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Full-Text Articles in Engineering
Towards Attention Based Convlstm For Long-Term Travel Time Prediction Of Bus Journey, Jianqing Wu, Qiang Wu, Jun Shen, Chen Cai
Towards Attention Based Convlstm For Long-Term Travel Time Prediction Of Bus Journey, Jianqing Wu, Qiang Wu, Jun Shen, Chen Cai
Faculty of Engineering and Information Sciences - Papers: Part B
Travel time prediction is critical for advanced travelerinformation systems (ATISs), which provides valuable information for enhancing the efficiency and effectiveness of the urban transportation systems. However, in the area of bus trips, existing studies have focused on directly using the structured data to predict travel time for a single bus trip. For state‐of‐the‐art public transportation information systems, a bus journey generally has multiple bus trips. Additionally, due to the lack of study on data fusion, it is even inadequate for the development of underlying intelligent transportation systems. In this paper, we propose a novel framework for a hybrid data‐ driven …
Leveraging Smote In A Two-Layer Model For Prediction Of Protein-Protein Interactions, Huaming Chen, Lei Wang, Chi-Hung Chi, Jun Shen
Leveraging Smote In A Two-Layer Model For Prediction Of Protein-Protein Interactions, Huaming Chen, Lei Wang, Chi-Hung Chi, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
The research of the mechanisms of infectious diseases between host and pathogens remains a hot topic. It takes stock of the interactions data between host and pathogens, including proteins and genomes, to facilitate the discoveries and prediction of underlying mechanisms. However, the incomplete protein-protein interactions data impediment the advances in this exploration and solicit the wet-lab experiments to examine and verify the latent interactions. Although there have been numerous studies trying to leverage the computational models, especially machine learning models, the performances of these models were not good enough to produce high-fidelity candidates of interactions data due to the nature …
Towards A General Prediction System For The Primary Delay In Urban Railways, Jianqing Wu, Luping Zhou, Chen Cai, Fang Dong, Jun Shen, Geng Sun
Towards A General Prediction System For The Primary Delay In Urban Railways, Jianqing Wu, Luping Zhou, Chen Cai, Fang Dong, Jun Shen, Geng Sun
Faculty of Engineering and Information Sciences - Papers: Part B
Nowadays a large amount of data is collected from sensor devices across the cyber-physical networks. Accurate and reliable primary delay predictions are essential for rail operations management and planning. However, very few existing 'big data' methods meet the specific needs in railways. We propose a comprehensive and general data-driven Primary Delay Prediction System (PDPS) framework, which combines General Transit Feed Specification (GTFS), Critical Point Search (CPS), and deep learning models to leverage the data fusion. Based on this framework, we have also developed an open source data collection and processing tool that reduces the barrier to the use of the …
Parsimonious Network Based On A Fuzzy Inference System (Panfis) For Time Series Feature Prediction Of Low Speed Slew Bearing Prognosis, Wahyu Caesarendra, Mahardhika Pratama, Buyung Kosasih, Tegoeh Tjahjowidodo, Adam Glowacz
Parsimonious Network Based On A Fuzzy Inference System (Panfis) For Time Series Feature Prediction Of Low Speed Slew Bearing Prognosis, Wahyu Caesarendra, Mahardhika Pratama, Buyung Kosasih, Tegoeh Tjahjowidodo, Adam Glowacz
Faculty of Engineering and Information Sciences - Papers: Part B
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 …
Integrated Condition Monitoring And Prognosis Method For Incipient Defect Detection And Remaining Life Prediction Of Low Speed Slew Bearings, Wahyu Caesarendra, Tegoeh Tjahjowidodo, Buyung Kosasih, Anh Kiet Tieu
Integrated Condition Monitoring And Prognosis Method For Incipient Defect Detection And Remaining Life Prediction Of Low Speed Slew Bearings, Wahyu Caesarendra, Tegoeh Tjahjowidodo, Buyung Kosasih, Anh Kiet Tieu
Faculty of Engineering and Information Sciences - Papers: Part B
This paper presents an application of multivariate state estimation technique (MSET), sequential probability ratio test (SPRT) and kernel regression for low speed slew bearing condition monitoring and prognosis. The method is applied in two steps. Step (1) is the detection of the incipient slew bearing defect. In this step, combined MSET and SPRT is used with circular-domain kurtosis, time-domain kurtosis, wavelet decomposition (WD) kurtosis, empirical mode decomposition (EMD) kurtosis and the largest Lyapunov exponent (LLE) feature. Step (2) is the prediction of the selected features' trends and the estimation of the remaining useful life (RUL) of the slew bearing. In …
Collaborative Data Analytics Towards Prediction On Pathogen-Host Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Jiangning Song
Collaborative Data Analytics Towards Prediction On Pathogen-Host Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Jiangning Song
Faculty of Engineering and Information Sciences - Papers: Part B
Nowadays more and more data are being sequenced and accumulated in system biology, which bring the data analytics researchers to a brand new era, namely 'big data', to extract the inner relationship and knowledge from the huge amount of data.
Leveraging Stacked Denoising Autoencoder In Prediction Of Pathogen-Host Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Jiangning Song
Leveraging Stacked Denoising Autoencoder In Prediction Of Pathogen-Host Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Jiangning Song
Faculty of Engineering and Information Sciences - Papers: Part B
In big data research related to bioinformatics, one of the most critical areas is proteomics. In this paper, we focus on the protein-protein interactions, especially on pathogen-host protein-protein interactions (PHPPIs), which reveals the critical molecular process in biology. Conventionally, biologists apply in-lab methods, including small-scale biochemical, biophysical, genetic experiments and large-scale experiment methods (e.g. yeast-two-hybrid analysis), to identify the interactions. These in-lab methods are time consuming and labor intensive. Since the interactions between proteins from different species play very critical roles for both the infectious diseases and drug design, the motivation behind this study is to provide a basic framework …
Linear Regression Models For Prediction Of Annual Heating And Cooling Demand In Representative Australian Residential Dwellings, Navid Aghdaei, Georgios Kokogiannakis, Daniel J. Daly, Timothy J. Mccarthy
Linear Regression Models For Prediction Of Annual Heating And Cooling Demand In Representative Australian Residential Dwellings, Navid Aghdaei, Georgios Kokogiannakis, Daniel J. Daly, Timothy J. Mccarthy
Faculty of Engineering and Information Sciences - Papers: Part B
This paper presents the development methodology of linear regression models that were developed for the prediction of annual thermal loads in representative residential buildings across three major climates in New South Wales, Australia, and the assessment of the impact of building envelope upgrades. A differential sensitivity analysis was undertaken for sixteen building envelope parameters, with six parameters being identified as significant. These six parameters were then explored using EnergyPlus simulation, and a number of linear regression models developed from the simulation outputs. Random values for design parameters were generated, and the results of EnergyPlus simulations using these parameters were used …
The Effect Of Imputing Missing Clinical Attribute Values On Training Lung Cancer Survival Prediction Model Performance, Mohamed S. Barakat, Matthew Field, Aditya K. Ghose, David Stirling, Lois C. Holloway, Shalini K. Vinod, Andre Dekker, David Thwaites
The Effect Of Imputing Missing Clinical Attribute Values On Training Lung Cancer Survival Prediction Model Performance, Mohamed S. Barakat, Matthew Field, Aditya K. Ghose, David Stirling, Lois C. Holloway, Shalini K. Vinod, Andre Dekker, David Thwaites
Faculty of Engineering and Information Sciences - Papers: Part B
According to the estimations of the World Health Organization and the International Agency for Research in Cancer, lung cancer is the most common cause of death from cancer worldwide. The last few years have witnessed a rise in the attention given to the use of clinical decision support systems in medicine generally and in cancer in particular. These can predict patients' likelihood of survival based on analysis of and learning from previously treated patients. The datasets that are mined for developing clinical decision support functionality are often incomplete, which adversely impacts the quality of the models developed and the decision …