https://nova.newcastle.edu.au/vital/access/manager/Index ${session.getAttribute("locale")} 5 An asymptotically optimal indirect approach to continuous-time system identification https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:35341 Wed 17 Jul 2019 10:00:25 AEST ]]> Nonlinear System Identification: Learning while Respecting Physical Models Using a Sequential Monte Carlo Method https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:48030 Wed 15 Feb 2023 16:52:14 AEDT ]]> On stability and performance of finite control set MPC for power converters https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:10674 Wed 11 Apr 2018 15:37:14 AEST ]]> Application of a neuro-fuzzy model to evaluate the thermal performance of typical Australian residential masonry buildings https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:5944 Wed 11 Apr 2018 13:56:20 AEST ]]> Long-term immersion corrosion of steel subject to large annual variations in seawater temperature and nutrient concentration https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:33282 Tue 25 Sep 2018 12:32:19 AEST ]]> Learning to prioritize test programs for compiler testing https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:32674 learning to test, which learns the characteristics of bug-revealing test programs from previous test programs that triggered bugs. Based on the idea of learning to test, we propose LET, an approach to prioritizing test programs for compiler testing acceleration. LET consists of a learning process and a scheduling process. In the learning process, LET identifies a set of features of test programs, trains a capability model to predict the probability of a new test program for triggering compiler bugs and a time model to predict the execution time of a test program. In the scheduling process, LET prioritizes new test programs according to their bug-revealing probabilities in unit time, which is calculated based on the two trained models. Our extensive experiments show that LET significantly accelerates compiler testing. In particular, LET reduces more than 50% of the testing time in 24.64% of the cases, and reduces between 25% and 50% of the testing time in 36.23% of the cases.]]> Tue 10 Jul 2018 15:38:29 AEST ]]> On the stability and robustness of model predictive direct current control https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:19983 Sat 24 Mar 2018 07:50:58 AEDT ]]> New research findings for the atmospheric corrosion of steel structures and the development of predictive models for corrosion loss and pitting https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:6027 Sat 24 Mar 2018 07:47:52 AEDT ]]> Corrosion wastage in aged structures https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:6616 Sat 24 Mar 2018 07:46:22 AEDT ]]> A nonlinear programming approach to exposure optimization in scanning laser lithography https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:28560 Sat 24 Mar 2018 07:28:44 AEDT ]]> Challenges for capacitor voltage balancing in a cascaded h-bridge StatCom utilising finite control set Model Predictive Control https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:23718 Sat 24 Mar 2018 07:16:53 AEDT ]]> Prompt Tuning in Code Intelligence: An Experimental Evaluation https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:54193 Mon 12 Feb 2024 14:36:23 AEDT ]]> Model predictive control of distributed air-conditioning loads for mitigation of solar variability https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:34715 Fri 26 Apr 2019 11:54:23 AEST ]]> An operational planning framework for large-scale thermostatically controlled load dispatch https://nova.newcastle.edu.au/vital/access/manager/Repository/uon:34636 Fri 05 Apr 2019 15:31:48 AEDT ]]>