| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNINA9910585937403321 |
|
|
Autore |
Peng Kuan-Chuan |
|
|
Titolo |
AAAI Workshop on Artificial Intelligence with Biased or Scarce Data (AIBSD) / / Kuan-Chuan Peng, Ziyan Wu |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Basel : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2022 |
|
|
|
|
|
|
|
Descrizione fisica |
|
1 electronic resource (186 p.) |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Technology: general issues |
Artificial intelligence |
History of engineering & technology |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Nota di contenuto |
|
About the Editors -- Statement of Peer Review -- Electricity Consumption Forecasting for Out-of-Distribution Time-of-Use Tariffs -- Measuring Embedded Human-Like Biases in Face Recognition Models -- Measuring Gender Bias in Contextualized Embeddings -- The Details Matter: Preventing Class Collapsein Supervised Contrastive Learning -- DAP-SDD: Distribution-Aware Pseudo Labeling for Small Defect Detection -- Quantifying Bias in a Face -- Verification System -- Super-Resolution for Brain MR Images from a Significantly Small Amount of Training Data -- Dual Complementary Prototype Learning for Few-Shot Segmentation -- Extracting Salient Facts from Company Reviews with Scarce Labels -- Long-Tail Zero and Few-Shot Learning via Contrastive Pretraining on and for Small Data -- Age Should Not Matter: -- Towards More Accurate Pedestrian Detection via Self-Training. |
|
|
|
|
|
|
|
|
Sommario/riassunto |
|
This book is a collection of the accepted papers presented at the Workshop on Artificial Intelligence with Biased or Scarce Data (AIBSD) in conjunction with the 36th AAAI Conference on Artificial Intelligence 2022. During AIBSD 2022, the attendees addressed the existing issues of data bias and scarcity in Artificial Intelligence and discussed potential solutions in real-world scenarios. A set of papers presented at AIBSD 2022 is selected for further publication and included in this |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
2. |
Record Nr. |
UNINA9910781844403321 |
|
|
Autore |
Badiru Adedeji Bodunde <1952, > |
|
|
Titolo |
Industrial control systems : mathematical and statistical models and techniques / / Adedeji B. Badiru, Oye Ibidapo-Obe, Babatunde J. Ayeni |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Boca Raton : , : CRC Press, , 2012 |
|
|
|
|
|
|
|
ISBN |
|
0-429-13987-X |
1-283-34982-5 |
9786613349828 |
1-4200-7559-4 |
|
|
|
|
|
|
|
|
Descrizione fisica |
|
1 online resource (374 p.) |
|
|
|
|
|
|
Collana |
|
|
|
|
|
|
Classificazione |
|
TEC016000TEC009000TEC007000 |
|
|
|
|
|
|
Altri autori (Persone) |
|
Ibidapo-ObeOye |
AyeniBabatunde J |
|
|
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Process control - Mathematical models |
Process control - Statistical methods |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
Description based upon print version of record. |
|
|
|
|
|
|
Nota di bibliografia |
|
Includes bibliographical references. |
|
|
|
|
|
|
Nota di contenuto |
|
Front Cover; Contents; Preface; Acknowledgments; Authors; Chapter 1: Mathematical modeling for product design; Chapter 2: Dynamic fuzzy systems modeling; Chapter 3: Stochastic systems modeling; Chapter 4: Systems optimization techniques; Chapter 5: Statistical control techniques; Chapter 6: Design of experiment techniques; Chapter 7: Risk analysis and estimation techniques; Chapter 8: Mathematical modeling and control of multi- constrained projects; Chapter 9: Online support vector regression with varying parameters for time-dependent data; Appendix: Mathematical and engineering formulae |
Back Cover |
|
|
|
|
|
|
|
|
Sommario/riassunto |
|
Preface This book presents the mathematical foundation for building and implementing industrial control systems. It contains mathematically rigorous models and techniques for control systems, in general, with specific orientation toward industrial systems. Industrial control encompasses several types of control systems. Some common |
|
|
|
|
|
|
|
|
|
|
elements of industrial control systems include supervisory control and data acquisition systems, distributed control systems, and other generic control system configurations, such as programmable logic controllers, that are often found in industrial operations and engineering infrastructure. Industrial control systems are not limited to production or manufacturing enterprises, as they are typically used in general industries such as electrical, water, oil and gas, and data acquisition devices. Based on information received from remote sensors, automated commands can be sent to remote control devices, which are referred to as field devices. Field devices are used to control local operations. These may include opening and closing valves, tripping breakers, collecting data from sensors, and monitoring local operating conditions. All of these are governed by some form of mathematical representation. Thus, this book has great importance in linking theory and practice. Distributed control systems are used to control industrial processes such as electric power generation, oil and gas refineries, water and wastewater treatment, and chemical, food, and automotive production. -- |
|
|
|
|
|
| |