Showing items 81 - 100 of 2094

Your selections:

Dynamic mesh commonality modeling using the cuboidal partitioning

  • Authors: Ahmmed, Ashek , Paul, Manoranjan , Murshed, Manzur , Pickering, Mark
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022, Suzhou, China, 13-16 December 2022, 2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022
  • Full Text: false
  • Reviewed:
  • Description: For 3D object representation, volumetric contents like meshes and point clouds provide suitable formats. However, a dynamic mesh sequence may require significantly large amount of data because it consists of information that varies with time. Hence, for the facilitation of storage and transmission of such content, efficient compression technologies are required. MPEG has started standardization activities aiming to develop a mesh compression standard that would be able to handle dynamic meshes with time varying connectivity information and time varying attribute maps. The attribute maps are features associated with the mesh surface and stored as 2D images/videos. In this paper, we propose to capture the commonality information in the dynamic mesh attribute maps using the cuboidal partitioning algorithm. This algorithm is capable of modeling both the global and local commonality within an image in a compact and computationally efficient way. Experimental results show that the proposed approach can outperform the anchor HEVC codec, suggested by MPEG to encode such sequences, with a bit rate savings of up to 3.66%. © 2022 IEEE.

Dynamic signature-based alignment factor for Var allocation

Effect of multiple loading rates on uniaxial compressive strength of rock

  • Authors: Meerza, J. , Khandelwal, Manoj
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 56th U.S. Rock Mechanics/Geomechanics Symposium, Santa Fe, USA, 26-29 June 2022, 56th U.S. Rock Mechanics/Geomechanics Symposium
  • Full Text: false
  • Reviewed:
  • Description: It is of crucial significance to study the effect of loading rate on the behaviour of rock because engineering structures are subjected to multiple loading conditions in their entire life. Although rock behaviour under single loading rate has been widely studied but very limited research has been conducted to study the performance of rock strength subjected to multiple loading conditions. This paper presents an experimental study of the effects of single and multiple strain rates on Sandstone samples. The first set of samples was tested at constant strain rates until failure to determine the peak uniaxial compressive strength (UCS). For the second set of samples, the first strain rate was applied to the sample up to a predetermined load, and then the second strain was initiated to find out the influence of multiple loading rates on the UCS of rock samples. © 2022 ARMA, American Rock Mechanics Association.

Efficient scalable 360-degree video compression scheme using 3d cuboid partitioning

  • Authors: Afsana, Fariha , Paul, Manoranjan , Murshed, Manzur , Taubman, David
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 29th IEEE International Conference on Image Processing, ICIP 2022 p. 996-1000
  • Full Text: false
  • Reviewed:
  • Description: Video coding techniques minimize spatial and temporal redundancies inherent in video sequences based on non-overlapping block-based image partitioning. Due to depending on the information from already encoded neighboring blocks, these algorithms lack efficient techniques to exploit the overall global redundancies. Compared to the traditional block-based coding, the cuboid coding (2D) framework has been proven to be a more effective method of image compression that exploits global redundancy by considering homogeneous pixel correlation within a frame. In this paper, we improved the idea of 2D cuboid coding to exploit both local and global redundancy from a video sequence by adopting a three-dimensional (3D) cuboid partitioning scheme for SHVC compression improvement of 360-degree videos. The proposed method considers a group of successive frames as a 3D cuboid and recursively partitions it into sub-3D cuboids where static information over a selected GOP share the same cuboid and moving regions share new cuboids with better-defined objects. All the 3D cuboids are then encoded to create a coarse representation of the video stream. Experiments indicate that the proposed framework significantly outperforms its relevant benchmarks, notably by 17.18% (average) in BD-Rate reduction and 0.82 dB in BD-PSNR gain with respect to the standard SHVC codec. © 2022 IEEE.

Embedding-based neural network for investment return prediction

Enhanced EV charging experience with an ultra-rapid charger and 800-V battery

  • Authors: Li, Zilin , Cheng, Ka-Wai , Chan, Kevin , Hu, Jiefeng
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 9th International Conference on Power Electronics Systems and Applications, PESA 2022, Hong Kong, 20-22 September 2022, Proceedings of the 9th International Conference on Power Electronics Systems and Applications, PESA 2022
  • Full Text: false
  • Reviewed:
  • Description: It is promising to achieve zero vehicular emissions by electricity-driven vehicles, and impressive development has been reported. Nevertheless, driving range anxiety is a long-standing issue that hinders the further public acceptance of electric vehicles (EVs). The issue is mainly caused by the driving range per full charge and long charging time, which can be addressed by increasing the charging power of EV batteries, leading to ultra-rapid DC chargers and high-voltage EV batteries. This paper is aimed to investigate the EV charging time with an ultra-rapid DC charger and 800-V battery. A two-stage charging system is first developed, including an active front-end AC-DC converter, three-phase dual active-bridge (3p-DAB), and associated controllers. The operation principles of 3p-DAB are derived with details. Then, the charging system is realized on a real-time simulator, and impacts of the charging current setting are investigated on the EV battery voltage, charging current, and charging power. Based on real-time simulation results, recharging times with different charging current settings are analyzed and compared with details. Finally, the paper is concluded with findings on the relationship between charging current, battery SoC, and total charging time. © 2022 IEEE.

Enhancing self-efficacy for fluid management in chronic kidney disease with fitbit and flex

  • Authors: Oseni, Taiwo , Firmin, Sally
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 Australasian Computer Science Week, ACSW 2022, Virtual, Online, 14-17 February 2022, ACM International Conference Proceeding Series p. 239-241
  • Full Text: false
  • Reviewed:
  • Description: Chronic kidney disease patients are required to restrict their fluid intake quite severely. Compliance is particularly challenging for many patients but failing to comply can lead to life-threatening consequences. There is some evidence that behaviour change programs based on education are effective, however a program known as Flex that is based on encouraging patients to be more adaptable and adhere less rigidly to a range of habits of daily living has not been evaluated. A core feature of the Flex program is the generation of text messages to remind patients to perform previously agreed habit changes (known as Do's) based on the analysis of Fitbit data. This study aims to apply a constructivist, qualitative methodology with a group of chronic kidney disease patients to assess the extent to which the Flex program has a positive impact on self-efficacy and ultimately on fluid management. © 2022 ACM.

Enhancing Sustainable Development on Land: Using birds of prey to disperse flocks of native birds that threaten resource use and human activities

Ensemble regression modelling for genetic network inference

  • Authors: Gamage, Hasini , Chetty, Madhu , Shatte, Adrian , Hallinan, Jennifer
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022, Ottawa Canada, 15-17 August 2022, 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022
  • Full Text: false
  • Reviewed:
  • Description: An accurate reconstruction of Gene Regulatory Networks (GRNs) from time series gene expression data is crucial for discovering complex biological interactions. Among many different approaches for inferring GRNs, there are several methods which produce high false positive interactions, and are unstable, requiring fine tuning for many of their parameters. In this paper, we consider the GRN inference problem as a regression problem, and propose a simple ensemble regression-based feature selection model which is a combination of cross-validated Lasso and cross-validated Ridge algorithms for reconstructing GRNs. Due to the novelty of the proposed ensemble model, it is able to eliminate overfitting, multi co-linearity issues, and irrelevant genes within one computational approach. While observing the type of gene-gene regulatory interactions the regression model also identifies the direction of these interactions. A new coefficient of determination (R2)-based approach identifies the best model to fit the data among LassoCV and RidgeCV, and evaluates the model importance in term of gene-wise maximum in-degree which decides the maximum number of regulatory genes including self-regulations that can be selected from a given method. Then, an evaluated gene score-based majority voting technique aggregates the selected gene lists from each method. In our experiments, the performance of the proposed ensemble approach was evaluated using gene expression datasets from three small-scale real gene networks. Our proposed model outperformed other state-of-the-art methods, producing high true positives, reducing false positives, and obtaining high Structural Accuracy, while maintaining model stability and efficiency. © 2022 IEEE.

Evaluating human-like explanations for robot actions in reinforcement learning scenarios

  • Authors: Cruz, Francisco , Young, Charlotte , Dazeley, Richard , Vamplew, Peter
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022, Kyoto, Japan, 23-27 October 2022, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Vol. 2022-October, p. 894-901
  • Full Text:
  • Reviewed:
  • Description: Explainable artificial intelligence is a research field that tries to provide more transparency for autonomous intelligent systems. Explainability has been used, particularly in reinforcement learning and robotic scenarios, to better understand the robot decision-making process. Previous work, however, has been widely focused on providing technical explanations that can be better understood by AI practitioners than non-expert end-users. In this work, we make use of human-like explanations built from the probability of success to complete the goal that an autonomous robot shows after performing an action. These explanations are intended to be understood by people who have no or very little experience with artificial intelligence methods. This paper presents a user trial to study whether these explanations that focus on the probability an action has of succeeding in its goal constitute a suitable explanation for non-expert end-users. The results obtained show that non-expert participants rate robot explanations that focus on the probability of success higher and with less variance than technical explanations generated from Q-values, and also favor counterfactual explanations over standalone explanations. © 2022 IEEE.

Failure analysis of Slurry Pump assets in refinery for reduction of risks and costs

  • Authors: Welandage Don , Chattopadhyay, Gopi , Kahandawa, Gayan , Kamruzzaman, Joarder , Zhang, L.
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2022, Anand, India, 12-15 December 2022, 2022 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2022
  • Full Text: false
  • Reviewed:
  • Description: Assets fail due to design, manufacturing, installation, operations and maintenance, including ageing. Analysis of failures is needed for understanding the failure mechanism and identifying improvement opportunities to reduce risks and costs. Every year, the refinery spends a large amount of money on the maintenance of pumps and related accessories. There are unplanned maintenance and downtimes due to breakdowns. Reliability Centre Maintenance (RCM), Failure Mode and Effect Analysis (FMEA) and Residual life tracker, Equipment Management Strategy (EMS) were considered to create Preventive Maintenance (PM) activities and forecast the maintenance costs. However, budget overruns continued due to unplanned failures. This study analysed failures and identified gaps within the existing strategy for Washer and thickener underflow pumps. A review of maintenance activities was conducted along with review of design capabilities, failure modes and failure mechanisms and trends. Opportunities for improvements (OFI) were identified, and improvement actions were carried out to reduce risks and costs. © 2022 IEEE.

Feasibility study and design of an underground entry/access structure at an underground gold mine

  • Authors: Carlisles, B. , Koroznikova, Larissa , Javidan, Fatemeh , Khandelwal, Manoj
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 56th U.S. Rock Mechanics/Geomechanics Symposium, Santa Fe, USA, 26-29 June 2022, 56th U.S. Rock Mechanics/Geomechanics Symposium
  • Full Text: false
  • Reviewed:
  • Description: This paper investigates the viability of an increase in waste rock storage by backfilling the Falcon pit and extending the portal of the Fosterville gold mine, located in Bendigo, Australia. The structure will maintain the current access to the tunnel whilst developing a fillable void. Once completed the project will allow for a total increase of 900, 000 cubic metres of storage. Furthermore, a finite element study has been conducted to investigate the structural performance of a proposed design using corrugated steel sheets. Stresses and displacements are studied taking into account various design factors such as steel properties and geometry. Results demonstrate the location of critical stress values according to the proposed design. The selection of optimum steel geometry is also investigated with regards to the factor of safety. © 2022 ARMA, American Rock Mechanics Association.

Fuzzy-based operational resilience modelling

Geometric design of the limaçon rotary compressor using bayesian optimization

  • Authors: Lu, Kui , Sultan, Ibrahim , Phung, Truong
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 3rd International Conference on Energy and Power, ICEP 2021, Chiang Mai, Thailand, 18-20 November 2021, AIP Conference Proceedings 2681 Vol. 2681
  • Full Text:
  • Reviewed:
  • Description: In the design of positive displacement compressors, the mathematical relationship between design parameters and design objectives is usually impractical and costly to be extracted, making the optimization process becomes a 'black-box' problem. In the available literature, the Bayesian optimization method, compared to other optimization techniques, has been proven as an accurate and efficient method to solve the 'black-box' problem. However, the application of such a method in the design of the rotary compressor has not been discussed in published papers. Hence, the current study is intended to employ Bayesian optimization to geometric design a class of positive displacement compressors, which is known as the limaçon compressor. In this paper, the geometric characteristics of the limaçon compressor are presented, and a function, which incorporates volumetric and geometric aspects, is employed to evaluate the optimization process and to reveal the optimum design scenario as per design requirements. A case study is offered to prove the validity of the presented approach. © 2022 American Institute of Physics Inc.. All rights reserved.

Graph augmentation learning

  • Authors: Yu, Shuo , Huang, Huafei , Dao, Minh , Xia, Feng
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 31st ACM Web Conference, WWW 2022, Virtual, online, 25 April 2022, WWW 2022 - Companion Proceedings of the Web Conference 2022 p. 1063-1072
  • Full Text:
  • Reviewed:
  • Description: Graph Augmentation Learning (GAL) provides outstanding solutions for graph learning in handling incomplete data, noise data, etc. Numerous GAL methods have been proposed for graph-based applications such as social network analysis and traffic flow forecasting. However, the underlying reasons for the effectiveness of these GAL methods are still unclear. As a consequence, how to choose optimal graph augmentation strategy for a certain application scenario is still in black box. There is a lack of systematic, comprehensive, and experimentally validated guideline of GAL for scholars. Therefore, in this survey, we in-depth review GAL techniques from macro (graph), meso (subgraph), and micro (node/edge) levels. We further detailedly illustrate how GAL enhance the data quality and the model performance. The aggregation mechanism of augmentation strategies and graph learning models are also discussed by different application scenarios, i.e., data-specific, model-specific, and hybrid scenarios. To better show the outperformance of GAL, we experimentally validate the effectiveness and adaptability of different GAL strategies in different downstream tasks. Finally, we share our insights on several open issues of GAL, including heterogeneity, spatio-temporal dynamics, scalability, and generalization. © 2022 ACM.

High step-up common grounded switched Quasi Z-source dc-dc converter using coupled inductor

Human pose based video compression via forward-referencing using deep learning

  • Authors: Rajin, S.M. Ataul Karim , Murshed, Manzur , Paul, Manoranjan , Teng, Shyh , Ma, Jiangang
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022, Suzhou, China,13-16 December 2022, 2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022
  • Full Text: false
  • Reviewed:
  • Description: To exploit high temporal correlations in video frames of the same scene, the current frame is predicted from the already-encoded reference frames using block-based motion estimation and compensation techniques. While this approach can efficiently exploit the translation motion of the moving objects, it is susceptible to other types of affine motion and object occlusion/deocclusion. Recently, deep learning has been used to model the high-level structure of human pose in specific actions from short videos and then generate virtual frames in future time by predicting the pose using a generative adversarial network (GAN). Therefore, modelling the high-level structure of human pose is able to exploit semantic correlation by predicting human actions and determining its trajectory. Video surveillance applications will benefit as stored 'big' surveillance data can be compressed by estimating human pose trajectories and generating future frames through semantic correlation. This paper explores a new way of video coding by modelling human pose from the already-encoded frames and using the generated frame at the current time as an additional forward-referencing frame. It is expected that the proposed approach can overcome the limitations of the traditional backward-referencing frames by predicting the blocks containing the moving objects with lower residuals. Our experimental results show that the proposed approach can achieve on average up to 2.83 dB PSNR gain and 25.93% bitrate savings for high motion video sequences compared to standard video coding. © 2022 IEEE.

Image quality assessment metric fusing traditional and dempster-shafer theory

  • Authors: Kaur, Roopdeep , Karmakar, Gour
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 26th International Conference on Pattern Recognition, ICPR 2022 Montréal, 21-25 August 2022, Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges, Montreal, QC, Canada, August 21–25, 2022, Proceedings, Part II Vol. 13644 LNCS, p. 482-497
  • Full Text: false
  • Reviewed:
  • Description: Image analysis is being applied in many applications including industrial automation with the Industrial Internet of Things and machine vision. The images captured by cameras, from the outdoor environment are impacted by various parameters such as lens blur, dirty lens and lens distortion (barrel distortion). There exist many approaches that assess the impact of camera parameters on the quality of the images. However, most of these techniques do not use important quality assessment metrics such as Oriented FAST and Rotated BRIEF and Structural Content. None of these techniques objectively evaluate the impact of barrel distortion on the image quality using quality assessment metrics such as Mean Square Error, Peak signal-to-noise ratio, Structural Content, Oriented FAST and Rotated BRIEF and Structural Similarity Index. In this paper, besides lens dirtiness and blurring, we also examine the impact of barrel distortion using various types of dataset having different levels of barrel distortion. Analysis shows none of the existing metrics produces quality values consistent with intuitively defined impact levels for lens blur, dirtiness and barrel distortion. To address the loopholes of existing metrics and make the quality assessment metric more reliable, we present two new image quality assessment metrics. For our combined metric, results show that the maximum values of impact level created by barrel distortion, blurriness and dirtiness are 66.6%, 87.9% and 94.4%, respectively. These results demonstrate the effectiveness of our metric to assess the impact level more accurately. The second approach fuses the quality values obtained from different metrics using a decision fusion technique known as the Dempster-Shafer (DS) theory. Our metric produces quality values that are more consistent and conform with the perceptually defined camera parameter impact levels. For all above-mentioned camera impacts, our metric DS exhibits 100 % assessment reliability, which includes an enormous improvement over other metrics. © 2023, Springer Nature Switzerland AG.

Implementation and efficiency calculation of fuel-cell vehicles using a bidirectional DC/DC converter with ZVS

Informing app design to reduce self-management challenges identified for chronic disease

  • Authors: Firmin, Sally , Khurram, Sadaf
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 Australasian Computer Science Week, ACSW 2022, Virtual, Online, 14-17 February 2022, ACM International Conference Proceeding Series p. 246-249
  • Full Text: false
  • Reviewed:
  • Description: Self-managing chronic diseases is challenging for patients and involves handling a range of long-term treatments. Patients have many responsibilities to manage. Feeling overwhelmed by this information can lead to a sense of hopelessness and depression. Mobile apps can be beneficial support for chronically ill patients to selfmanage their condition. Many apps are available for various purposes, such as fitness and daily wellbeing, but none are customised for chronically ill patients who need to manage their disease daily. The purpose of this paper is to propose an approach to identify the critical challenges faced by patients when self-managing their chronic illness. These challenges will inform the design of a customised mobile app that is user friendly and will assist patients in managing their disease efficiently and effectively. A qualitative interpretive approach analysed through the theoretical lens of the theory of planned behaviour (TPB) is the proposed methodology for this project. Interpretivism is about understanding people in their natural world. The social world of people with chronic illnesses is lonely and isolating. The TPB connects a persons attitudes and behaviour. Understanding chronically ill patients feelings of isolation and helplessness through data gathered by semi-structured open-ended interviews will enable a thematic analysis of the critical challenges chronically ill patients face. This paper provides a unique approach for identifying and analysing critical issues that the chronically ill face from a patient viewpoint. These issues will be used to customise the design of an app for self-management purposes. © 2022 Association for Computing Machinery. All rights reserved.