro, R.J., Elizondo, D., Fernández-Caballero, A., Fernandez-Jover, E., Formoso, M.A., Gallego-Molina, N.J., Gamazo, J., González, J. García, Garcia-Rodriguez, J., Garre, C., Garrigós, J., Gómez-Rodellar, A., Gómez-Vilda, P., Graña, M., Guerrero-Rodriguez, B., Hendrikse, S.C.F., Jimenez-Mesa, C., Jodra-Chuan, M., Julian, V., Kotz, G., Kutt, K., Leming, M., de Lope, J., Macas, B., Marrero-Aguiar, V., Martinez, J.J., Martinez-Murcia, F.J., Martínez-Tomás, R., Mekyska, J., Nalepa, G.J., Novais, P., Orellana, D., Ortiz, A., Palacios-Alonso, D., Palma, J., Pereira, A., Pinacho-Davidson, P., Pinninghoff, M.A., Ponticorvo, M., Psarrou, A., Ramírez, J., Rincón, M., Rodellar-Biarge, V., Rodríguez-Rodríguez, I., Roelofsma, P.H.M.P., Santos, J., Salas-Gonzalez, D., Salcedo-Lagos, P., Segovia, F., Shoeibi, A., Silva, M., Simic, D., Suckling, J., Treur, J., Tsanas, A., Varela, R., Wang, S.H., Wang, W., Zhang, Y.D., Zhu, H., Zhu, Z., and Ferrández-Vicente, J.M.

Published
2023
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27. Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

Author

Universidad de Alicante. Departamento de Tecnología Informática y Computación, Górriz, J.M., Álvarez-Illán, I., Álvarez-Marquina, A., Arco, J.E., Atzmueller, M., Ballarini, F., Barakova, E., Bologna, G., Bonomini, P., Castellanos-Dominguez, G., Castillo-Barnes, D., Cho, S.B., Contreras, R., Cuadra, J.M., Domínguez, E., Domínguez-Mateos, F., Duro, R.J., Elizondo, D., Fernández-Caballero, A., Fernandez-Jover, E., Formoso, M.A., Gallego-Molina, N.J., Gamazo, J., García González, J., Garcia-Rodriguez, Jose, Garre, C., Garrigós, J., Gómez-Rodellar, A., Gómez-Vilda, P., Graña, M., Guerrero-Rodriguez, B., Hendrikse, S.C.F., Jimenez-Mesa, C., Jodra-Chuan, M., Julián, Vicente, Kotz, G., Kutt, K., Leming, M., Lope, J. de, Macas, B., Marrero-Aguiar, V., Martinez, J.J., Martinez-Murcia, F.J., Martínez-Tomás, R., Mekyska, J., Nalepa, G.J., Novais, Paulo, Orellana, D., Ortiz, A., Palacios-Alonso, Daniel, Palma, J., Pereira, A., Pinacho-Davidson, P., Pinninghoff, M.A., Ponticorvo, M., Psarrou, A., Ramírez, J., Rincón, M., Rodellar-Biarge, V., Rodríguez-Rodríguez, I., Roelofsma, P.H.M.P., Santos, J., Salas-Gonzalez, D., Salcedo-Lagos, P., Segovia, F., Shoeibi, A., Silva, M., Simic, D., Suckling, J., Treur, J., Tsanas, A., Varela, R., Wang, S.H., Wang, W., Zhang, Y.D., Zhu, H., Zhu, Z., Ferrández-Vicente, J.M., Universidad de Alicante. Departamento de Tecnología Informática y Computación, Górriz, J.M., Álvarez-Illán, I., Álvarez-Marquina, A., Arco, J.E., Atzmueller, M., Ballarini, F., Barakova, E., Bologna, G., Bonomini, P., Castellanos-Dominguez, G., Castillo-Barnes, D., Cho, S.B., Contreras, R., Cuadra, J.M., Domínguez, E., Domínguez-Mateos, F., Duro, R.J., Elizondo, D., Fernández-Caballero, A., Fernandez-Jover, E., Formoso, M.A., Gallego-Molina, N.J., Gamazo, J., García González, J., Garcia-Rodriguez, Jose, Garre, C., Garrigós, J., Gómez-Rodellar, A., Gómez-Vilda, P., Graña, M., Guerrero-Rodriguez, B., Hendrikse, S.C.F., Jimenez-Mesa, C., Jodra-Chuan, M., Julián, Vicente, Kotz, G., Kutt, K., Leming, M., Lope, J. de, Macas, B., Marrero-Aguiar, V., Martinez, J.J., Martinez-Murcia, F.J., Martínez-Tomás, R., Mekyska, J., Nalepa, G.J., Novais, Paulo, Orellana, D., Ortiz, A., Palacios-Alonso, Daniel, Palma, J., Pereira, A., Pinacho-Davidson, P., Pinninghoff, M.A., Ponticorvo, M., Psarrou, A., Ramírez, J., Rincón, M., Rodellar-Biarge, V., Rodríguez-Rodríguez, I., Roelofsma, P.H.M.P., Santos, J., Salas-Gonzalez, D., Salcedo-Lagos, P., Segovia, F., Shoeibi, A., Silva, M., Simic, D., Suckling, J., Treur, J., Tsanas, A., Varela, R., Wang, S.H., Wang, W., Zhang, Y.D., Zhu, H., Zhu, Z., and Ferrández-Vicente, J.M.

Abstract

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.

Published
2023

30. New design of La(Fe, Co, Si)13magnetocaloric composites using Gd as a binder

Author

Wu, Z., Zhang, H.G., Pan, W.J., Zhang, Y.D., Huang, J.H., Yue, M., Skokov, K.P., and Gutfleisch, O.

Abstract

We propose a new type of magnetocaloric composite that excludes the conventional use of non-magnetocaloric materials as the binder. Spark plasma sintering was adopted to make a fully dense La(Fe, Co, Si)13/Gd composite at low temperatures (450–600 °C), which shows relatively high thermal conductivity and compressive strength compared with the counterparts of other bonded magnetic refrigeration composites. The mechanism of high thermal conductivity and compressive strength were discussed. The diffusion processes between La(Fe, Co, Si)13and Gd were also analyzed, though it has a limited influence on the magnetocaloric effect of the composite. The results suggest that using Gd as the metal binder is promising for improving the performance of present magnetic refrigeration composites. It also provides an important approach for the design of new magnetocaloric materials.

Published
2024
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