Results 91 to 100 of about 403,268 (198)

Adaptive learning for disruption prediction in non-stationary conditions

Nuclear Fusion, 2019
For many years, machine learning tools have proved to be very powerful disruption predictors in tokamaks. On the other hand, the vast majority of the techniques deployed assume that the input data is independent and is sampled from exactly the same probability distribution for the training set, the test set and the final real time deployment.
Murari, A., Lungaroni, M., Gelfusa, M., Peluso, E., Vega, J., Abduallev, S., Abhangi, M., Abreu, P., Afanasev, V, Afzal, M., Aggarwal, K. M., Ahlgren, T., Aho-Mantila, L., Aiba, N., Airila, M., Alarcon, T., Albanese, R., Alegre, D., Aleiferis, S., Alessi, E., Aleynikov, P., Alkseev, A., Allinson, M., Alper, B., Alves, E., Ambrosino, G., Ambrosino, R., Amosov, V, Sunden, E. Andersson, Andrews, R., Angelone, M., Anghel, M., Angioni, C., Appel, L., Appelbee, C., Arena, P., Ariola, M., Arshad, S., Artaud, J., Arter, W., Ash, A., Ashikawa, N., Aslanyan, V, Asunta, O., Asztalos, O., Auriemma, F., Austin, Y., Avotina, L., Axton, M., Ayres, C., Baciero, A., Baiao, D., Balboa, I, Balden, M., Balshaw, N., Bandaru, V. K., Banks, J., Baranov, Y. F., Barcellona, C., Barnard, T., Barnes, M., Barnsley, R., Wiechec, A. Baron, Orte, L. Barrera, Baruzzo, M., Basiuk, V, Bassan, M., Bastow, R., Batista, A., Batistoni, P., Baumane, L., Bauvir, B., Baylor, L., Beaumont, P. S., Beckers, M., Beckett, B., Bekris, N., Beldishevski, M., Bell, K., Belli, F., Belonohy, E., Benayas, J., Bergsaker, H., Bernardo, J., Bernert, M., Berry, M., Bertalot, L., Besiliu, C., Betar, H., Beurskens, M., Bielecki, J., Biewer, T., Bilato, R., Biletskyi, O., Bilkova, P., Binda, F., Birkenmeier, G., Bizarro, J. P. S., Bjorkas, C., Blackburn, J., Blackman, T. R., Blanchard, P., Blatchford, P., Bobkov, V, Boboc, A., Bogar, O., Bohm, P., Bohm, T., Bolshakova, I, Bolzonella, T., Bonanomi, N., Boncagni, L., Bonfiglio, D., Bonnin, X., Boom, J., Borba, D., Borodin, D., Borodkina, I, Boulbe, C., Bourdelle, C., Bowden, M., Bowman, C., Boyce, T., Boyer, H., Bradnam, S. C., Braic, V, Bravanec, R., Breizman, B., Brennan, D., Breton, S., Brett, A., Brezinsek, S., Bright, M., Brix, M., Broeckx, W., Brombin, M., Broslawski, A., Brown, B., Brunetti, D., Bruno, E., Buch, J., Buchanan, J., Buckingham, R., Buckley, M., Bucolo, M., Budny, R., Bufferand, H., Buller, S., Bunting, P., Buratti, P., Burckhart, A., Burroughes, G., Buscarino, A., Busse, A., Butcher, D., Butler, B., Bykov, I, Cahyna, P., Calabro, G., Calacci, L., Callaghan, D., Callaghan, J., Calvo, I, Camenen, Y., Camp, P., Campling, D. C., Cannas, B., Capat, A., Carcangiu, S., Card, P., Cardinali, A., Carman, P., Carnevale, D., Carr, M., Carralero, D., Carraro, L., Carvalho, B. B., Carvalho, I, Carvalho, P., Carvalho, D. D., Casson, F. J., Castaldo, C., Catarino, N., Causa, F., Cavazzana, R., Cave-Ayland, K., Cavedon, M., Cecconello, M., Ceccuzzi, S., Cecil, E., Challis, C. D., Chandra, D., Chang, C. S., Chankin, A., Chapman, I. T., Chapman, B., Chapman, S. C., Chernyshova, M., Chiariello, A., Chitarin, G., Dal Molin, A., Dreval, M., Enachescu, M., Fanni, A., Formisano, A., Fresa, R., Kos, B., Leichtle, D., Loarte, A., Macusova, E., Marchetto, C., Marconato, N., Martone, R., Matejcik, S., Mattei, M., Nordlund, K., Pavlichenko, R., Piron, L., Plyusnin, V, Predebon, I, Ratta, G., Refy, D., Romanelli, F., Safi, E., Sauter, O., Soare, S., Sonato, P., Subba, F., Tokitani, M., Vicente, J., Wischmeier, M.   +230 more
openaire   +8 more sources

The traveler costs of unplanned transport network disruptions: An activity-based modeling approach [PDF]

In this paper we introduce an activity-based modeling approach for evaluating the traveler costs of transport network disruptions. The model handles several important aspects of such events: increases in travel time may be very long in relation to the ...
David Levinson, Erik Jenelius, Lars-Goran Mattsson   +2 more
core  

Correlation between General Health with Life Expectancy and Quality of Life in Patients with leukemia

Majallah-i Dānishgāh-i ̒Ulūm-i Pizishkī-i Qum, 2017
Background and Objectives: Cancer is one of the most important diseases of the present century and the second cause of death. This study aimed to determine the correlation between general health with life expectancy and quality of life in Patients with ...
Farhad Kahrazee, Behzad Rigi Kooteh, Amir Akbari Esfahani   +2 more
doaj  

Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction. [PDF]

Environ Sci Technol, 2020
Zorn KM, Foil DH, Lane TR, Russo DP, Hillwalker W, Feifarek DJ, Jones F, Klaren WD, Brinkman AM, Ekins S.   +9 more
europepmc   +1 more source

Network disruption prediction based on neural networks

Collection of selected papers of the III International Conference on Information Technology and Nanotechnology, 2017
Network disruptions cause significant financial losses and discomfort of customers. However, communication systems provide various data about equipment condition. This information can be used to predict network disruptions. Purpose of research is applying neural networks to network disruption prediction.
openaire   +1 more source

Disruption prediction at JET [Joint European Torus] [PDF]

, 1998
The sudden loss of the plasma magnetic confinement, known as disruption, is one of the major issue in a nuclear fusion machine as JET (Joint European Torus), Disruptions pose very serious problems to the safety of the machine. The energy stored in the plasma is released to the machine structure in few milliseconds resulting in forces that at JET reach ...
openaire   +1 more source

A systematic literature review of diabetes prediction using metaheuristic algorithm-based feature selection: Algorithms and challenges method

Applied Computer Science
Diabetes is a disruption in metabolism that leads to elevated levels of glucose in the bloodstream and causes many other problems, such as stroke, kidney failure, heart, and nerve issues that are of serious concern globally.
Sirmayanti, Pulung Hendro PRASTYO, Mahyati, Farhan RAHMAN   +3 more
doaj   +1 more source

Different effects of verbal and visual working memory loads on Language prediction

Scientific Reports
Mounting studies suggest that working memory (WM) plays a crucial role in language prediction, but how varying types of WM loads influence language prediction remains unclear.
Shun Liu, Wenpeng Hu, Xiqin Liu
doaj   +1 more source

The effect of COVID-19 pandemic on emotional wellbeing of education instructors: A perspective of Kenya’s private schools

Journal of Educational Management and Instruction
The COVID-19 pandemic phenomenon generated inordinate strain and experiences across a wide range of sectors in Kenya, with the education segment introduced to its own set of unique challenges.
Hillary Busolo, Caren Jerop, Carolyne Omulando   +2 more
doaj   +1 more source

Enhancing disruption prediction through Bayesian neural network in KSTAR

Plasma Physics and Controlled Fusion
Abstract In this research, we develop a data-driven disruption predictor based on Bayesian deep probabilistic learning, capable of predicting disruptions and modeling uncertainty in KSTAR. Unlike conventional neural networks within a frequentist approach, Bayesian neural networks can quantify the uncertainty associated with their ...
Jinsu Kim, Jeongwon Lee, Jaemin Seo, Young-Chul Ghim, Yeongsun Lee, Yong-Su Na   +5 more
openaire   +2 more sources