ORCID Profile
0000-0001-6531-5087
Current Organisation
University of South Australia
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Publisher: IEEE
Date: 07-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Elsevier BV
Date: 05-2017
Publisher: Society for Industrial and Applied Mathematics
Date: 06-05-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 16-10-2022
Publisher: Springer Science and Business Media LLC
Date: 19-03-2021
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Elsevier BV
Date: 12-2016
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Publisher: Association for Computing Machinery (ACM)
Date: 15-04-2020
DOI: 10.1145/3380972
Abstract: The timely and accurate prediction of remote sensing data is of utmost importance especially in a situation where the predicted data is utilized to provide insights into emerging issues, like environmental nowcasting. Significant research progress can be found to date in devising variants of neural network (NN) models to fulfil this requirement by improving feature extraction and dynamic process representation power. Nevertheless, all these existing NN models are built upon rigid structures that often fail to maintain tradeoff between bias and variance, and consequently, need to spend a lot of time to empirically determine the most appropriate network configuration. This article proposes a self-adaptive recurrent deep incremental network model (SARDINE) which is a novel variant of the deep recurrent neural network with intrinsic capability of self-constructing the network structure in a dynamic and incremental fashion while learning from observed data s les. Moreover, the proposed SARDINE is able to model the spatial feature evolution while scanning the data in a single pass manner, and this further saves significant time when dealing with remote sensing imagery containing millions of pixels. Subsequently, we employ SARDINE in combination with a spatial influence mapping unit to accomplish the prediction. The effectiveness of the proposed model is evaluated in terms of predicting a time series of normalized difference vegetation index (NDVI) data derived from Landsat Thematic Mapper (TM)-5 and Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite imagery. The experimental result demonstrates that the SARDINE-based prediction is able to achieve state-of-the-art accuracy with significantly reduced computational cost.
Publisher: IEEE
Date: 05-2020
Publisher: Wiley
Date: 17-05-2019
DOI: 10.1002/ACS.3002
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: Elsevier BV
Date: 08-2019
Publisher: IEEE
Date: 30-05-2021
Publisher: IEEE
Date: 11-2017
Publisher: Hindawi Limited
Date: 09-2021
DOI: 10.1155/2021/3005276
Abstract: Data-driven quality monitoring is highly demanded in practice since it enables relieving manual quality inspection of the product quality. Conventional data-driven quality monitoring is constrained by its offline characteristic thus being unable to handle streaming nature of sensory data and nonstationary environments of machine operations. Recently, there have been pioneering works of online quality monitoring taking advantage of online learning concepts in the literature, but it is still far from realization of minimum operator intervention in the quality monitoring because it calls for full supervision in labelling data s les. This paper proposes Parsimonious Network++ (ParsNet++) as an online semisupervised learning approach being able to handle extreme label scarcity in the quality monitoring task. That is, it is capable of coping with varieties of semisupervised learning conditions including random access of ground truth and infinitely delayed access of ground truth. ParsNet++ features the one-pass learning approach to deal with streaming data while characterizing elastic structure to overcome rapidly changing data distributions. That is, it is capable of initiating its learning structure from scratch with the absence of a predefined network structure where its hidden nodes can be added and discarded on the fly in respect to drifting data distributions. Furthermore, it is equipped by a feature extraction layer in terms of 1D convolutional layer extracting natural features of multivariate time-series data s les of sensors and coping well with the many-to-one label relationship, a common problem of practical quality monitoring. Rigorous numerical evaluation has been carried out using the injection molding machine and the industrial transfer molding machine from our own projects. ParsNet++ delivers highly competitive performance even compared to fully supervised competitors.
Publisher: IEEE
Date: 07-2019
Publisher: Springer Science and Business Media LLC
Date: 17-08-2018
Publisher: Springer International Publishing
Date: 2020
Publisher: IEEE
Date: 04-12-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2018
Publisher: Elsevier BV
Date: 02-2019
Publisher: IEEE
Date: 11-2018
Publisher: IEEE
Date: 10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2020
Publisher: Elsevier BV
Date: 06-2020
Publisher: IEEE
Date: 23-10-2021
Publisher: IEEE
Date: 23-10-2021
Publisher: Springer Science and Business Media LLC
Date: 09-08-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: Elsevier BV
Date: 11-2017
Publisher: Springer Science and Business Media LLC
Date: 25-08-2021
DOI: 10.1007/S12530-021-09398-X
Abstract: Evolving fuzzy systems (EFS) have enjoyed a wide attraction in the community to handle learning from data streams in an incremental, single-pass and transparent manner. The main concentration so far lied in the development of approaches for single EFS models, basically used for prediction purposes. Forgetting mechanisms have been used to increase their flexibility, especially for the purpose to adapt quickly to changing situations such as drifting data distributions. These require forgetting factors steering the degree of timely out-weighing older learned concepts, whose adequate setting in advance or in adaptive fashion is not an easy and not a fully resolved task. In this paper, we propose a new concept of learning fuzzy systems from data streams, which we call online sequential ensembling of fuzzy systems (OS-FS) . It is able to model the recent dependencies in streams on a chunk-wise basis: for each new incoming chunk, a new fuzzy model is trained from scratch and added to the ensemble (of fuzzy systems trained before). This induces (i) maximal flexibility in terms of being able to apply variable chunk sizes according to the actual system delay in receiving target values and (ii) fast reaction possibilities in the case of arising drifts. The latter are realized with specific prediction techniques on new data chunks based on the sequential ensemble members trained so far over time. We propose four different prediction variants including various weighting concepts in order to put higher weights on the members with higher inference certainty during the amalgamation of predictions of single members to a final prediction. In this sense, older members, which keep in mind knowledge about past states, may get dynamically reactivated in the case of cyclic drifts, which induce dynamic changes in the process behavior which are re-occurring from time to time later. Furthermore, we integrate a concept for properly resolving possible contradictions among members with similar inference certainties. The reaction onto drifts is thus autonomously handled on demand and on the fly during the prediction stage (and not during model adaptation/evolution stage as conventionally done in single EFS models), which yields enormous flexibility. Finally, in order to cope with large-scale and (theoretically) infinite data streams within a reasonable amount of prediction time, we demonstrate two concepts for pruning past ensemble members, one based on atypical high error trends of single members and one based on the non- ersity of ensemble members. The results based on two data streams showed significantly improved performance compared to single EFS models in terms of a better convergence of the accumulated chunk-wise ahead prediction error trends, especially in the case of regular and cyclic drifts. Moreover, the more advanced prediction schemes could significantly outperform standard averaging over all members’ outputs. Furthermore, resolving contradictory outputs among members helped to improve the performance of the sequential ensemble further. Results on a wider range of data streams from different application scenarios showed (i) improved error trend lines over single EFS models, as well as over related AI methods OS-ELM and MLPs neural networks retrained on data chunks, and (ii) slightly worse trend lines than on-line bagged EFS (as specific EFS ensembles), but with around 100 times faster processing times (achieving low processing times way below requiring milli-seconds for single s les updates).
Publisher: IEEE
Date: 15-12-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2014
Publisher: IEEE
Date: 04-2013
Publisher: IEEE
Date: 07-2019
Publisher: Elsevier BV
Date: 11-2018
Publisher: Elsevier BV
Date: 04-2019
Publisher: IEEE
Date: 07-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2016
Publisher: Elsevier BV
Date: 10-2020
Publisher: Elsevier BV
Date: 2024
Publisher: IEEE
Date: 11-2016
Publisher: IEEE
Date: 12-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2020
Publisher: Elsevier BV
Date: 11-2017
Publisher: IEEE
Date: 07-2016
Publisher: Hindawi Limited
Date: 20-05-2020
DOI: 10.1155/2020/3974503
Abstract: Real-world complex systems inevitably suffer from perturbations. When some system components break down and trigger cascading failures on a system, the system will be out of control. In order to assess the tolerance of complex systems to perturbations, an effective way is to model a system as a network composed of nodes and edges and then carry out network robustness analysis. Percolation theories have proven as one of the most effective ways for assessing the robustness of complex systems. However, existing percolation theories are mainly for multilayer or interdependent networked systems, while little attention is paid to complex systems that are modeled as multipartite networks. This paper fills this void by establishing the percolation theories for multipartite networked systems under random failures. To achieve this goal, this paper first establishes two network models to describe how cascading failures propagate on multipartite networks subject to random node failures. Afterward, this paper adopts the largest connected component concept to quantify the networks’ robustness. Finally, this paper develops the corresponding percolation theories based on the developed network models. Simulations on computer-generated multipartite networks demonstrate that the proposed percolation theories coincide quite well with the simulations.
Publisher: IEEE
Date: 07-2016
Publisher: IEEE
Date: 07-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2018
Publisher: IEEE
Date: 02-2017
Publisher: Elsevier BV
Date: 05-2018
Publisher: Elsevier BV
Date: 10-2019
Publisher: IEEE
Date: 05-2017
Publisher: IEEE
Date: 06-2019
Publisher: Springer International Publishing
Date: 2019
Publisher: Elsevier BV
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
Publisher: Springer Science and Business Media LLC
Date: 29-08-2016
Publisher: Elsevier BV
Date: 03-2018
Publisher: IEEE
Date: 10-2019
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 14-03-2015
Publisher: IEEE
Date: 11-2021
Publisher: IEEE
Date: 11-2011
Publisher: IEEE
Date: 07-2019
Publisher: IEEE
Date: 02-2019
Publisher: Elsevier BV
Date: 04-2019
Publisher: Elsevier BV
Date: 09-2021
Publisher: IEEE
Date: 12-2019
Publisher: Elsevier BV
Date: 02-2020
Publisher: IEEE
Date: 05-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: IEEE
Date: 12-2019
Publisher: Elsevier BV
Date: 06-2021
Publisher: Elsevier BV
Date: 03-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Publisher: IEEE
Date: 06-2019
Publisher: Elsevier BV
Date: 2018
Publisher: IEEE
Date: 05-2018
Publisher: IEEE
Date: 10-2015
DOI: 10.1109/SMC.2015.487
Publisher: IEEE
Date: 11-2018
Publisher: IEEE
Date: 12-2014
Publisher: Universitas Negeri Semarang
Date: 09-12-2019
DOI: 10.15294/JEJAK.V12I2.21289
Abstract: Regional expansion could be a strength to improve the performance of local governments and subsequently should have a positive impact, such as improving the welfare of the local community. Regional expansion also aims to make governments at regional level to be more focused on escalating potential sectors in their regions. This study analyzes the repositioning of the GRDP contributing sector before and after expansion between two regency regions in 2010-2017, one parental Regency and one area that is separated from its parental. Repositioned sectors show the influence of an area in terms of its wealth of the resources as basis sector. The research method uses Location Quotient (LQ) and Shift Share analysis. Results of this study show that before and after expansion of districts into new regency, agricultural sector has declined in the contribution of GRDP. But after the expansion, the sectors with the best economic performance are the construction, administration and trade & repair sectors. In addition, agricultural sector to some extent has been decreased in the two regions. But at the same time, agricultural sector become leading sector in the new region with slow growth. Implication of this study is that the ision of regions would not create new leading sector if the potential sector in a new region is the leading sector in the older region. Therefore, policy making which ensure basic sectors to have positive proportional shift and differential shift could drive economic development planning in both regions.
Publisher: Springer International Publishing
Date: 2014
Publisher: Hindawi Limited
Date: 20-11-2019
DOI: 10.1155/2019/2680972
Abstract: Complex networks in reality may suffer from target attacks which can trigger the breakdown of the entire network. It is therefore pivotal to evaluate the extent to which a network could withstand perturbations. The research on network robustness has proven as a potent instrument towards that purpose. The last two decades have witnessed the enthusiasm on the studies of network robustness. However, existing studies on network robustness mainly focus on multilayer networks while little attention is paid to multipartite networks which are an indispensable part of complex networks. In this study, we investigate the robustness of multipartite networks under intentional node attacks. We develop two network models based on the largest connected component theory to depict the cascading failures on multipartite networks under target attacks. We then investigate the robustness of computer-generated multipartite networks with respect to eight node centrality metrics. We discover that the robustness of multipartite networks could display either discontinuous or continuous phase transitions. Interestingly, we discover that larger number of partite sets of a multipartite network could increase its robustness which is opposite to the phenomenon observed on multilayer networks. Our findings shed new lights on the robust structure design of complex systems. We finally present useful discussions on the applications of existing percolation theories that are well studied for network robustness analysis to multipartite networks. We show that existing percolation theories are not amenable to multipartite networks. Percolation on multipartite networks still deserves in-depth efforts.
Publisher: Inderscience Publishers
Date: 2020
Publisher: IEEE
Date: 11-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
Publisher: Elsevier BV
Date: 10-2022
Publisher: IEEE
Date: 10-2017
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Elsevier BV
Date: 08-2022
Publisher: IEEE
Date: 07-2014
Publisher: IEEE
Date: 11-2016
Publisher: Springer International Publishing
Date: 2022
Publisher: Elsevier BV
Date: 05-2016
Publisher: IEEE
Date: 07-2020
Publisher: Elsevier BV
Date: 12-2019
Publisher: Springer Science and Business Media LLC
Date: 15-05-2023
DOI: 10.1038/S41598-023-33414-6
Abstract: In multi-objective optimization, it becomes prohibitively difficult to cover the Pareto front (PF) as the number of points scales exponentially with the dimensionality of the objective space. The challenge is exacerbated in expensive optimization domains where evaluation data is at a premium. To overcome insufficient representations of PFs, Pareto estimation (PE) invokes inverse machine learning to map preferred but unexplored regions along the front to the Pareto set in decision space. However, the accuracy of the inverse model depends on the training data, which is inherently scarce/small given high-dimensional/expensive objectives. To alleviate this small data challenge, this paper marks a first study on multi-source inverse transfer learning for PE. A method to maximally utilize experiential source tasks to augment PE in the target optimization task is proposed. Information transfers between heterogeneous source-target pairs is uniquely enabled in the inverse setting through the unification provided by common objective spaces. Our approach is tested experimentally on benchmark functions as well as on high-fidelity, multidisciplinary simulation data of composite materials manufacturing processes, revealing significant gains to the predictive accuracy and PF approximation capacity of Pareto set learning. With such accurate inverse models made feasible, a future of on-demand human-machine interaction facilitating multi-objective decisions is envisioned.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2021
Publisher: Elsevier BV
Date: 02-2018
Publisher: Walter de Gruyter GmbH
Date: 31-12-2018
Abstract: Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is one of the recent research topics related to the field of autonomous MAVs. Some desiring features of the FW MAV are quick flight, vertical take-off and landing, hovering, and fast turn, and enhanced manoeuvrability contrasted with similar-sized fixed and rotary wing MAVs. Inspired by the FW MAV’s advanced features, a four-wing Nature-inspired (NI) FW MAV is modelled and controlled in this work. The Fuzzy C-Means (FCM) clustering algorithm is utilized to construct the data-driven NIFW MAV model. Being model free, it does not depend on the system dynamics and can incorporate various uncertainties like sensor error, wind gust etc. Furthermore, a Takagi-Sugeno (T-S) fuzzy structure based adaptive fuzzy controller is proposed. The proposed adaptive controller can tune its antecedent and consequent parameters using FCM clustering technique. This controller is employed to control the altitude of the NIFW MAV, and compared with a standalone Proportional Integral Derivative (PID) controller, and a Sliding Mode Control (SMC) theory based advanced controller. Parameter adaptation of the proposed controller helps to outperform it static PID counterpart. Performance of our controller is also comparable with its advanced and complex counterpart namely SMC-Fuzzy controller.
Publisher: Elsevier BV
Date: 10-2021
Publisher: Elsevier BV
Date: 03-2019
Publisher: IEEE
Date: 11-2019
Publisher: IEEE
Date: 05-2020
Publisher: IEEE
Date: 08-2015
Publisher: IEEE
Date: 18-07-2021
Publisher: Cambridge University Press (CUP)
Date: 10-2016
DOI: 10.1017/S1743921317001247
Abstract: Planetary nebulae retain the signature of the nucleosynthesis and mixing events that occurred during the previous AGB phase. Observational signatures complement observations of AGB and post-AGB stars and their binary companions. The abundances of the elements heavier than iron such as Kr and Xe in planetary nebulae can be used to complement abundances of Sr/Y/Zr and Ba/La/Ce in AGB stars, respectively, to determine the operation of the slow neutron-capture process (the s process) in AGB stars. Additionally, observations of the Rb abundance in Type I planetary nebulae may allow us to infer the initial mass of the central star. Several noble gas components present in meteoritic stardust silicon carbide (SiC) grains are associated with implantation into the dust grains in the high-energy environment connected to the fast winds from the central stars during the planetary nebulae phase.
Publisher: CSIRO Publishing
Date: 2023
DOI: 10.1071/AN23057
Publisher: Elsevier BV
Date: 09-2023
Publisher: IEEE
Date: 11-2018
Publisher: Elsevier BV
Date: 2016
Publisher: ACM
Date: 20-08-2020
Publisher: Elsevier BV
Date: 05-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer Science and Business Media LLC
Date: 03-11-2016
Publisher: IEEE
Date: 11-2018
Publisher: Elsevier BV
Date: 05-2020
Publisher: Elsevier BV
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2014
Publisher: Elsevier BV
Date: 05-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Publisher: IEEE
Date: 11-2017
Publisher: MDPI AG
Date: 17-12-2018
DOI: 10.3390/APP8122656
Abstract: In recent years, the utilization of rotating parts, e.g., bearings and gears, has been continuously supporting the manufacturing line to produce a consistent output quality. Due to their critical role, the breakdown of these components might significantly impact the production rate. Prognosis, which is an approach that predicts the machine failure, has attracted significant interest in the last few decades. In this paper, the prognostic approaches are described briefly and advanced predictive analytics, namely a parsimonious network based on a fuzzy inference system (PANFIS), is proposed and tested for low speed slew bearing data. PANFIS differs itself from conventional prognostic approaches, supporting online lifelong prognostics without the requirement of a retraining or reconfiguration phase. The PANFIS method is applied to normal-to-failure bearing vibration data collected for 139 days to predict the time-domain features of vibration slew bearing signals. The performance of the proposed method is compared to some established methods, such as ANFIS, eTS, and Simp_eTS. From the results, it is suggested that PANFIS offers an outstanding performance compared to those methods.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2015
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 07-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Elsevier BV
Date: 06-2013
Publisher: IEEE
Date: 18-06-2023
Publisher: IEEE
Date: 10-2022
Publisher: ACM
Date: 17-10-2022
Publisher: Public Library of Science (PLoS)
Date: 13-03-2019
Publisher: IEEE
Date: 10-2018
Publisher: IEEE
Date: 11-2019
Publisher: IEEE
Date: 12-2019
No related grants have been discovered for mahardhika pratama.