A new global index for short term voltage stability assessment
- Alshareef, Abdulrhman, Shah, Rakibuzzaman, Mithulananthan, Nadarajah, Alzahrani, Saeed
- Authors: Alshareef, Abdulrhman , Shah, Rakibuzzaman , Mithulananthan, Nadarajah , Alzahrani, Saeed
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 36114-36124
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- Description: The utility scale of non-conventional generators (NCGs), such as wind and photovoltaic (PV) plants, are competitive alternatives to synchronous machines (SMs) for power generation. Higher penetration of NCGs has been respondent of causing several recent incidents leading up to voltage collapse in power systems due to the distinct characteristics of NCGs under different operating conditions. Consequently, the so-called system strength has been reduced with higher NCGs penetration. A number of indices have been developed to quantify system strength from the short-term voltage stability (STVS) perspective. None of the indices capture the overall performances of power systems on dynamic voltage recovery. In this paper, an improvement in one of the STVS indices namely, the Voltage Recovery Index (VRI), is proposed to overcome shortcomings in the original index. Moreover, the improved index is globalized to establish a new index defined as system voltage recovery index (VRIsys) to quantify STVS at the system level. The amended VRI and developed VRIsys are used in systematic simulations to quantify the impact and interaction of various factors that could affect system strength. The assessment was conducted using time-domain simulation with direct connected induction motors (DCIMs) and a proliferation of converter-based technologies on both the generation and load sides, namely, NCGs and Variable Speed Drives (VSDs), respectively. © 2013 IEEE.
- Authors: Alshareef, Abdulrhman , Shah, Rakibuzzaman , Mithulananthan, Nadarajah , Alzahrani, Saeed
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 36114-36124
- Full Text:
- Reviewed:
- Description: The utility scale of non-conventional generators (NCGs), such as wind and photovoltaic (PV) plants, are competitive alternatives to synchronous machines (SMs) for power generation. Higher penetration of NCGs has been respondent of causing several recent incidents leading up to voltage collapse in power systems due to the distinct characteristics of NCGs under different operating conditions. Consequently, the so-called system strength has been reduced with higher NCGs penetration. A number of indices have been developed to quantify system strength from the short-term voltage stability (STVS) perspective. None of the indices capture the overall performances of power systems on dynamic voltage recovery. In this paper, an improvement in one of the STVS indices namely, the Voltage Recovery Index (VRI), is proposed to overcome shortcomings in the original index. Moreover, the improved index is globalized to establish a new index defined as system voltage recovery index (VRIsys) to quantify STVS at the system level. The amended VRI and developed VRIsys are used in systematic simulations to quantify the impact and interaction of various factors that could affect system strength. The assessment was conducted using time-domain simulation with direct connected induction motors (DCIMs) and a proliferation of converter-based technologies on both the generation and load sides, namely, NCGs and Variable Speed Drives (VSDs), respectively. © 2013 IEEE.
Adversarial network with multiple classifiers for open set domain adaptation
- Shermin, Tasfia, Lu, Guojun, Teng, Shyh, Murshed, Manzur, Sohel, Ferdous
- Authors: Shermin, Tasfia , Lu, Guojun , Teng, Shyh , Murshed, Manzur , Sohel, Ferdous
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Multimedia Vol. 23, no. (2021), p. 2732-2744
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- Description: Domain adaptation aims to transfer knowledge from a domain with adequate labeled samples to a domain with scarce labeled samples. Prior research has introduced various open set domain adaptation settings in the literature to extend the applications of domain adaptation methods in real-world scenarios. This paper focuses on the type of open set domain adaptation setting where the target domain has both private ('unknown classes') label space and the shared ('known classes') label space. However, the source domain only has the 'known classes' label space. Prevalent distribution-matching domain adaptation methods are inadequate in such a setting that demands adaptation from a smaller source domain to a larger and diverse target domain with more classes. For addressing this specific open set domain adaptation setting, prior research introduces a domain adversarial model that uses a fixed threshold for distinguishing known from unknown target samples and lacks at handling negative transfers. We extend their adversarial model and propose a novel adversarial domain adaptation model with multiple auxiliary classifiers. The proposed multi-classifier structure introduces a weighting module that evaluates distinctive domain characteristics for assigning the target samples with weights which are more representative to whether they are likely to belong to the known and unknown classes to encourage positive transfers during adversarial training and simultaneously reduces the domain gap between the shared classes of the source and target domains. A thorough experimental investigation shows that our proposed method outperforms existing domain adaptation methods on a number of domain adaptation datasets. © 1999-2012 IEEE.
- Authors: Shermin, Tasfia , Lu, Guojun , Teng, Shyh , Murshed, Manzur , Sohel, Ferdous
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Multimedia Vol. 23, no. (2021), p. 2732-2744
- Full Text:
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- Description: Domain adaptation aims to transfer knowledge from a domain with adequate labeled samples to a domain with scarce labeled samples. Prior research has introduced various open set domain adaptation settings in the literature to extend the applications of domain adaptation methods in real-world scenarios. This paper focuses on the type of open set domain adaptation setting where the target domain has both private ('unknown classes') label space and the shared ('known classes') label space. However, the source domain only has the 'known classes' label space. Prevalent distribution-matching domain adaptation methods are inadequate in such a setting that demands adaptation from a smaller source domain to a larger and diverse target domain with more classes. For addressing this specific open set domain adaptation setting, prior research introduces a domain adversarial model that uses a fixed threshold for distinguishing known from unknown target samples and lacks at handling negative transfers. We extend their adversarial model and propose a novel adversarial domain adaptation model with multiple auxiliary classifiers. The proposed multi-classifier structure introduces a weighting module that evaluates distinctive domain characteristics for assigning the target samples with weights which are more representative to whether they are likely to belong to the known and unknown classes to encourage positive transfers during adversarial training and simultaneously reduces the domain gap between the shared classes of the source and target domains. A thorough experimental investigation shows that our proposed method outperforms existing domain adaptation methods on a number of domain adaptation datasets. © 1999-2012 IEEE.
AI and IoT-Enabled smart exoskeleton system for rehabilitation of paralyzed people in connected communities
- Jacob, Sunil, Alagirisamy, Mukil, Xi, Chen, Balasubramanian, Venki, Srinivasan, Ram
- Authors: Jacob, Sunil , Alagirisamy, Mukil , Xi, Chen , Balasubramanian, Venki , Srinivasan, Ram
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 80340-80350
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- Description: In recent years, the number of cases of spinal cord injuries, stroke and other nervous impairments have led to an increase in the number of paralyzed patients worldwide. Rehabilitation that can aid and enhance the lives of such patients is the need of the hour. Exoskeletons have been found as one of the popular means of rehabilitation. The existing exoskeletons use techniques that impose limitations on adaptability, instant response and continuous control. Also most of them are expensive, bulky, and requires high level of training. To overcome all the above limitations, this paper introduces an Artificial Intelligence (AI) powered Smart and light weight Exoskeleton System (AI-IoT-SES) which receives data from various sensors, classifies them intelligently and generates the desired commands via Internet of Things (IoT) for rendering rehabilitation and support with the help of caretakers for paralyzed patients in smart and connected communities. In the proposed system, the signals collected from the exoskeleton sensors are processed using AI-assisted navigation module, and helps the caretakers in guiding, communicating and controlling the movements of the exoskeleton integrated to the patients. The navigation module uses AI and IoT enabled Simultaneous Localization and Mapping (SLAM). The casualties of a paralyzed person are reduced by commissioning the IoT platform to exchange data from the intelligent sensors with the remote location of the caretaker to monitor the real time movement and navigation of the exoskeleton. The automated exoskeleton detects and take decisions on navigation thereby improving the life conditions of such patients. The experimental results simulated using MATLAB shows that the proposed system is the ideal method for rendering rehabilitation and support for paralyzed patients in smart communities. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**
- Authors: Jacob, Sunil , Alagirisamy, Mukil , Xi, Chen , Balasubramanian, Venki , Srinivasan, Ram
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 80340-80350
- Full Text:
- Reviewed:
- Description: In recent years, the number of cases of spinal cord injuries, stroke and other nervous impairments have led to an increase in the number of paralyzed patients worldwide. Rehabilitation that can aid and enhance the lives of such patients is the need of the hour. Exoskeletons have been found as one of the popular means of rehabilitation. The existing exoskeletons use techniques that impose limitations on adaptability, instant response and continuous control. Also most of them are expensive, bulky, and requires high level of training. To overcome all the above limitations, this paper introduces an Artificial Intelligence (AI) powered Smart and light weight Exoskeleton System (AI-IoT-SES) which receives data from various sensors, classifies them intelligently and generates the desired commands via Internet of Things (IoT) for rendering rehabilitation and support with the help of caretakers for paralyzed patients in smart and connected communities. In the proposed system, the signals collected from the exoskeleton sensors are processed using AI-assisted navigation module, and helps the caretakers in guiding, communicating and controlling the movements of the exoskeleton integrated to the patients. The navigation module uses AI and IoT enabled Simultaneous Localization and Mapping (SLAM). The casualties of a paralyzed person are reduced by commissioning the IoT platform to exchange data from the intelligent sensors with the remote location of the caretaker to monitor the real time movement and navigation of the exoskeleton. The automated exoskeleton detects and take decisions on navigation thereby improving the life conditions of such patients. The experimental results simulated using MATLAB shows that the proposed system is the ideal method for rendering rehabilitation and support for paralyzed patients in smart communities. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**
Canonical duality theory and algorithm for solving bilevel knapsack problems with applications
- Authors: Gao, David
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Systems, Man, and Cybernetics: Systems Vol. 51, no. 2 (2021), p. 893-904
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- Description: A novel canonical duality theory (CDT) is presented for solving general bilevel mixed integer nonlinear optimization governed by linear and quadratic knapsack problems. It shows that the challenging knapsack problems can be solved analytically in term of their canonical dual solutions. The existence and uniqueness of these analytical solutions are proved. NP-hardness of the knapsack problems is discussed. A powerful CDT algorithm combined with an alternative iteration and a volume reduction method is proposed for solving the NP-hard bilevel knapsack problems. Application is illustrated by benchmark problems in optimal topology design. The performance and novelty of the proposed method are compared with the popular commercial codes. © 2013 IEEE.
- Authors: Gao, David
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Systems, Man, and Cybernetics: Systems Vol. 51, no. 2 (2021), p. 893-904
- Full Text:
- Reviewed:
- Description: A novel canonical duality theory (CDT) is presented for solving general bilevel mixed integer nonlinear optimization governed by linear and quadratic knapsack problems. It shows that the challenging knapsack problems can be solved analytically in term of their canonical dual solutions. The existence and uniqueness of these analytical solutions are proved. NP-hardness of the knapsack problems is discussed. A powerful CDT algorithm combined with an alternative iteration and a volume reduction method is proposed for solving the NP-hard bilevel knapsack problems. Application is illustrated by benchmark problems in optimal topology design. The performance and novelty of the proposed method are compared with the popular commercial codes. © 2013 IEEE.
Efficient high-resolution video compression scheme using background and foreground layers
- Afsana, Fariha, Paul, Manoranjan, Murshed, Manzur, Taubman, David
- Authors: Afsana, Fariha , Paul, Manoranjan , Murshed, Manzur , Taubman, David
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 157411-157421
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- Description: Video coding using dynamic background frame achieves better compression compared to the traditional techniques by encoding background and foreground separately. This process reduces coding bits for the overall frame significantly; however, encoding background still requires many bits that can be compressed further for achieving better coding efficiency. The cuboid coding framework has been proven to be one of the most effective methods of image compression which exploits homogeneous pixel correlation within a frame and has better alignment with object boundary compared to traditional block-based coding. In a video sequence, the cuboid-based frame partitioning varies with the changes of the foreground. However, since the background remains static for a group of pictures, the cuboid coding exploits better spatial pixel homogeneity. In this work, the impact of cuboid coding on the background frame for high-resolution videos (Ultra-High-Definition (UHD) and 360-degree videos) is investigated using the multilayer framework of SHVC. After the cuboid partitioning, the method of coarse frame generation has been improved with a novel idea by keeping human-visual sensitive information. Unlike the traditional SHVC scheme, in the proposed method, cuboid coded background and the foreground are encoded in separate layers in an implicit manner. Simulation results show that the proposed video coding method achieves an average BD-Rate reduction of 26.69% and BD-PSNR gain of 1.51 dB against SHVC with significant encoding time reduction for both UHD and 360 videos. It also achieves an average of 13.88% BD-Rate reduction and 0.78 dB BD-PSNR gain compared to the existing relevant method proposed by X. Hoang Van. © 2013 IEEE.
- Authors: Afsana, Fariha , Paul, Manoranjan , Murshed, Manzur , Taubman, David
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 157411-157421
- Full Text:
- Reviewed:
- Description: Video coding using dynamic background frame achieves better compression compared to the traditional techniques by encoding background and foreground separately. This process reduces coding bits for the overall frame significantly; however, encoding background still requires many bits that can be compressed further for achieving better coding efficiency. The cuboid coding framework has been proven to be one of the most effective methods of image compression which exploits homogeneous pixel correlation within a frame and has better alignment with object boundary compared to traditional block-based coding. In a video sequence, the cuboid-based frame partitioning varies with the changes of the foreground. However, since the background remains static for a group of pictures, the cuboid coding exploits better spatial pixel homogeneity. In this work, the impact of cuboid coding on the background frame for high-resolution videos (Ultra-High-Definition (UHD) and 360-degree videos) is investigated using the multilayer framework of SHVC. After the cuboid partitioning, the method of coarse frame generation has been improved with a novel idea by keeping human-visual sensitive information. Unlike the traditional SHVC scheme, in the proposed method, cuboid coded background and the foreground are encoded in separate layers in an implicit manner. Simulation results show that the proposed video coding method achieves an average BD-Rate reduction of 26.69% and BD-PSNR gain of 1.51 dB against SHVC with significant encoding time reduction for both UHD and 360 videos. It also achieves an average of 13.88% BD-Rate reduction and 0.78 dB BD-PSNR gain compared to the existing relevant method proposed by X. Hoang Van. © 2013 IEEE.
Examination of effective VAr with respect to dynamic voltage stability in renewable rich power grids
- Alzahrani, Saeed, Shah, Rakibuzzaman, Mithulananthan, N.
- Authors: Alzahrani, Saeed , Shah, Rakibuzzaman , Mithulananthan, N.
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 75494-75508
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- Description: High penetrations of inverter-based renewable resources (IBRs) diminish the resilience that traditional power systems had due to constant research and developments for many years. In particular, dynamic voltage stability becomes one of the major concerns for transmission system operators due to the limited capabilities of IBRs (i.e., voltage and frequency regulation). A heavily loaded renewable-rich network is susceptible to fault-induced delayed voltage recovery (FIDVR) due to insufficient effective reactive power (E-VAr) in power grids. Hence, it is crucial to thoroughly scrutinize each VAr resources' participation in E-VAr under various operating conditions. Moreover, it is essential to investigate the influence of E-VAr on system post-fault performance. The E-VAr investigation would help in determining the optimal location and sizing of grid-connected IBRs and allow more renewable energy integration. Furthermore, it would enrich decision-making about adopting additional grid support devices. In this paper, a comprehensive assessment framework is utilized to assess the E-VAr of a power system with a large-scale photovoltaic power. Plant under different realistic operating conditions. Several indices quantifying the contribution of VAr resources and load bus voltage recovery assists to explore the transient response and voltage trajectories. The recovery indices help have a better understanding of the factors affecting E-VAr. The proposed framework has been tested in the New England (IEEE 39 bus system) through simulation by DIgSILENT Power Factory. © 2013 IEEE.
Examination of effective VAr with respect to dynamic voltage stability in renewable rich power grids
- Authors: Alzahrani, Saeed , Shah, Rakibuzzaman , Mithulananthan, N.
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 75494-75508
- Full Text:
- Reviewed:
- Description: High penetrations of inverter-based renewable resources (IBRs) diminish the resilience that traditional power systems had due to constant research and developments for many years. In particular, dynamic voltage stability becomes one of the major concerns for transmission system operators due to the limited capabilities of IBRs (i.e., voltage and frequency regulation). A heavily loaded renewable-rich network is susceptible to fault-induced delayed voltage recovery (FIDVR) due to insufficient effective reactive power (E-VAr) in power grids. Hence, it is crucial to thoroughly scrutinize each VAr resources' participation in E-VAr under various operating conditions. Moreover, it is essential to investigate the influence of E-VAr on system post-fault performance. The E-VAr investigation would help in determining the optimal location and sizing of grid-connected IBRs and allow more renewable energy integration. Furthermore, it would enrich decision-making about adopting additional grid support devices. In this paper, a comprehensive assessment framework is utilized to assess the E-VAr of a power system with a large-scale photovoltaic power. Plant under different realistic operating conditions. Several indices quantifying the contribution of VAr resources and load bus voltage recovery assists to explore the transient response and voltage trajectories. The recovery indices help have a better understanding of the factors affecting E-VAr. The proposed framework has been tested in the New England (IEEE 39 bus system) through simulation by DIgSILENT Power Factory. © 2013 IEEE.
Forced oscillation in power systems with converter controlled-based resources- a survey with case studies
- Surinkaew, Tossaporn, Emami, Koanoush, Shah, Rakibuzzaman, Islam, Syed, Mithulananthan, N.
- Authors: Surinkaew, Tossaporn , Emami, Koanoush , Shah, Rakibuzzaman , Islam, Syed , Mithulananthan, N.
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 150911-150924
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- Description: In future power systems, conventional synchronous generators will be replaced by converter controlled-based generations (CCGs), i.e., wind and solar generations, and battery energy storage systems. Thus, the paradigm shift in power systems will lead to the inferior system strength and inertia scarcity. Therefore, the problems of forced oscillation (FO) will emerge with new features of the CCGs. The state-of-the-art review in this paper emphasizes previous strategies for FO detection, source identification, and mitigation. Moreover, the effect of FO is investigated in a power system with CCGs. In its conclusion, this paper also highlights important findings and provides suggestions for subsequent research in this important topic of future power systems. © 2013 IEEE.
- Authors: Surinkaew, Tossaporn , Emami, Koanoush , Shah, Rakibuzzaman , Islam, Syed , Mithulananthan, N.
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 150911-150924
- Full Text:
- Reviewed:
- Description: In future power systems, conventional synchronous generators will be replaced by converter controlled-based generations (CCGs), i.e., wind and solar generations, and battery energy storage systems. Thus, the paradigm shift in power systems will lead to the inferior system strength and inertia scarcity. Therefore, the problems of forced oscillation (FO) will emerge with new features of the CCGs. The state-of-the-art review in this paper emphasizes previous strategies for FO detection, source identification, and mitigation. Moreover, the effect of FO is investigated in a power system with CCGs. In its conclusion, this paper also highlights important findings and provides suggestions for subsequent research in this important topic of future power systems. © 2013 IEEE.
Green underwater wireless communications using hybrid optical-acoustic technologies
- Islam, Kazi, Ahmad, Iftekhar, Habibi, Daryoush, Zahed, M., Kamruzzaman, Joarder
- Authors: Islam, Kazi , Ahmad, Iftekhar , Habibi, Daryoush , Zahed, M. , Kamruzzaman, Joarder
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 85109-85123
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- Description: Underwater wireless communication is a rapidly growing field, especially with the recent emergence of technologies such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). To support the high-bandwidth applications using these technologies, underwater optics has attracted significant attention, alongside its complementary technology - underwater acoustics. In this paper, we propose a hybrid opto-acoustic underwater wireless communication model that reduces network power consumption and supports high-data rate underwater applications by selecting appropriate communication links in response to varying traffic loads and dynamic weather conditions. Underwater optics offers high data rates and consumes less power. However, due to the severe absorption of light in the medium, the communication range is short in underwater optics. Conversely, acoustics suffers from low data rate and high power consumption, but provides longer communication ranges. Since most underwater equipment relies on battery power, energy-efficient communication is critical for reliable underwater communications. In this work, we derive analytical models for both underwater acoustics and optics, and calculate the required transmit power for reliable communications in various underwater communication environments. We then formulate an optimization problem that minimizes the network power consumption for carrying data from underwater nodes to surface sinks under varying traffic loads and weather conditions. The proposed optimization model can be solved offline periodically, hence the additional computational complexity to find the optimum solution for larger networks is not a limiting factor for practical applications. Our results indicate that the proposed technique yields up to 35% power savings compared to existing opto-acoustic solutions. © 2013 IEEE.
- Authors: Islam, Kazi , Ahmad, Iftekhar , Habibi, Daryoush , Zahed, M. , Kamruzzaman, Joarder
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 85109-85123
- Full Text:
- Reviewed:
- Description: Underwater wireless communication is a rapidly growing field, especially with the recent emergence of technologies such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). To support the high-bandwidth applications using these technologies, underwater optics has attracted significant attention, alongside its complementary technology - underwater acoustics. In this paper, we propose a hybrid opto-acoustic underwater wireless communication model that reduces network power consumption and supports high-data rate underwater applications by selecting appropriate communication links in response to varying traffic loads and dynamic weather conditions. Underwater optics offers high data rates and consumes less power. However, due to the severe absorption of light in the medium, the communication range is short in underwater optics. Conversely, acoustics suffers from low data rate and high power consumption, but provides longer communication ranges. Since most underwater equipment relies on battery power, energy-efficient communication is critical for reliable underwater communications. In this work, we derive analytical models for both underwater acoustics and optics, and calculate the required transmit power for reliable communications in various underwater communication environments. We then formulate an optimization problem that minimizes the network power consumption for carrying data from underwater nodes to surface sinks under varying traffic loads and weather conditions. The proposed optimization model can be solved offline periodically, hence the additional computational complexity to find the optimum solution for larger networks is not a limiting factor for practical applications. Our results indicate that the proposed technique yields up to 35% power savings compared to existing opto-acoustic solutions. © 2013 IEEE.
Livestock data – is it there and is it FAIR? A systematic review of livestock farming datasets in Australia
- Bahlo, Christiane, Dahlhaus, Peter
- Authors: Bahlo, Christiane , Dahlhaus, Peter
- Date: 2021
- Type: Text , Journal article
- Relation: Computers and Electronics in Agriculture Vol. 188, no. (2021), p.
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- Description: The global adoption of the FAIR principles for scientific data: findable, accessible, interoperable and reusable, has been relatively slow in agriculture, compared to other disciplines. A recent review of the literature showed that the use of precision farming technologies and the development and adoption of open data standards was particularly low in extensive livestock farming. However, a plethora of public datasets exist that have the potential to be used to inform precision farming decision tools. Using extensive livestock farming in Australia as example, we investigate the quantity and quality of datasets available via a systematic dataset review. This systematic review of datasets begins with a search of open data catalogues and querying these to find datasets. Software scripts are developed and used to query the Application Programming Interfaces (APIs) of many of the large data catalogues in Australia, while catalogues without public APIs are queried manually via available web portals. Following the systematic search, a combined list of all datasets is collated and tested for FAIRness and other quality metrics. The contribution of this work is the resulting overview of the state of open datasets within the livestock farming domain on the one hand, but also the development of a systematic dataset search strategy, reusable methods and software scripts. © 2021 Elsevier B.V.
- Authors: Bahlo, Christiane , Dahlhaus, Peter
- Date: 2021
- Type: Text , Journal article
- Relation: Computers and Electronics in Agriculture Vol. 188, no. (2021), p.
- Full Text:
- Reviewed:
- Description: The global adoption of the FAIR principles for scientific data: findable, accessible, interoperable and reusable, has been relatively slow in agriculture, compared to other disciplines. A recent review of the literature showed that the use of precision farming technologies and the development and adoption of open data standards was particularly low in extensive livestock farming. However, a plethora of public datasets exist that have the potential to be used to inform precision farming decision tools. Using extensive livestock farming in Australia as example, we investigate the quantity and quality of datasets available via a systematic dataset review. This systematic review of datasets begins with a search of open data catalogues and querying these to find datasets. Software scripts are developed and used to query the Application Programming Interfaces (APIs) of many of the large data catalogues in Australia, while catalogues without public APIs are queried manually via available web portals. Following the systematic search, a combined list of all datasets is collated and tested for FAIRness and other quality metrics. The contribution of this work is the resulting overview of the state of open datasets within the livestock farming domain on the one hand, but also the development of a systematic dataset search strategy, reusable methods and software scripts. © 2021 Elsevier B.V.
Optimal placement of synchronized voltage traveling wave sensors in a radial distribution network
- Tashakkori, Ali, Abu-Siada, Ahmed, Wolfs, Peter, Islam, Syed
- Authors: Tashakkori, Ali , Abu-Siada, Ahmed , Wolfs, Peter , Islam, Syed
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 65380-65387
- Full Text:
- Reviewed:
- Description: A transmission line fault generates transient high frequency travelling waves (TWs) that propagate through the entire network. The fault location can be determined by recording the instants at which the incident waves arrive at various points in the network. In single end-based methods, the incident wave arrival time and its subsequent reflections from the fault point are used to identify the fault location. In heavily branched distribution networks, the magnitude of the traveling wave declines rapidly as it passes through multiple junctions that cause reflection and refraction to the signal. Therefore, detecting the first incident wave from a high impedance fault is a significant challenge in the electrical distribution networks, in particular, subsequent reflections from a temporarily fault may not be possible. Therefore, to identify a high impedance or temporary faults in a distribution network with many branches, loads, switching devices and distributed transformers, multiple observers are required to observe the entire network. A fully observable and locatable network requires at least one observer per branch or spur which is not a cost effective solution. This paper proposes a reasonable number of relatively low-cost voltage TW observers with GPS time-synchronization and radio communication to detect and timestamp the TW arrival at several points in the network. In this regard, a method to optimally place a given number of TW detectors to maximize the network observability and locatability is presented. Results show the robustness of the proposed method to detect high impedance and intermittent faults within distribution networks with a minimum number of observers. © 2013 IEEE.
- Authors: Tashakkori, Ali , Abu-Siada, Ahmed , Wolfs, Peter , Islam, Syed
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 65380-65387
- Full Text:
- Reviewed:
- Description: A transmission line fault generates transient high frequency travelling waves (TWs) that propagate through the entire network. The fault location can be determined by recording the instants at which the incident waves arrive at various points in the network. In single end-based methods, the incident wave arrival time and its subsequent reflections from the fault point are used to identify the fault location. In heavily branched distribution networks, the magnitude of the traveling wave declines rapidly as it passes through multiple junctions that cause reflection and refraction to the signal. Therefore, detecting the first incident wave from a high impedance fault is a significant challenge in the electrical distribution networks, in particular, subsequent reflections from a temporarily fault may not be possible. Therefore, to identify a high impedance or temporary faults in a distribution network with many branches, loads, switching devices and distributed transformers, multiple observers are required to observe the entire network. A fully observable and locatable network requires at least one observer per branch or spur which is not a cost effective solution. This paper proposes a reasonable number of relatively low-cost voltage TW observers with GPS time-synchronization and radio communication to detect and timestamp the TW arrival at several points in the network. In this regard, a method to optimally place a given number of TW detectors to maximize the network observability and locatability is presented. Results show the robustness of the proposed method to detect high impedance and intermittent faults within distribution networks with a minimum number of observers. © 2013 IEEE.
Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety
- Vamplew, Peter, Foale, Cameron, Dazeley, Richard, Bignold, Adam
- Authors: Vamplew, Peter , Foale, Cameron , Dazeley, Richard , Bignold, Adam
- Date: 2021
- Type: Text , Journal article
- Relation: Engineering Applications of Artificial Intelligence Vol. 100, no. (2021), p.
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- Description: The concept of impact-minimisation has previously been proposed as an approach to addressing the safety concerns that can arise from utility-maximising agents. An impact-minimising agent takes into account the potential impact of its actions on the state of the environment when selecting actions, so as to avoid unacceptable side-effects. This paper proposes and empirically evaluates an implementation of impact-minimisation within the framework of multiobjective reinforcement learning. The key contributions are a novel potential-based approach to specifying a measure of impact, and an examination of a variety of non-linear action-selection operators so as to achieve an acceptable trade-off between achieving the agent's primary task and minimising environmental impact. These experiments also highlight a previously unreported issue with noisy estimates for multiobjective agents using non-linear action-selection, which has broader implications for the application of multiobjective reinforcement learning. © 2021
- Authors: Vamplew, Peter , Foale, Cameron , Dazeley, Richard , Bignold, Adam
- Date: 2021
- Type: Text , Journal article
- Relation: Engineering Applications of Artificial Intelligence Vol. 100, no. (2021), p.
- Full Text:
- Reviewed:
- Description: The concept of impact-minimisation has previously been proposed as an approach to addressing the safety concerns that can arise from utility-maximising agents. An impact-minimising agent takes into account the potential impact of its actions on the state of the environment when selecting actions, so as to avoid unacceptable side-effects. This paper proposes and empirically evaluates an implementation of impact-minimisation within the framework of multiobjective reinforcement learning. The key contributions are a novel potential-based approach to specifying a measure of impact, and an examination of a variety of non-linear action-selection operators so as to achieve an acceptable trade-off between achieving the agent's primary task and minimising environmental impact. These experiments also highlight a previously unreported issue with noisy estimates for multiobjective agents using non-linear action-selection, which has broader implications for the application of multiobjective reinforcement learning. © 2021
Reduced switch multilevel inverter topologies for renewable energy sources
- Sarebanzadeh, Maryam, Hosseinzadeh, Mohammad, Garcia, Cristian, Babaei, Ebrahim, Islam, Syed, Rodriguez, Jose
- Authors: Sarebanzadeh, Maryam , Hosseinzadeh, Mohammad , Garcia, Cristian , Babaei, Ebrahim , Islam, Syed , Rodriguez, Jose
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 120580-120595
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- Description: This article proposes two generalized multilevel inverter configurations that reduce the number of switching devices, isolated DC sources, and total standing voltage on power switches, making them suitable for renewable energy sources. The main topology is a multilevel inverter that handles two isolated DC sources with ten power switches to create 25 voltage levels. Based on the main proposed topology, two generalized multilevel inverters are introduced to provide flexibility in the design and to minimize the number of elements. The optimal topologies for both extensive multilevel inverters are derived from different design objectives such as minimizing the number of elements (gate drivers, DC sources), achieving a large number of levels, and minimizing the total standing voltage. The main advantages of the proposed topologies are a reduced number of elements compared to those required by other existing multilevel inverter topologies. The power loss analysis and standalone PV application of the proposed topologies are discussed. Experimental results are presented for the proposed topology to demonstrate its correct operation. © 2013 IEEE.
- Authors: Sarebanzadeh, Maryam , Hosseinzadeh, Mohammad , Garcia, Cristian , Babaei, Ebrahim , Islam, Syed , Rodriguez, Jose
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 120580-120595
- Full Text:
- Reviewed:
- Description: This article proposes two generalized multilevel inverter configurations that reduce the number of switching devices, isolated DC sources, and total standing voltage on power switches, making them suitable for renewable energy sources. The main topology is a multilevel inverter that handles two isolated DC sources with ten power switches to create 25 voltage levels. Based on the main proposed topology, two generalized multilevel inverters are introduced to provide flexibility in the design and to minimize the number of elements. The optimal topologies for both extensive multilevel inverters are derived from different design objectives such as minimizing the number of elements (gate drivers, DC sources), achieving a large number of levels, and minimizing the total standing voltage. The main advantages of the proposed topologies are a reduced number of elements compared to those required by other existing multilevel inverter topologies. The power loss analysis and standalone PV application of the proposed topologies are discussed. Experimental results are presented for the proposed topology to demonstrate its correct operation. © 2013 IEEE.
Robust image classification using a low-pass activation function and DCT augmentation
- Hossain, Md Tahmid, Teng, Shyh, Sohel, Ferdous, Lu, Guojun
- Authors: Hossain, Md Tahmid , Teng, Shyh , Sohel, Ferdous , Lu, Guojun
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 86460-86474
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- Description: Convolutional Neural Network's (CNN's) performance disparity on clean and corrupted datasets has recently come under scrutiny. In this work, we analyse common corruptions in the frequency domain, i.e., High Frequency corruptions (HFc, e.g., noise) and Low Frequency corruptions (LFc, e.g., blur). Although a simple solution to HFc is low-pass filtering, ReLU - a widely used Activation Function (AF), does not have any filtering mechanism. In this work, we instill low-pass filtering into the AF (LP-ReLU) to improve robustness against HFc. To deal with LFc, we complement LP-ReLU with Discrete Cosine Transform based augmentation. LP-ReLU, coupled with DCT augmentation, enables a deep network to tackle the entire spectrum of corruption. We use CIFAR-10-C and Tiny ImageNet-C for evaluation and demonstrate improvements of 5% and 7.3% in accuracy respectively, compared to the State-Of-The-Art (SOTA). We further evaluate our method's stability on a variety of perturbations in CIFAR-10-P and Tiny ImageNet-P, achieving new SOTA in these experiments as well. To further strengthen our understanding regarding CNN's lack of robustness, a decision space visualisation process is proposed and presented in this work. © 2013 IEEE.
- Authors: Hossain, Md Tahmid , Teng, Shyh , Sohel, Ferdous , Lu, Guojun
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 86460-86474
- Full Text:
- Reviewed:
- Description: Convolutional Neural Network's (CNN's) performance disparity on clean and corrupted datasets has recently come under scrutiny. In this work, we analyse common corruptions in the frequency domain, i.e., High Frequency corruptions (HFc, e.g., noise) and Low Frequency corruptions (LFc, e.g., blur). Although a simple solution to HFc is low-pass filtering, ReLU - a widely used Activation Function (AF), does not have any filtering mechanism. In this work, we instill low-pass filtering into the AF (LP-ReLU) to improve robustness against HFc. To deal with LFc, we complement LP-ReLU with Discrete Cosine Transform based augmentation. LP-ReLU, coupled with DCT augmentation, enables a deep network to tackle the entire spectrum of corruption. We use CIFAR-10-C and Tiny ImageNet-C for evaluation and demonstrate improvements of 5% and 7.3% in accuracy respectively, compared to the State-Of-The-Art (SOTA). We further evaluate our method's stability on a variety of perturbations in CIFAR-10-P and Tiny ImageNet-P, achieving new SOTA in these experiments as well. To further strengthen our understanding regarding CNN's lack of robustness, a decision space visualisation process is proposed and presented in this work. © 2013 IEEE.
Rock-burst occurrence prediction based on optimized naïve bayes models
- Ke, Bo, Khandelwal, Manoj, Asteris, Panagiotis, Skentou, Athanasia, Mamou, Anna, Armaghani, Danial
- Authors: Ke, Bo , Khandelwal, Manoj , Asteris, Panagiotis , Skentou, Athanasia , Mamou, Anna , Armaghani, Danial
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 91347-91360
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- Description: Rock-burst is a common failure in hard rock related projects in civil and mining construction and therefore, proper classification and prediction of this phenomenon is of interest. This research presents the development of optimized naïve Bayes models, in predicting rock-burst failures in underground projects. The naïve Bayes models were optimized using four weight optimization techniques including forward, backward, particle swarm optimization, and evolutionary. An evolutionary random forest model was developed to identify the most significant input parameters. The maximum tangential stress, elastic energy index, and uniaxial tensile stress were then selected by the feature selection technique (i.e., evolutionary random forest) to develop the optimized naïve Bayes models. The performance of the models was assessed using various criteria as well as a simple ranking system. The results of this research showed that particle swarm optimization was the most effective technique in improving the accuracy of the naïve Bayes model for rock-burst prediction (cumulative ranking = 21), while the backward technique was the worst weight optimization technique (cumulative ranking = 11). All the optimized naïve Bayes models identified the maximum tangential stress as the most significant parameter in predicting rock-burst failures. The results of this research demonstrate that particle swarm optimization technique may improve the accuracy of naïve Bayes algorithms in predicting rock-burst occurrence. © 2013 IEEE.
- Authors: Ke, Bo , Khandelwal, Manoj , Asteris, Panagiotis , Skentou, Athanasia , Mamou, Anna , Armaghani, Danial
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 91347-91360
- Full Text:
- Reviewed:
- Description: Rock-burst is a common failure in hard rock related projects in civil and mining construction and therefore, proper classification and prediction of this phenomenon is of interest. This research presents the development of optimized naïve Bayes models, in predicting rock-burst failures in underground projects. The naïve Bayes models were optimized using four weight optimization techniques including forward, backward, particle swarm optimization, and evolutionary. An evolutionary random forest model was developed to identify the most significant input parameters. The maximum tangential stress, elastic energy index, and uniaxial tensile stress were then selected by the feature selection technique (i.e., evolutionary random forest) to develop the optimized naïve Bayes models. The performance of the models was assessed using various criteria as well as a simple ranking system. The results of this research showed that particle swarm optimization was the most effective technique in improving the accuracy of the naïve Bayes model for rock-burst prediction (cumulative ranking = 21), while the backward technique was the worst weight optimization technique (cumulative ranking = 11). All the optimized naïve Bayes models identified the maximum tangential stress as the most significant parameter in predicting rock-burst failures. The results of this research demonstrate that particle swarm optimization technique may improve the accuracy of naïve Bayes algorithms in predicting rock-burst occurrence. © 2013 IEEE.
A low-complexity equalizer for video broadcasting in cyber-physical social systems through handheld mobile devices
- Solyman, Ahmad, Attar, Hani, Khosravi, Mohammad, Menon, Varun, Jolfaei, Alireza, Balasubramanian, Venki, Selvaraj, Buvana, Tavallali, Pooya
- Authors: Solyman, Ahmad , Attar, Hani , Khosravi, Mohammad , Menon, Varun , Jolfaei, Alireza , Balasubramanian, Venki , Selvaraj, Buvana , Tavallali, Pooya
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 67591-67602
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- Description: In Digital Video Broadcasting-Handheld (DVB-H) devices for cyber-physical social systems, the Discrete Fractional Fourier Transform-Orthogonal Chirp Division Multiplexing (DFrFT-OCDM) has been suggested to enhance the performance over Orthogonal Frequency Division Multiplexing (OFDM) systems under time and frequency-selective fading channels. In this case, the need for equalizers like the Minimum Mean Square Error (MMSE) and Zero-Forcing (ZF) arises, though it is excessively complex due to the need for a matrix inversion, especially for DVB-H extensive symbol lengths. In this work, a low complexity equalizer, Least-Squares Minimal Residual (LSMR) algorithm, is used to solve the matrix inversion iteratively. The paper proposes the LSMR algorithm for linear and nonlinear equalizers with the simulation results, which indicate that the proposed equalizer has significant performance and reduced complexity over the classical MMSE equalizer and other low complexity equalizers, in time and frequency-selective fading channels. © 2013 IEEE.
- Authors: Solyman, Ahmad , Attar, Hani , Khosravi, Mohammad , Menon, Varun , Jolfaei, Alireza , Balasubramanian, Venki , Selvaraj, Buvana , Tavallali, Pooya
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 67591-67602
- Full Text:
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- Description: In Digital Video Broadcasting-Handheld (DVB-H) devices for cyber-physical social systems, the Discrete Fractional Fourier Transform-Orthogonal Chirp Division Multiplexing (DFrFT-OCDM) has been suggested to enhance the performance over Orthogonal Frequency Division Multiplexing (OFDM) systems under time and frequency-selective fading channels. In this case, the need for equalizers like the Minimum Mean Square Error (MMSE) and Zero-Forcing (ZF) arises, though it is excessively complex due to the need for a matrix inversion, especially for DVB-H extensive symbol lengths. In this work, a low complexity equalizer, Least-Squares Minimal Residual (LSMR) algorithm, is used to solve the matrix inversion iteratively. The paper proposes the LSMR algorithm for linear and nonlinear equalizers with the simulation results, which indicate that the proposed equalizer has significant performance and reduced complexity over the classical MMSE equalizer and other low complexity equalizers, in time and frequency-selective fading channels. © 2013 IEEE.
A new data driven long-term solar yield analysis model of photovoltaic power plants
- Ray, Biplob, Shah, Rakibuzzaman, Islam, Md Rabiul, Islam, Syed
- Authors: Ray, Biplob , Shah, Rakibuzzaman , Islam, Md Rabiul , Islam, Syed
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 136223-136233
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- Description: Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital. Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting method. It has been identified that the hybrid approach may outperform the individual technique in minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed for the output prediction of the solar photovoltaic systems (i.e. proposed PV system) in Australia to obtain the trade-off between accuracy and efficacy. The historical dataset from 1990-2013 in Australian locations (e.g. North Queensland) are used to train the model. The model is developed using the combination of multivariate long and short-term memory (LSTM) and convolutional neural network (CNN). The proposed hybrid deep learning (LSTM-CNN) is compared with the existing neural network ensemble (NNE), random forest, statistical analysis, and artificial neural network (ANN) based techniques to assess the performance. The proposed model could be useful for generation planning and reserve estimation in power systems with high penetration of solar photovoltaics (PVs) or other renewable energy sources (RESs). © 2013 IEEE.
- Authors: Ray, Biplob , Shah, Rakibuzzaman , Islam, Md Rabiul , Islam, Syed
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 136223-136233
- Full Text:
- Reviewed:
- Description: Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital. Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting method. It has been identified that the hybrid approach may outperform the individual technique in minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed for the output prediction of the solar photovoltaic systems (i.e. proposed PV system) in Australia to obtain the trade-off between accuracy and efficacy. The historical dataset from 1990-2013 in Australian locations (e.g. North Queensland) are used to train the model. The model is developed using the combination of multivariate long and short-term memory (LSTM) and convolutional neural network (CNN). The proposed hybrid deep learning (LSTM-CNN) is compared with the existing neural network ensemble (NNE), random forest, statistical analysis, and artificial neural network (ANN) based techniques to assess the performance. The proposed model could be useful for generation planning and reserve estimation in power systems with high penetration of solar photovoltaics (PVs) or other renewable energy sources (RESs). © 2013 IEEE.
A secured framework for SDN-based edge computing in IoT-enabled healthcare system
- Li, Junxia, Cai, Jinjin, Khan, Fazlullah, Rehman, Ateeq, Balasubramanian, Venki
- Authors: Li, Junxia , Cai, Jinjin , Khan, Fazlullah , Rehman, Ateeq , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 135479-135490
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- Description: The Internet of Things (IoT) consists of resource-constrained smart devices capable to sense and process data. It connects a huge number of smart sensing devices, i.e., things, and heterogeneous networks. The IoT is incorporated into different applications, such as smart health, smart home, smart grid, etc. The concept of smart healthcare has emerged in different countries, where pilot projects of healthcare facilities are analyzed. In IoT-enabled healthcare systems, the security of IoT devices and associated data is very important, whereas Edge computing is a promising architecture that solves their computational and processing problems. Edge computing is economical and has the potential to provide low latency data services by improving the communication and computation speed of IoT devices in a healthcare system. In Edge-based IoT-enabled healthcare systems, load balancing, network optimization, and efficient resource utilization are accurately performed using artificial intelligence (AI), i.e., intelligent software-defined network (SDN) controller. SDN-based Edge computing is helpful in the efficient utilization of limited resources of IoT devices. However, these low powered devices and associated data (private sensitive data of patients) are prone to various security threats. Therefore, in this paper, we design a secure framework for SDN-based Edge computing in IoT-enabled healthcare system. In the proposed framework, the IoT devices are authenticated by the Edge servers using a lightweight authentication scheme. After authentication, these devices collect data from the patients and send them to the Edge servers for storage, processing, and analyses. The Edge servers are connected with an SDN controller, which performs load balancing, network optimization, and efficient resource utilization in the healthcare system. The proposed framework is evaluated using computer-based simulations. The results demonstrate that the proposed framework provides better solutions for IoT-enabled healthcare systems. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramaniam” is provided in this record**
- Authors: Li, Junxia , Cai, Jinjin , Khan, Fazlullah , Rehman, Ateeq , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 135479-135490
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) consists of resource-constrained smart devices capable to sense and process data. It connects a huge number of smart sensing devices, i.e., things, and heterogeneous networks. The IoT is incorporated into different applications, such as smart health, smart home, smart grid, etc. The concept of smart healthcare has emerged in different countries, where pilot projects of healthcare facilities are analyzed. In IoT-enabled healthcare systems, the security of IoT devices and associated data is very important, whereas Edge computing is a promising architecture that solves their computational and processing problems. Edge computing is economical and has the potential to provide low latency data services by improving the communication and computation speed of IoT devices in a healthcare system. In Edge-based IoT-enabled healthcare systems, load balancing, network optimization, and efficient resource utilization are accurately performed using artificial intelligence (AI), i.e., intelligent software-defined network (SDN) controller. SDN-based Edge computing is helpful in the efficient utilization of limited resources of IoT devices. However, these low powered devices and associated data (private sensitive data of patients) are prone to various security threats. Therefore, in this paper, we design a secure framework for SDN-based Edge computing in IoT-enabled healthcare system. In the proposed framework, the IoT devices are authenticated by the Edge servers using a lightweight authentication scheme. After authentication, these devices collect data from the patients and send them to the Edge servers for storage, processing, and analyses. The Edge servers are connected with an SDN controller, which performs load balancing, network optimization, and efficient resource utilization in the healthcare system. The proposed framework is evaluated using computer-based simulations. The results demonstrate that the proposed framework provides better solutions for IoT-enabled healthcare systems. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramaniam” is provided in this record**
A Survey on Behavioral Pattern Mining from Sensor Data in Internet of Things
- Rashid, Md Mamunur, Kamruzzaman, Joarder, Hassan, Mohammad, Shahriar Shafin, Sakib, Bhuiyan, Md Zakirul
- Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Hassan, Mohammad , Shahriar Shafin, Sakib , Bhuiyan, Md Zakirul
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 33318-33341
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- Description: The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. © 2013 IEEE.
- Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Hassan, Mohammad , Shahriar Shafin, Sakib , Bhuiyan, Md Zakirul
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 33318-33341
- Full Text:
- Reviewed:
- Description: The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. © 2013 IEEE.
An adaptive and flexible brain energized full body exoskeleton with IoT edge for assisting the paralyzed patients
- Jacob, Sunil, Alagirisamy, Mukil, Menon, Varun, Kumar, B. Manoj, Balasubramanian, Venki
- Authors: Jacob, Sunil , Alagirisamy, Mukil , Menon, Varun , Kumar, B. Manoj , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 100721-100731
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- Description: The paralyzed population is increasing worldwide due to stroke, spinal code injury, post-polio, and other related diseases. Different assistive technologies are used to improve the physical and mental health of the affected patients. Exoskeletons have emerged as one of the most promising technology to provide movement and rehabilitation for the paralyzed. But exoskeletons are limited by the constraints of weight, flexibility, and adaptability. To resolve these issues, we propose an adaptive and flexible Brain Energized Full Body Exoskeleton (BFBE) for assisting the paralyzed people. This paper describes the design, control, and testing of BFBE with 15 degrees of freedom (DoF) for assisting the users in their daily activities. The flexibility is incorporated into the system by a modular design approach. The brain signals captured by the Electroencephalogram (EEG) sensors are used for controlling the movements of BFBE. The processing happens at the edge, reducing delay in decision making and the system is further integrated with an IoT module that helps to send an alert message to multiple caregivers in case of an emergency. The potential energy harvesting is used in the system to solve the power issues related to the exoskeleton. The stability in the gait cycle is ensured by using adaptive sensory feedback. The system validation is done by using six natural movements on ten different paralyzed persons. The system recognizes human intensions with an accuracy of 85%. The result shows that BFBE can be an efficient method for providing assistance and rehabilitation for paralyzed patients. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**
- Authors: Jacob, Sunil , Alagirisamy, Mukil , Menon, Varun , Kumar, B. Manoj , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 100721-100731
- Full Text:
- Reviewed:
- Description: The paralyzed population is increasing worldwide due to stroke, spinal code injury, post-polio, and other related diseases. Different assistive technologies are used to improve the physical and mental health of the affected patients. Exoskeletons have emerged as one of the most promising technology to provide movement and rehabilitation for the paralyzed. But exoskeletons are limited by the constraints of weight, flexibility, and adaptability. To resolve these issues, we propose an adaptive and flexible Brain Energized Full Body Exoskeleton (BFBE) for assisting the paralyzed people. This paper describes the design, control, and testing of BFBE with 15 degrees of freedom (DoF) for assisting the users in their daily activities. The flexibility is incorporated into the system by a modular design approach. The brain signals captured by the Electroencephalogram (EEG) sensors are used for controlling the movements of BFBE. The processing happens at the edge, reducing delay in decision making and the system is further integrated with an IoT module that helps to send an alert message to multiple caregivers in case of an emergency. The potential energy harvesting is used in the system to solve the power issues related to the exoskeleton. The stability in the gait cycle is ensured by using adaptive sensory feedback. The system validation is done by using six natural movements on ten different paralyzed persons. The system recognizes human intensions with an accuracy of 85%. The result shows that BFBE can be an efficient method for providing assistance and rehabilitation for paralyzed patients. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**
An enhancement to the spatial pyramid matching for image classification and retrieval
- Karmakar, Priyabrata, Teng, Shyh, Lu, Guojun, Zhang, Dengsheng
- Authors: Karmakar, Priyabrata , Teng, Shyh , Lu, Guojun , Zhang, Dengsheng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 22463-22472
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- Description: Spatial pyramid matching (SPM) is one of the widely used methods to incorporate spatial information into the image representation. Despite its effectiveness, the traditional SPM is not rotation invariant. A rotation invariant SPM has been proposed in the literature but it has many limitations regarding the effectiveness. In this paper, we investigate how to make SPM robust to rotation by addressing those limitations. In an SPM framework, an image is divided into an increasing number of partitions at different pyramid levels. In this paper, our main focus is on how to partition images in such a way that the resulting structure can deal with image-level rotations. To do that, we investigate three concentric ring partitioning schemes. Apart from image partitioning, another important component of the SPM framework is a weight function. To apportion the contribution of each pyramid level to the final matching between two images, the weight function is needed. In this paper, we propose a new weight function which is suitable for the rotation-invariant SPM structure. Experiments based on image classification and retrieval are performed on five image databases. The detailed result analysis shows that we are successful in enhancing the effectiveness of SPM for image classification and retrieval. © 2013 IEEE.
- Authors: Karmakar, Priyabrata , Teng, Shyh , Lu, Guojun , Zhang, Dengsheng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 22463-22472
- Full Text:
- Reviewed:
- Description: Spatial pyramid matching (SPM) is one of the widely used methods to incorporate spatial information into the image representation. Despite its effectiveness, the traditional SPM is not rotation invariant. A rotation invariant SPM has been proposed in the literature but it has many limitations regarding the effectiveness. In this paper, we investigate how to make SPM robust to rotation by addressing those limitations. In an SPM framework, an image is divided into an increasing number of partitions at different pyramid levels. In this paper, our main focus is on how to partition images in such a way that the resulting structure can deal with image-level rotations. To do that, we investigate three concentric ring partitioning schemes. Apart from image partitioning, another important component of the SPM framework is a weight function. To apportion the contribution of each pyramid level to the final matching between two images, the weight function is needed. In this paper, we propose a new weight function which is suitable for the rotation-invariant SPM structure. Experiments based on image classification and retrieval are performed on five image databases. The detailed result analysis shows that we are successful in enhancing the effectiveness of SPM for image classification and retrieval. © 2013 IEEE.