Multi-source cyber-attacks detection using machine learning
- Taheri, Sona, Gondal, Iqbal, Bagirov, Adil, Harkness, Greg, Brown, Simon, Chi, Chihung
- Authors: Taheri, Sona , Gondal, Iqbal , Bagirov, Adil , Harkness, Greg , Brown, Simon , Chi, Chihung
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 1167-1172
- Full Text:
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- Description: The Internet of Things (IoT) has significantly increased the number of devices connected to the Internet ranging from sensors to multi-source data information. As the IoT continues to evolve with new technologies number of threats and attacks against IoT devices are on the increase. Analyzing and detecting these attacks originating from different sources needs machine learning models. These models provide proactive solutions for detecting attacks and their sources. In this paper, we propose to apply a supervised machine learning classification technique to identify cyber-attacks from each source. More precisely, we apply the incremental piecewise linear classifier that constructs boundary between sources/classes incrementally starting with one hyperplane and adding more hyperplanes at each iteration. The algorithm terminates when no further significant improvement of the separation of sources/classes is possible. The construction and usage of piecewise linear boundaries allows us to avoid any possible overfitting. We apply the incremental piecewise linear classifier on the multi-source real world cyber security data set to identify cyber-attacks and their sources.
- Description: Proceedings of the IEEE International Conference on Industrial Technology
- Authors: Taheri, Sona , Gondal, Iqbal , Bagirov, Adil , Harkness, Greg , Brown, Simon , Chi, Chihung
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 1167-1172
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) has significantly increased the number of devices connected to the Internet ranging from sensors to multi-source data information. As the IoT continues to evolve with new technologies number of threats and attacks against IoT devices are on the increase. Analyzing and detecting these attacks originating from different sources needs machine learning models. These models provide proactive solutions for detecting attacks and their sources. In this paper, we propose to apply a supervised machine learning classification technique to identify cyber-attacks from each source. More precisely, we apply the incremental piecewise linear classifier that constructs boundary between sources/classes incrementally starting with one hyperplane and adding more hyperplanes at each iteration. The algorithm terminates when no further significant improvement of the separation of sources/classes is possible. The construction and usage of piecewise linear boundaries allows us to avoid any possible overfitting. We apply the incremental piecewise linear classifier on the multi-source real world cyber security data set to identify cyber-attacks and their sources.
- Description: Proceedings of the IEEE International Conference on Industrial Technology
Patient-empowered electronic health records
- Sahama, Tony, Stranieri, Andrew, Butler-Henderson, Kerryn
- Authors: Sahama, Tony , Stranieri, Andrew , Butler-Henderson, Kerryn
- Date: 2019
- Type: Text , Conference proceedings
- Relation: MEDINFO 2019: Health and Wellbeing e-Networks for All Vol. 264, p. 1765
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- Description: Electronic Health Records (EHRs) constitute evidence of online health information management. Critical healthcare information technology (HIT) infrastructure facilitates health information exchange of 'modern' health systems. The growth and implementation of EHRs are progressing in many countries while the adoption rate is lagging and lacking momentum amidst privacy and security concerns. This paper uses an interrupted time series (ITS) analysis of OECD data related to EHRs from many countries to make predictions about EHR adoption. The ITS model can be used to explore the impact of various HIT on adoption. Assumptions about the impact of Information Accountability are entered into the model to generate projections if information accountability technologies are developed. In this way, the OECD data and ITS analysis can be used to perform simulations for improving EHR adoption.
- Authors: Sahama, Tony , Stranieri, Andrew , Butler-Henderson, Kerryn
- Date: 2019
- Type: Text , Conference proceedings
- Relation: MEDINFO 2019: Health and Wellbeing e-Networks for All Vol. 264, p. 1765
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- Description: Electronic Health Records (EHRs) constitute evidence of online health information management. Critical healthcare information technology (HIT) infrastructure facilitates health information exchange of 'modern' health systems. The growth and implementation of EHRs are progressing in many countries while the adoption rate is lagging and lacking momentum amidst privacy and security concerns. This paper uses an interrupted time series (ITS) analysis of OECD data related to EHRs from many countries to make predictions about EHR adoption. The ITS model can be used to explore the impact of various HIT on adoption. Assumptions about the impact of Information Accountability are entered into the model to generate projections if information accountability technologies are developed. In this way, the OECD data and ITS analysis can be used to perform simulations for improving EHR adoption.
Privacy and Security of Connected Vehicles in Intelligent Transportation System
- Jolfaei, Alireza, Kant, Krishna
- Authors: Jolfaei, Alireza , Kant, Krishna
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume, DSN-S 2019, Portland, United States; 24-27 June 2019. p. 9-10
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- Description: The paper considers data security and privacy issues in intelligent transportation systems which involve data streams coming out from individual vehicles to road side units. In this environment, there are issues in regards to the scalability of key management and computation limitations at the edge of the network. To address these issues, we suggest the formation of groups in the vehicular layer, where a group leader is assigned to communicate with group members and the road side unit. We propose a lightweight permutation mechanism for preserving the confidentiality and privacy of sensory data. © 2019 IEEE.
- Description: E1
- Authors: Jolfaei, Alireza , Kant, Krishna
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume, DSN-S 2019, Portland, United States; 24-27 June 2019. p. 9-10
- Full Text:
- Reviewed:
- Description: The paper considers data security and privacy issues in intelligent transportation systems which involve data streams coming out from individual vehicles to road side units. In this environment, there are issues in regards to the scalability of key management and computation limitations at the edge of the network. To address these issues, we suggest the formation of groups in the vehicular layer, where a group leader is assigned to communicate with group members and the road side unit. We propose a lightweight permutation mechanism for preserving the confidentiality and privacy of sensory data. © 2019 IEEE.
- Description: E1
PU-shapelets : Towards pattern-based positive unlabeled classification of time series
- Liang, Shen, Zhang, Yanchun, Ma, Jiangang
- Authors: Liang, Shen , Zhang, Yanchun , Ma, Jiangang
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019; Chiang Mai, Thailand; 22nd-25th April 2019; part of the Lecture Notes in Computer Science book series, also part of the Information Systems and Applications, incl. Internet/Web and HCI sub series Vol. 11446 LNCS, p. 87-103
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- Description: Real-world time series classification applications often involve positive unlabeled (PU) training data, where there are only a small set PL of positive labeled examples and a large set U of unlabeled ones. Most existing time series PU classification methods utilize all readings in the time series, making them sensitive to non-characteristic readings. Characteristic patterns named shapelets present a promising solution to this problem, yet discovering shapelets under PU settings is not easy. In this paper, we take on the challenging task of shapelet discovery with PU data. We propose a novel pattern ensemble technique utilizing both characteristic and non-characteristic patterns to rank U examples by their possibilities of being positive. We also present a novel stopping criterion to estimate the number of positive examples in U. These enable us to effectively label all U training examples and conduct supervised shapelet discovery. The shapelets are then used to build a one-nearest-neighbor classifier for online classification. Extensive experiments demonstrate the effectiveness of our method.
- Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Authors: Liang, Shen , Zhang, Yanchun , Ma, Jiangang
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019; Chiang Mai, Thailand; 22nd-25th April 2019; part of the Lecture Notes in Computer Science book series, also part of the Information Systems and Applications, incl. Internet/Web and HCI sub series Vol. 11446 LNCS, p. 87-103
- Full Text:
- Reviewed:
- Description: Real-world time series classification applications often involve positive unlabeled (PU) training data, where there are only a small set PL of positive labeled examples and a large set U of unlabeled ones. Most existing time series PU classification methods utilize all readings in the time series, making them sensitive to non-characteristic readings. Characteristic patterns named shapelets present a promising solution to this problem, yet discovering shapelets under PU settings is not easy. In this paper, we take on the challenging task of shapelet discovery with PU data. We propose a novel pattern ensemble technique utilizing both characteristic and non-characteristic patterns to rank U examples by their possibilities of being positive. We also present a novel stopping criterion to estimate the number of positive examples in U. These enable us to effectively label all U training examples and conduct supervised shapelet discovery. The shapelets are then used to build a one-nearest-neighbor classifier for online classification. Extensive experiments demonstrate the effectiveness of our method.
- Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Remote asset management for reducing life cycle costs (LCC), risks and enhancing asset performance
- Chundhoo, Vickram, Chattopadhyay, Gopinath, Parida, Aditya
- Authors: Chundhoo, Vickram , Chattopadhyay, Gopinath , Parida, Aditya
- Date: 2019
- Type: Text , Conference proceedings
- Relation: Proceedings of the 5th International Workshop and Congress on eMaintenance: eMaintenance: Trends in Technologies & methodologies, challenges, possibilites and applications p. 74-80
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- Description: Remote asset management are faced with additional challenges in monitoring conditions, coordinating logistics for maintenance crew, transport and spare parts for maintenance delivery and asset replacements. Recent trends in technologies, remote performance monitoring and risk-based decision making in Capital Expenditure (CAPEX) and Operations and Maintenance Expenditure (OPEX) decisions for asset management are being embraced by asset intensive industries around the world, where critical assets are located in geographically distributed remote areas or difficult to inspect and maintain locations. Industries are also pushing boundaries by reducing crew size, deferring capital expenditure and overhauling and decision making in inspection and in some cases relaxing Original Equipment Manufacturers (OEM) recommended maintenance schedules. This paper discusses some of the issues and challenges with remote asset management. Illustrative example from heavy haul rail is used to explain reduction in Life Cycle Costs (LCC) and further enhancing operational performance.
- Description: E1
- Authors: Chundhoo, Vickram , Chattopadhyay, Gopinath , Parida, Aditya
- Date: 2019
- Type: Text , Conference proceedings
- Relation: Proceedings of the 5th International Workshop and Congress on eMaintenance: eMaintenance: Trends in Technologies & methodologies, challenges, possibilites and applications p. 74-80
- Full Text:
- Reviewed:
- Description: Remote asset management are faced with additional challenges in monitoring conditions, coordinating logistics for maintenance crew, transport and spare parts for maintenance delivery and asset replacements. Recent trends in technologies, remote performance monitoring and risk-based decision making in Capital Expenditure (CAPEX) and Operations and Maintenance Expenditure (OPEX) decisions for asset management are being embraced by asset intensive industries around the world, where critical assets are located in geographically distributed remote areas or difficult to inspect and maintain locations. Industries are also pushing boundaries by reducing crew size, deferring capital expenditure and overhauling and decision making in inspection and in some cases relaxing Original Equipment Manufacturers (OEM) recommended maintenance schedules. This paper discusses some of the issues and challenges with remote asset management. Illustrative example from heavy haul rail is used to explain reduction in Life Cycle Costs (LCC) and further enhancing operational performance.
- Description: E1
Security hardening of implantable cardioverter defibrillators
- Jaffar, Iram, Usman, Muhammad, Jolfaei, Alireza
- Authors: Jaffar, Iram , Usman, Muhammad , Jolfaei, Alireza
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 1173-1178
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- Description: Contemporary healthcare has witnessed a wide deployment of Implantable Cardioverter Defibrillators (ICDs), which have the capability to be controlled remotely, making them equally accessible from both home and hospitals. The therapeutic benefits of ICDs seem to outweigh potential security concerns, yet overlooking the presence of malicious attacks cannot be justified. This study investigates the scenario where an adversary falsifies a controller command and sends instructions to issue high electric shocks in succession. We propose a novel security hardening mechanism to protect data communications between ICD and controller from malicious data manipulations. Our proposed method verifies the correctness of an external command with respect to the history of heart rhythms. The proposed method is evaluated using real data. Multi-aspect analyses show the effectiveness of the proposed scheme.
- Description: Proceedings of the IEEE International Conference on Industrial Technology
- Authors: Jaffar, Iram , Usman, Muhammad , Jolfaei, Alireza
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 1173-1178
- Full Text:
- Reviewed:
- Description: Contemporary healthcare has witnessed a wide deployment of Implantable Cardioverter Defibrillators (ICDs), which have the capability to be controlled remotely, making them equally accessible from both home and hospitals. The therapeutic benefits of ICDs seem to outweigh potential security concerns, yet overlooking the presence of malicious attacks cannot be justified. This study investigates the scenario where an adversary falsifies a controller command and sends instructions to issue high electric shocks in succession. We propose a novel security hardening mechanism to protect data communications between ICD and controller from malicious data manipulations. Our proposed method verifies the correctness of an external command with respect to the history of heart rhythms. The proposed method is evaluated using real data. Multi-aspect analyses show the effectiveness of the proposed scheme.
- Description: Proceedings of the IEEE International Conference on Industrial Technology
Vulnerability modelling for hybrid IT systems
- Ur-Rehman, Attiq, Gondal, Iqbal, Kamruzzuman, Joarder, Jolfaei, Alireza
- Authors: Ur-Rehman, Attiq , Gondal, Iqbal , Kamruzzuman, Joarder , Jolfaei, Alireza
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 1186-1191
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- Description: Common vulnerability scoring system (CVSS) is an industry standard that can assess the vulnerability of nodes in traditional computer systems. The metrics computed by CVSS would determine critical nodes and attack paths. However, traditional IT security models would not fit IoT embedded networks due to distinct nature and unique characteristics of IoT systems. This paper analyses the application of CVSS for IoT embedded systems and proposes an improved vulnerability scoring system based on CVSS v3 framework. The proposed framework, named CVSSIoT, is applied to a realistic IT supply chain system and the results are compared with the actual vulnerabilities from the national vulnerability database. The comparison result validates the proposed model. CVSSIoT is not only effective, simple and capable of vulnerability evaluation for traditional IT system, but also exploits unique characteristics of IoT devices.
- Description: Proceedings of the IEEE International Conference on Industrial Technology
- Authors: Ur-Rehman, Attiq , Gondal, Iqbal , Kamruzzuman, Joarder , Jolfaei, Alireza
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 1186-1191
- Full Text:
- Reviewed:
- Description: Common vulnerability scoring system (CVSS) is an industry standard that can assess the vulnerability of nodes in traditional computer systems. The metrics computed by CVSS would determine critical nodes and attack paths. However, traditional IT security models would not fit IoT embedded networks due to distinct nature and unique characteristics of IoT systems. This paper analyses the application of CVSS for IoT embedded systems and proposes an improved vulnerability scoring system based on CVSS v3 framework. The proposed framework, named CVSSIoT, is applied to a realistic IT supply chain system and the results are compared with the actual vulnerabilities from the national vulnerability database. The comparison result validates the proposed model. CVSSIoT is not only effective, simple and capable of vulnerability evaluation for traditional IT system, but also exploits unique characteristics of IoT devices.
- Description: Proceedings of the IEEE International Conference on Industrial Technology
A novel no-reference subjective quality metric for free viewpoint video using human eye movement
- Podder, Pallab, Paul, Manoranjan, Murshed, Manzur
- Authors: Podder, Pallab , Paul, Manoranjan , Murshed, Manzur
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 8th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2017; Wuhan, China; 20th-24th November 2017; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10749 LNCS, p. 237-251
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- Reviewed:
- Description: The free viewpoint video (FVV) allows users to interactively control the viewpoint and generate new views of a dynamic scene from any 3D position for better 3D visual experience with depth perception. Multiview video coding exploits both texture and depth video information from various angles to encode a number of views to facilitate FVV. The usual practice for the single view or multiview quality assessment is characterized by evolving the objective quality assessment metrics due to their simplicity and real time applications such as the peak signal-to-noise ratio (PSNR) or the structural similarity index (SSIM). However, the PSNR or SSIM requires reference image for quality evaluation and could not be successfully employed in FVV as the new view in FVV does not have any reference view to compare with. Conversely, the widely used subjective estimator- mean opinion score (MOS) is often biased by the testing environment, viewers mode, domain knowledge, and many other factors that may actively influence on actual assessment. To address this limitation, in this work, we devise a no-reference subjective quality assessment metric by simply exploiting the pattern of human eye browsing on FVV. Over different quality contents of FVV, the participants eye-tracker recorded spatio-temporal gaze-data indicate more concentrated eye-traversing approach for relatively better quality. Thus, we calculate the Length, Angle, Pupil-size, and Gaze-duration features from the recorded gaze trajectory. The content and resolution invariant operation is carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric using eye traversal (QMET). Tested results reveal that the proposed QMET performs better than the SSIM and MOS in terms of assessing different aspects of coded video quality for a wide range of FVV contents.
- Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Authors: Podder, Pallab , Paul, Manoranjan , Murshed, Manzur
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 8th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2017; Wuhan, China; 20th-24th November 2017; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10749 LNCS, p. 237-251
- Full Text:
- Reviewed:
- Description: The free viewpoint video (FVV) allows users to interactively control the viewpoint and generate new views of a dynamic scene from any 3D position for better 3D visual experience with depth perception. Multiview video coding exploits both texture and depth video information from various angles to encode a number of views to facilitate FVV. The usual practice for the single view or multiview quality assessment is characterized by evolving the objective quality assessment metrics due to their simplicity and real time applications such as the peak signal-to-noise ratio (PSNR) or the structural similarity index (SSIM). However, the PSNR or SSIM requires reference image for quality evaluation and could not be successfully employed in FVV as the new view in FVV does not have any reference view to compare with. Conversely, the widely used subjective estimator- mean opinion score (MOS) is often biased by the testing environment, viewers mode, domain knowledge, and many other factors that may actively influence on actual assessment. To address this limitation, in this work, we devise a no-reference subjective quality assessment metric by simply exploiting the pattern of human eye browsing on FVV. Over different quality contents of FVV, the participants eye-tracker recorded spatio-temporal gaze-data indicate more concentrated eye-traversing approach for relatively better quality. Thus, we calculate the Length, Angle, Pupil-size, and Gaze-duration features from the recorded gaze trajectory. The content and resolution invariant operation is carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric using eye traversal (QMET). Tested results reveal that the proposed QMET performs better than the SSIM and MOS in terms of assessing different aspects of coded video quality for a wide range of FVV contents.
- Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
An Attention-Based Approach for Single Image Super Resolution
- Liu, Yuan, Wang, Yuancheng, Li, Nan, Cheng, Xu, Zhang, Yifeng, Huang, Yongming, Lu, Guojun
- Authors: Liu, Yuan , Wang, Yuancheng , Li, Nan , Cheng, Xu , Zhang, Yifeng , Huang, Yongming , Lu, Guojun
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 2018 24th International Conference on Pattern Recognition, ICPR 2018; Beijing, China; 20th-24th August 2018 Vol. 2018, p. 2777-2784
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- Description: The main challenge of single image super resolution (SISR) is the recovery of high frequency details such as tiny textures. However, most of the state-of-the-art methods lack specific modules to identify high frequency areas, causing the output image to be blurred. We propose an attention-based approach to give a discrimination between texture areas and smooth areas. After the positions of high frequency details are located, high frequency compensation is carried out. This approach can incorporate with previously proposed SISR networks. By providing high frequency enhancement, better performance and visual effect are achieved. We also propose our own SISR network composed of DenseRes blocks. The block provides an effective way to combine the low level features and high level features. Extensive benchmark evaluation shows that our proposed method achieves significant improvement over the state-of-the-art works in SISR.
- Authors: Liu, Yuan , Wang, Yuancheng , Li, Nan , Cheng, Xu , Zhang, Yifeng , Huang, Yongming , Lu, Guojun
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 2018 24th International Conference on Pattern Recognition, ICPR 2018; Beijing, China; 20th-24th August 2018 Vol. 2018, p. 2777-2784
- Full Text:
- Reviewed:
- Description: The main challenge of single image super resolution (SISR) is the recovery of high frequency details such as tiny textures. However, most of the state-of-the-art methods lack specific modules to identify high frequency areas, causing the output image to be blurred. We propose an attention-based approach to give a discrimination between texture areas and smooth areas. After the positions of high frequency details are located, high frequency compensation is carried out. This approach can incorporate with previously proposed SISR networks. By providing high frequency enhancement, better performance and visual effect are achieved. We also propose our own SISR network composed of DenseRes blocks. The block provides an effective way to combine the low level features and high level features. Extensive benchmark evaluation shows that our proposed method achieves significant improvement over the state-of-the-art works in SISR.
Analysis of Classifiers for Prediction of Type II Diabetes Mellitus
- Barhate, Rahul, Kulkarni, Pradnya
- Authors: Barhate, Rahul , Kulkarni, Pradnya
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 4th International Conference on Computing, Communication Control and Automation, ICCUBEA 2018
- Full Text:
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- Description: Diabetes mellitus is a chronic disease and a health challenge worldwide. According to the International Diabetes Federation, 451 million people across the globe have diabetes, with this number anticipated to rise up to 693 million people by 2045. It has been shown that 80% of the complications arising from type II diabetes can be prevented or delayed by early identification of the people who are at risk. Diabetes is difficult to diagnose in the early stages as its symptoms grow subtly and gradually. In a majority of the cases, the patients remain undiagnosed until they are admitted for a heart attack or begin to lose their sight. This paper analyzes the different classification algorithms based on a patient's health history to aid doctors identify the presence of as well as promote early diagnosis and treatment. The experiments were conducted on Pima Indian Diabetes data set. Various classifiers used include K Nearest Neighbors, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, Support Vector Machine and Neural Network. Results demonstrate that Random Forests performed well on the data set giving an accuracy of 79.7%. © 2018 IEEE.
- Description: E1
- Authors: Barhate, Rahul , Kulkarni, Pradnya
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 4th International Conference on Computing, Communication Control and Automation, ICCUBEA 2018
- Full Text:
- Reviewed:
- Description: Diabetes mellitus is a chronic disease and a health challenge worldwide. According to the International Diabetes Federation, 451 million people across the globe have diabetes, with this number anticipated to rise up to 693 million people by 2045. It has been shown that 80% of the complications arising from type II diabetes can be prevented or delayed by early identification of the people who are at risk. Diabetes is difficult to diagnose in the early stages as its symptoms grow subtly and gradually. In a majority of the cases, the patients remain undiagnosed until they are admitted for a heart attack or begin to lose their sight. This paper analyzes the different classification algorithms based on a patient's health history to aid doctors identify the presence of as well as promote early diagnosis and treatment. The experiments were conducted on Pima Indian Diabetes data set. Various classifiers used include K Nearest Neighbors, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, Support Vector Machine and Neural Network. Results demonstrate that Random Forests performed well on the data set giving an accuracy of 79.7%. © 2018 IEEE.
- Description: E1
Breast density classification for cancer detection using DCT-PCA feature extraction and classifier ensemble
- Haque, Md Sarwar, Hassan, Md Rafiul, BinMakhashen, Galal, Owaidh, Abdullah, Kamruzzaman, Joarder
- Authors: Haque, Md Sarwar , Hassan, Md Rafiul , BinMakhashen, Galal , Owaidh, Abdullah , Kamruzzaman, Joarder
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 17th International Conference on Intelligent Systems Design and Applications, ISDA 2017; Delhi, India; 14th-16th December 2017; published in Intelligent Systems Design and Applications (part of the Advances in Intelligent Systems and Computing book series) Vol. 736, p. 702-711
- Full Text:
- Reviewed:
- Description: It is well known that breast density in mammograms may hinder the accuracy of diagnosis of breast cancer. Although the dense breasts should be processed in a special manner, most of the research has treated dense breast almost the same as fatty. Consequently, the dense tissues in the breast are diagnosed as a developed cancer. In contrast, dense-fatty should be clearly distinguished before the diagnosis of cancerous or not cancerous breast. In this paper, we develop such a system that will automatically analyze mammograms and identify significant features. For feature extraction, we develop a novel system by combining a two-dimensional discrete cosine transform (2D-DCT) and a principal component analysis (PCA) to extract a minimal feature set of mammograms to differentiate breast density. These features are fed to three classifiers: Backpropagation Multilayer Perceptron (MLP), Support Vector Machine (SVM) and K Nearest Neighbour (KNN). A majority voting on the outputs of different machine learning tools is also investigated to enhance the classification performance. The results show that features extracted using a combination of DCT-PCA provide a very high classification performance while using a majority voting of classifiers outputs from MLP, SVM, and KNN.
- Authors: Haque, Md Sarwar , Hassan, Md Rafiul , BinMakhashen, Galal , Owaidh, Abdullah , Kamruzzaman, Joarder
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 17th International Conference on Intelligent Systems Design and Applications, ISDA 2017; Delhi, India; 14th-16th December 2017; published in Intelligent Systems Design and Applications (part of the Advances in Intelligent Systems and Computing book series) Vol. 736, p. 702-711
- Full Text:
- Reviewed:
- Description: It is well known that breast density in mammograms may hinder the accuracy of diagnosis of breast cancer. Although the dense breasts should be processed in a special manner, most of the research has treated dense breast almost the same as fatty. Consequently, the dense tissues in the breast are diagnosed as a developed cancer. In contrast, dense-fatty should be clearly distinguished before the diagnosis of cancerous or not cancerous breast. In this paper, we develop such a system that will automatically analyze mammograms and identify significant features. For feature extraction, we develop a novel system by combining a two-dimensional discrete cosine transform (2D-DCT) and a principal component analysis (PCA) to extract a minimal feature set of mammograms to differentiate breast density. These features are fed to three classifiers: Backpropagation Multilayer Perceptron (MLP), Support Vector Machine (SVM) and K Nearest Neighbour (KNN). A majority voting on the outputs of different machine learning tools is also investigated to enhance the classification performance. The results show that features extracted using a combination of DCT-PCA provide a very high classification performance while using a majority voting of classifiers outputs from MLP, SVM, and KNN.
Carbon negative platform chemicals from waste using enhanced geothermal systems
- Ghayur, Adeel, Verheyen, Vincent
- Authors: Ghayur, Adeel , Verheyen, Vincent
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 14th Greenhouse Gas Control Technologies Conference, GHGT-14; Melbourne, Australian; 21st-26st October 2018 p. 1-4
- Full Text:
- Reviewed:
- Description: Australia has ample geothermal resource, however, it is of low-grade heat and requires Enhanced Geothermal Systems (EGS). Integrating heat recovered via EGS into a lignocellulosic biorefinery opens the avenue for countless opportunities in biomass to products industries. In this study, a biorefinery is modelled that uses heat from a supercritical CO
- Authors: Ghayur, Adeel , Verheyen, Vincent
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 14th Greenhouse Gas Control Technologies Conference, GHGT-14; Melbourne, Australian; 21st-26st October 2018 p. 1-4
- Full Text:
- Reviewed:
- Description: Australia has ample geothermal resource, however, it is of low-grade heat and requires Enhanced Geothermal Systems (EGS). Integrating heat recovered via EGS into a lignocellulosic biorefinery opens the avenue for countless opportunities in biomass to products industries. In this study, a biorefinery is modelled that uses heat from a supercritical CO
Cuboid colour image segmentation using intuitive distance measure
- Tania, Sheikh, Murshed, Manzur, Teng, Shyh, Karmakar, Gour
- Authors: Tania, Sheikh , Murshed, Manzur , Teng, Shyh , Karmakar, Gour
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 2018 International Conference on Image and Vision Computing New Zealand, IVCNZ 2018; Auckland, New Zealand; 19th-21st November 2018 Vol. 2018-November, p. 1-6
- Full Text:
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- Description: In this paper, an improved algorithm for cuboid image segmentation is proposed. To address the two main limitations of the recently proposed cuboid segmentation algorithm, the improved algorithm substitutes colour quantization in HCL colour space with infinity norm distance in RGB colour space along with a different way to impose area thresholding. We also propose a new metric to evaluate the quality of segmentation. Experimental results show that the proposed cuboid segmentation algorithm significantly outperforms the existing cuboid segmentation algorithm in terms of quality of segmentation.
- Description: International Conference Image and Vision Computing New Zealand
- Authors: Tania, Sheikh , Murshed, Manzur , Teng, Shyh , Karmakar, Gour
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 2018 International Conference on Image and Vision Computing New Zealand, IVCNZ 2018; Auckland, New Zealand; 19th-21st November 2018 Vol. 2018-November, p. 1-6
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- Description: In this paper, an improved algorithm for cuboid image segmentation is proposed. To address the two main limitations of the recently proposed cuboid segmentation algorithm, the improved algorithm substitutes colour quantization in HCL colour space with infinity norm distance in RGB colour space along with a different way to impose area thresholding. We also propose a new metric to evaluate the quality of segmentation. Experimental results show that the proposed cuboid segmentation algorithm significantly outperforms the existing cuboid segmentation algorithm in terms of quality of segmentation.
- Description: International Conference Image and Vision Computing New Zealand
Data exchange in delay tolerant networks using joint inter- and intra-flow network coding
- Ostovari, Pouya, Wu, Jie, Jolfaei, Alireza
- Authors: Ostovari, Pouya , Wu, Jie , Jolfaei, Alireza
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 37th IEEE International Performance Computing and Communications Conference, IPCCC 2018; Orlando, United States; 17th-19th November 2018 p. 1-8
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- Description: Data transmission in delay tolerant networks (DTNs) is a challenging problem due to the lack of continuous network connectivity and nondeterministic mobility of the nodes. Epidemic routing and spray-and-wait methods are two popular mechanisms that are proposed for DTNs. In order to reduce the transmission delay in DTNs, some previous works combine intra-flow network coding with the routing protocols. In this paper, we propose two routing mechanisms using systematic joint inter- and intra-flow network coding for the purpose of data exchange between the nodes. We discuss the reasons why inter-flow network coding helps to reduce the delivery delay of the packets, and we also analyze the delays related with only using intra-flow coding, and joint inter- and intra-flow coding methods. We empirically show the benefit of joint coding over just intra-flow coding. Based on our simulation, joint coding can reduce the delay up to 40%, compared to only intra-flow coding.
- Description: 2018 IEEE 37th International Performance Computing and Communications Conference, IPCCC 2018
- Authors: Ostovari, Pouya , Wu, Jie , Jolfaei, Alireza
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 37th IEEE International Performance Computing and Communications Conference, IPCCC 2018; Orlando, United States; 17th-19th November 2018 p. 1-8
- Full Text:
- Reviewed:
- Description: Data transmission in delay tolerant networks (DTNs) is a challenging problem due to the lack of continuous network connectivity and nondeterministic mobility of the nodes. Epidemic routing and spray-and-wait methods are two popular mechanisms that are proposed for DTNs. In order to reduce the transmission delay in DTNs, some previous works combine intra-flow network coding with the routing protocols. In this paper, we propose two routing mechanisms using systematic joint inter- and intra-flow network coding for the purpose of data exchange between the nodes. We discuss the reasons why inter-flow network coding helps to reduce the delivery delay of the packets, and we also analyze the delays related with only using intra-flow coding, and joint inter- and intra-flow coding methods. We empirically show the benefit of joint coding over just intra-flow coding. Based on our simulation, joint coding can reduce the delay up to 40%, compared to only intra-flow coding.
- Description: 2018 IEEE 37th International Performance Computing and Communications Conference, IPCCC 2018
Decision support tools for preventive maintenance intervals and replacement decisions of engineering assets
- Menon, M., Chattopadhyay, Gopinath, Beebe, Raymond
- Authors: Menon, M. , Chattopadhyay, Gopinath , Beebe, Raymond
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018; Bangkok, Thailand; 16th-19th December 2018 Vol. 2019-December, p. 257-261
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- Description: Prognostic models for maintenance decisions have inherent limitations due to quality quantity of historical data, assumptions made, and time required in validating models. In this paper, Preventive Maintenance (PM) Intervals, Failure events, cost and maintenance records from Computerized Maintenance Management System (CMMS) are analyzed for reducing downtimes and Operating Expenditure (OPEX). The proposed methodologies for maintenance intervals and replacements with acceptable level of confidence are articulated to asset maintenance of a City Council of Australian Local Government organisation as a case of improved decision making under limited information.
- Description: IEEE International Conference on Industrial Engineering and Engineering Management
- Authors: Menon, M. , Chattopadhyay, Gopinath , Beebe, Raymond
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018; Bangkok, Thailand; 16th-19th December 2018 Vol. 2019-December, p. 257-261
- Full Text:
- Reviewed:
- Description: Prognostic models for maintenance decisions have inherent limitations due to quality quantity of historical data, assumptions made, and time required in validating models. In this paper, Preventive Maintenance (PM) Intervals, Failure events, cost and maintenance records from Computerized Maintenance Management System (CMMS) are analyzed for reducing downtimes and Operating Expenditure (OPEX). The proposed methodologies for maintenance intervals and replacements with acceptable level of confidence are articulated to asset maintenance of a City Council of Australian Local Government organisation as a case of improved decision making under limited information.
- Description: IEEE International Conference on Industrial Engineering and Engineering Management
Detecting splicing and copy-move attacks in color images
- Islam, Mohammad, Karmakar, Gour, Kamruzzaman, Joarder, Murshed, Manzur, Kahandawa, Gayan, Parvin, Nahida
- Authors: Islam, Mohammad , Karmakar, Gour , Kamruzzaman, Joarder , Murshed, Manzur , Kahandawa, Gayan , Parvin, Nahida
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018; Canberra, Australia; 10th-13th December 2018 p. 1-7
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- Description: Image sensors are generating limitless digital images every day. Image forgery like splicing and copy-move are very common type of attacks that are easy to execute using sophisticated photo editing tools. As a result, digital forensics has attracted much attention to identify such tampering on digital images. In this paper, a passive (blind) image tampering identification method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) has been proposed. First, the chroma components of an image is divided into fixed sized non-overlapping blocks and 2D block DCT is applied to identify the changes due to forgery in local frequency distribution of the image. Then a texture descriptor, LBP is applied on the magnitude component of the 2D-DCT array to enhance the artifacts introduced by the tampering operation. The resulting LBP image is again divided into non-overlapping blocks. Finally, summations of corresponding inter-cell values of all the LBP blocks are computed and arranged as a feature vector. These features are fed into a Support Vector Machine (SVM) with Radial Basis Function (RBF) as kernel to distinguish forged images from authentic ones. The proposed method has been experimented extensively on three publicly available well-known image splicing and copy-move detection benchmark datasets of color images. Results demonstrate the superiority of the proposed method over recently proposed state-of-the-art approaches in terms of well accepted performance metrics such as accuracy, area under ROC curve and others.
- Description: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
- Authors: Islam, Mohammad , Karmakar, Gour , Kamruzzaman, Joarder , Murshed, Manzur , Kahandawa, Gayan , Parvin, Nahida
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018; Canberra, Australia; 10th-13th December 2018 p. 1-7
- Full Text:
- Reviewed:
- Description: Image sensors are generating limitless digital images every day. Image forgery like splicing and copy-move are very common type of attacks that are easy to execute using sophisticated photo editing tools. As a result, digital forensics has attracted much attention to identify such tampering on digital images. In this paper, a passive (blind) image tampering identification method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) has been proposed. First, the chroma components of an image is divided into fixed sized non-overlapping blocks and 2D block DCT is applied to identify the changes due to forgery in local frequency distribution of the image. Then a texture descriptor, LBP is applied on the magnitude component of the 2D-DCT array to enhance the artifacts introduced by the tampering operation. The resulting LBP image is again divided into non-overlapping blocks. Finally, summations of corresponding inter-cell values of all the LBP blocks are computed and arranged as a feature vector. These features are fed into a Support Vector Machine (SVM) with Radial Basis Function (RBF) as kernel to distinguish forged images from authentic ones. The proposed method has been experimented extensively on three publicly available well-known image splicing and copy-move detection benchmark datasets of color images. Results demonstrate the superiority of the proposed method over recently proposed state-of-the-art approaches in terms of well accepted performance metrics such as accuracy, area under ROC curve and others.
- Description: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
Enhanced colour image retrieval with cuboid segmentation
- Murshed, Manzur, Karmakar, Priyabrata, Teng, Shyh, Lu, Guojun
- Authors: Murshed, Manzur , Karmakar, Priyabrata , Teng, Shyh , Lu, Guojun
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018; Canberra, Australia; 10th-13th December 2018
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- Description: In this paper, we further investigate our recently proposed cuboid image segmentation algorithm for effective image retrieval. Instead of using all cuboids (i.e. segments), we have proposed two approaches to choose different subsets of cuboids appropriately. With the experimental results on eBay dataset, we have shown that our proposals outperform retrieval performance of the existing technique. In addition, we have investigated how many segments are required for the most effective image retrieval and provide a quick method to determine the suitable number of cuboids.
- Description: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
- Authors: Murshed, Manzur , Karmakar, Priyabrata , Teng, Shyh , Lu, Guojun
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018; Canberra, Australia; 10th-13th December 2018
- Full Text:
- Reviewed:
- Description: In this paper, we further investigate our recently proposed cuboid image segmentation algorithm for effective image retrieval. Instead of using all cuboids (i.e. segments), we have proposed two approaches to choose different subsets of cuboids appropriately. With the experimental results on eBay dataset, we have shown that our proposals outperform retrieval performance of the existing technique. In addition, we have investigated how many segments are required for the most effective image retrieval and provide a quick method to determine the suitable number of cuboids.
- Description: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
Evaluating Universal Design in Built Environments – A Scoping Project
- Watchorn,Valerie, Grant, Cathryn, Tucker,Richard, Hitch, Danielle, Frawley, Patsie, Ang, Susan, Aedy, Kathryn, Gohil, Apeksha
- Authors: Watchorn,Valerie , Grant, Cathryn , Tucker,Richard , Hitch, Danielle , Frawley, Patsie , Ang, Susan , Aedy, Kathryn , Gohil, Apeksha
- Date: 2018
- Type: Text , Conference proceedings
- Relation: Universal Design and Higher Education in Transformation Congress 2018;Dublin Castle, Ireland; 30th October to 2nd November, 2018 Volume 256: Transforming our World Through Design, Diversity and Education p. 689-695
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- Description: This project aimed to scope existing methods of evaluating the application of universal design to built environments and to explore relevant knowledge of key stakeholders, such as architects, access consultants and people who experience disability. The project commenced in 2017. Ethics approval was gained and a mixed methods approach was employed. Methods of data collection included electronic survey and in-depth interview. Early survey findings are reported in this paper. A descriptive approach was used to analyse quantitative data. A total of 157 survey responses were received from across Australia (83%) and internationally (16.6%). Preliminary findings indicate that most survey respondents (72%) had been involved in the process of applying universal design to the design of built environments. Although evaluating the application of universal design was rated as “extremely important” by 85% of respondents, only 36% had such experience. Of these, 74% had used specific tools for this purpose. Non-standardised checklists and access audits were the most frequently used and preferred tools. Overall, stakeholders perceived themselves to have ‘some knowledge’ on universal design theory and application. This project offers insight into how universal design is understood and applied to the design of built environments. Findings suggest that evaluation is less common than application and that there is a need to strengthen existing methods of evaluation to provide greater detail on universal design processes and outcomes.
- Authors: Watchorn,Valerie , Grant, Cathryn , Tucker,Richard , Hitch, Danielle , Frawley, Patsie , Ang, Susan , Aedy, Kathryn , Gohil, Apeksha
- Date: 2018
- Type: Text , Conference proceedings
- Relation: Universal Design and Higher Education in Transformation Congress 2018;Dublin Castle, Ireland; 30th October to 2nd November, 2018 Volume 256: Transforming our World Through Design, Diversity and Education p. 689-695
- Full Text:
- Reviewed:
- Description: This project aimed to scope existing methods of evaluating the application of universal design to built environments and to explore relevant knowledge of key stakeholders, such as architects, access consultants and people who experience disability. The project commenced in 2017. Ethics approval was gained and a mixed methods approach was employed. Methods of data collection included electronic survey and in-depth interview. Early survey findings are reported in this paper. A descriptive approach was used to analyse quantitative data. A total of 157 survey responses were received from across Australia (83%) and internationally (16.6%). Preliminary findings indicate that most survey respondents (72%) had been involved in the process of applying universal design to the design of built environments. Although evaluating the application of universal design was rated as “extremely important” by 85% of respondents, only 36% had such experience. Of these, 74% had used specific tools for this purpose. Non-standardised checklists and access audits were the most frequently used and preferred tools. Overall, stakeholders perceived themselves to have ‘some knowledge’ on universal design theory and application. This project offers insight into how universal design is understood and applied to the design of built environments. Findings suggest that evaluation is less common than application and that there is a need to strengthen existing methods of evaluation to provide greater detail on universal design processes and outcomes.
Experimental evaluation of methods for reclaiming sulfur loaded amine absorbents
- Garg, Bharti, Pearson, Pauline, Cousins, Ashleigh, Verheyen, Vincent, Puxty, Graeme, Feron, Paul
- Authors: Garg, Bharti , Pearson, Pauline , Cousins, Ashleigh , Verheyen, Vincent , Puxty, Graeme , Feron, Paul
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 14th Greenhouse Gas Control Technologies Conference (GHGT-14); Melbourne, Australia; 21st-26th October 2018 p. 1-8
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- Description: Sulfur dioxide (SO2) is a major flue gas contaminant that has a direct effect on the performance of amine-based carbon dioxide capture units operating on power plant flue gases. In many countries, flue gas desulfurisation (FGD) is an essential upstream requirement to CO2 capture systems, thereby increasing the overall operational and capital cost of the capture system. In Australia, the efficacy of CO2 capture may be compromised by the accumulation of SO2 in the absorption solvent. CSIRO’s CS-Cap process is designed to capture of both these acidic gases in one absorption column, thereby eliminating the need for a separate FGD unit which could potentially save millions of dollars. Previous research at CSIRO’s post-combustion capture pilot plant at Loy Yang power station has shown that mono-ethanolamine (MEA) solvent absorbs both CO2 and SO2, resulting in a spent amine absorbent rich in sulfates. Further development of the CS-Cap concept requires a deeper understanding of the properties of the sulfate-rich absorbent and the conditions under which it can be effectively regenerated. In the present study, thermal reclamation and reactive crystallisation processes were investigated, allowing the parameters affecting the regeneration of sulfate-loaded amine to be identified. It was found that amine losses were considerably higher in thermal reclamation than in reactive precipitation. During thermal reclamation, vacuum conditions were more effective than atmospheric, and pH of the initial solution played a significant role in recovery of MEA from the sulfate-rich absorbent. Reactive crystallisation could be effectively accomplished with the addition of KOH. An advantage of this process was that high purity K2SO4 crystals (~99%) were formed, despite the presence of degradation products in the solvent.
- Authors: Garg, Bharti , Pearson, Pauline , Cousins, Ashleigh , Verheyen, Vincent , Puxty, Graeme , Feron, Paul
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 14th Greenhouse Gas Control Technologies Conference (GHGT-14); Melbourne, Australia; 21st-26th October 2018 p. 1-8
- Full Text:
- Reviewed:
- Description: Sulfur dioxide (SO2) is a major flue gas contaminant that has a direct effect on the performance of amine-based carbon dioxide capture units operating on power plant flue gases. In many countries, flue gas desulfurisation (FGD) is an essential upstream requirement to CO2 capture systems, thereby increasing the overall operational and capital cost of the capture system. In Australia, the efficacy of CO2 capture may be compromised by the accumulation of SO2 in the absorption solvent. CSIRO’s CS-Cap process is designed to capture of both these acidic gases in one absorption column, thereby eliminating the need for a separate FGD unit which could potentially save millions of dollars. Previous research at CSIRO’s post-combustion capture pilot plant at Loy Yang power station has shown that mono-ethanolamine (MEA) solvent absorbs both CO2 and SO2, resulting in a spent amine absorbent rich in sulfates. Further development of the CS-Cap concept requires a deeper understanding of the properties of the sulfate-rich absorbent and the conditions under which it can be effectively regenerated. In the present study, thermal reclamation and reactive crystallisation processes were investigated, allowing the parameters affecting the regeneration of sulfate-loaded amine to be identified. It was found that amine losses were considerably higher in thermal reclamation than in reactive precipitation. During thermal reclamation, vacuum conditions were more effective than atmospheric, and pH of the initial solution played a significant role in recovery of MEA from the sulfate-rich absorbent. Reactive crystallisation could be effectively accomplished with the addition of KOH. An advantage of this process was that high purity K2SO4 crystals (~99%) were formed, despite the presence of degradation products in the solvent.
Exploiting user provided information in dynamic consolidation of virtual machines to minimize energy consumption of cloud data centers
- Khan, Anit, Paplinski, Andrew, Khan, Abdul, Murshed, Manzur, Buyya, Rajkumar
- Authors: Khan, Anit , Paplinski, Andrew , Khan, Abdul , Murshed, Manzur , Buyya, Rajkumar
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 3rd International Conference on Fog and Mobile Edge Computing, FMEC 2018; Barcelona, Spain; 23rd-26th April 2018; p. 105-114
- Full Text:
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- Description: Dynamic consolidation of Virtual Machines (VMs) can effectively enhance the resource utilization and energy-efficiency of the Cloud Data Centers (CDC). Existing research on Cloud resource reservation and scheduling signify that Cloud Service Users (CSUs) can play a crucial role in improving the resource utilization by providing valuable information to Cloud service providers. However, utilization of CSUs' provided information in minimization of energy consumption of CDC is a novel research direction. The challenges herein are twofold. First, finding the right benign information to be received from a CSU which can complement the energy-efficiency of CDC. Second, smart application of such information to significantly reduce the energy consumption of CDC. To address those research challenges, we have proposed a novel heuristic Dynamic VM Consolidation algorithm, RTDVMC, which minimizes the energy consumption of CDC through exploiting CSU provided information. Our research exemplifies the fact that if VMs are dynamically consolidated based on the time when a VM can be removed from CDC-a useful information to be received from respective CSU, then more physical machines can be turned into sleep state, yielding lower energy consumption. We have simulated the performance of RTDVMC with real Cloud workload traces originated from more than 800 PlanetLab VMs. The empirical figures affirm the superiority of RTDVMC over existing prominent Static and Adaptive Threshold based DVMC algorithms.
- Authors: Khan, Anit , Paplinski, Andrew , Khan, Abdul , Murshed, Manzur , Buyya, Rajkumar
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 3rd International Conference on Fog and Mobile Edge Computing, FMEC 2018; Barcelona, Spain; 23rd-26th April 2018; p. 105-114
- Full Text:
- Reviewed:
- Description: Dynamic consolidation of Virtual Machines (VMs) can effectively enhance the resource utilization and energy-efficiency of the Cloud Data Centers (CDC). Existing research on Cloud resource reservation and scheduling signify that Cloud Service Users (CSUs) can play a crucial role in improving the resource utilization by providing valuable information to Cloud service providers. However, utilization of CSUs' provided information in minimization of energy consumption of CDC is a novel research direction. The challenges herein are twofold. First, finding the right benign information to be received from a CSU which can complement the energy-efficiency of CDC. Second, smart application of such information to significantly reduce the energy consumption of CDC. To address those research challenges, we have proposed a novel heuristic Dynamic VM Consolidation algorithm, RTDVMC, which minimizes the energy consumption of CDC through exploiting CSU provided information. Our research exemplifies the fact that if VMs are dynamically consolidated based on the time when a VM can be removed from CDC-a useful information to be received from respective CSU, then more physical machines can be turned into sleep state, yielding lower energy consumption. We have simulated the performance of RTDVMC with real Cloud workload traces originated from more than 800 PlanetLab VMs. The empirical figures affirm the superiority of RTDVMC over existing prominent Static and Adaptive Threshold based DVMC algorithms.