Characterizations of minimal elements of topical functions on semimodules with applications
- Hassani, Sara, Mohebi, Hossein
- Authors: Hassani, Sara , Mohebi, Hossein
- Date: 2017
- Type: Text , Journal article
- Relation: Linear Algebra and Its Applications Vol. 520, no. (2017), p. 104-124
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- Description: In this paper, we first give characterizations of the superdifferential of extended valued topical functions defined on a semimodule with values in a semifield. Next, we characterize minimal elements of the upper support set of extended valued topical functions. Finally, as an application, we present a necessary and sufficient condition for global maximum of the difference of two strictly topical functions defined on a semimodule. (C) 2017 Elsevier Inc. All rights reserved.
- Authors: Hassani, Sara , Mohebi, Hossein
- Date: 2017
- Type: Text , Journal article
- Relation: Linear Algebra and Its Applications Vol. 520, no. (2017), p. 104-124
- Full Text:
- Reviewed:
- Description: In this paper, we first give characterizations of the superdifferential of extended valued topical functions defined on a semimodule with values in a semifield. Next, we characterize minimal elements of the upper support set of extended valued topical functions. Finally, as an application, we present a necessary and sufficient condition for global maximum of the difference of two strictly topical functions defined on a semimodule. (C) 2017 Elsevier Inc. All rights reserved.
Effectiveness of online tailored advice to prevent running-related injuries and promote preventive behaviour in Dutch trail runners : A pragmatic randomised controlled trial
- Hespanhol, Luiz, van Mechelen, Willem, Verhagen, Evert
- Authors: Hespanhol, Luiz , van Mechelen, Willem , Verhagen, Evert
- Date: 2018
- Type: Text , Journal article
- Relation: British journal of sports medicine Vol. 52, no. 13 (2018), p. 851-858
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- Description: BACKGROUND: Trail running is popular worldwide, but there is no preventive intervention for running-related injury (RRI). AIM: To evaluate the effectiveness of adding online tailored advice (TrailS6 ) to general advice on (1) the prevention of RRIs and (2) the determinants and actual preventive behaviour in Dutch trail runners. METHODS: Two-arm randomised controlled trial over 6 months. 232 trail runners were randomly assigned to an intervention or control group. All participants received online general advice on RRI prevention 1 week after baseline. Every 2 weeks, participants in the intervention group received specific advice tailored to their RRI status. The control group received no further intervention. Bayesian mixed models were used to analyse the data. RESULTS: Trail runners in the intervention group sustained 13% fewer RRIs compared with those in the control group after 6 months of follow-up (absolute risk difference -13.1%, 95% Bayesian highest posterior credible interval (95% BCI) -23.3 to -3.1). A preventive benefit was observed in one out of eight trail runners who had received the online tailored advice for 6 months (number needed to treat 8, 95% BCI 3 to 22). No significant between-group difference was observed on the determinants and actual preventive behaviours. CONCLUSIONS: Online tailored advice prevented RRIs among Dutch trail runners. Therefore, online tailored advice may be used as a preventive component in multicomponent RRI prevention programmes. No effect was observed on determinants and actual preventive behaviours. TRIAL REGISTRATION NUMBER: The Netherlands National Trial Register (NTR5431).
- Authors: Hespanhol, Luiz , van Mechelen, Willem , Verhagen, Evert
- Date: 2018
- Type: Text , Journal article
- Relation: British journal of sports medicine Vol. 52, no. 13 (2018), p. 851-858
- Full Text:
- Reviewed:
- Description: BACKGROUND: Trail running is popular worldwide, but there is no preventive intervention for running-related injury (RRI). AIM: To evaluate the effectiveness of adding online tailored advice (TrailS6 ) to general advice on (1) the prevention of RRIs and (2) the determinants and actual preventive behaviour in Dutch trail runners. METHODS: Two-arm randomised controlled trial over 6 months. 232 trail runners were randomly assigned to an intervention or control group. All participants received online general advice on RRI prevention 1 week after baseline. Every 2 weeks, participants in the intervention group received specific advice tailored to their RRI status. The control group received no further intervention. Bayesian mixed models were used to analyse the data. RESULTS: Trail runners in the intervention group sustained 13% fewer RRIs compared with those in the control group after 6 months of follow-up (absolute risk difference -13.1%, 95% Bayesian highest posterior credible interval (95% BCI) -23.3 to -3.1). A preventive benefit was observed in one out of eight trail runners who had received the online tailored advice for 6 months (number needed to treat 8, 95% BCI 3 to 22). No significant between-group difference was observed on the determinants and actual preventive behaviours. CONCLUSIONS: Online tailored advice prevented RRIs among Dutch trail runners. Therefore, online tailored advice may be used as a preventive component in multicomponent RRI prevention programmes. No effect was observed on determinants and actual preventive behaviours. TRIAL REGISTRATION NUMBER: The Netherlands National Trial Register (NTR5431).
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.
Privacy protection and energy optimization for 5G-aided industrial internet of things
- Humayun, Mamoona, Jhanjhi, Nz, Alruwaili, Madallah, Amalathas, Sagaya, Balasubramanian, Venki, Selvaraj, Buvana
- Authors: Humayun, Mamoona , Jhanjhi, Nz , Alruwaili, Madallah , Amalathas, Sagaya , Balasubramanian, Venki , Selvaraj, Buvana
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 183665-183677
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- Description: The 5G is expected to revolutionize every sector of life by providing interconnectivity of everything everywhere at high speed. However, massively interconnected devices and fast data transmission will bring the challenge of privacy as well as energy deficiency. In today's fast-paced economy, almost every sector of the economy is dependent on energy resources. On the other hand, the energy sector is mainly dependent on fossil fuels and is constituting about 80% of energy globally. This massive extraction and combustion of fossil fuels lead to a lot of adverse impacts on health, environment, and economy. The newly emerging 5G technology has changed the existing phenomenon of life by connecting everything everywhere using IoT devices. 5G enabled IIoT devices has transformed everything from traditional to smart, e.g. smart city, smart healthcare, smart industry, smart manufacturing etc. However, massive I/O technologies for providing D2D connection has also created the issue of privacy that need to be addressed. Privacy is the fundamental right of every individual. 5G industries and organizations need to preserve it for their stability and competency. Therefore, privacy at all three levels (data, identity and location) need to be maintained. Further, energy optimization is a big challenge that needs to be addressed for leveraging the potential benefits of 5G and 5G aided IIoT. Billions of IIoT devices that are expected to communicate using the 5G network will consume a considerable amount of energy while energy resources are limited. Therefore, energy optimization is a future challenge faced by 5G industries that need to be addressed. To fill these gaps, we have provided a comprehensive framework that will help energy researchers and practitioners in better understanding of 5G aided industry 4.0 infrastructure and energy resource optimization by improving privacy. The proposed framework is evaluated using case studies and mathematical modelling. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
- Authors: Humayun, Mamoona , Jhanjhi, Nz , Alruwaili, Madallah , Amalathas, Sagaya , Balasubramanian, Venki , Selvaraj, Buvana
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 183665-183677
- Full Text:
- Reviewed:
- Description: The 5G is expected to revolutionize every sector of life by providing interconnectivity of everything everywhere at high speed. However, massively interconnected devices and fast data transmission will bring the challenge of privacy as well as energy deficiency. In today's fast-paced economy, almost every sector of the economy is dependent on energy resources. On the other hand, the energy sector is mainly dependent on fossil fuels and is constituting about 80% of energy globally. This massive extraction and combustion of fossil fuels lead to a lot of adverse impacts on health, environment, and economy. The newly emerging 5G technology has changed the existing phenomenon of life by connecting everything everywhere using IoT devices. 5G enabled IIoT devices has transformed everything from traditional to smart, e.g. smart city, smart healthcare, smart industry, smart manufacturing etc. However, massive I/O technologies for providing D2D connection has also created the issue of privacy that need to be addressed. Privacy is the fundamental right of every individual. 5G industries and organizations need to preserve it for their stability and competency. Therefore, privacy at all three levels (data, identity and location) need to be maintained. Further, energy optimization is a big challenge that needs to be addressed for leveraging the potential benefits of 5G and 5G aided IIoT. Billions of IIoT devices that are expected to communicate using the 5G network will consume a considerable amount of energy while energy resources are limited. Therefore, energy optimization is a future challenge faced by 5G industries that need to be addressed. To fill these gaps, we have provided a comprehensive framework that will help energy researchers and practitioners in better understanding of 5G aided industry 4.0 infrastructure and energy resource optimization by improving privacy. The proposed framework is evaluated using case studies and mathematical modelling. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
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
- Full Text:
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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.
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**
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
- Full Text:
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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**
Artificial neural network modeling and sensitivity analysis of performance and emissions in a compression ignition engine using biodiesel fuel
- Jaliliantabar, Farzad, Ghobadian, Barat, Najafi, Gholamhassan, Yusaf, Talal
- Authors: Jaliliantabar, Farzad , Ghobadian, Barat , Najafi, Gholamhassan , Yusaf, Talal
- Date: 2018
- Type: Text , Journal article
- Relation: Energies Vol. 11, no. 9 (2018), p. 1-24
- Full Text:
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- Description: In the present research work, a neural network model has been developed to predict the exhaust emissions and performance of a compression ignition engine. The significance and novelty of the work, with respect to existing literature, is the application of sensitivity analysis and an artificial neural network (ANN) simultaneously in order to predict the engine parameters. The inputs of the model were engine load (0, 25, 50, 75 and 100%), engine speed (1700, 2100, 2500 and 2900 rpm) and the percent of biodiesel fuel derived from waste cooking oil in diesel fuel (B0, B5, B10, B15 and B20). The relationship between the input parameters and engine cylinder performance and emissions can be determined by the network. The global sensitivity analysis results show that all the investigated factors are effective on the created model and cannot be ignored. In addition, it is found that the most emissions decreased while using biodiesel fuel in the compression ignition engine.
- Authors: Jaliliantabar, Farzad , Ghobadian, Barat , Najafi, Gholamhassan , Yusaf, Talal
- Date: 2018
- Type: Text , Journal article
- Relation: Energies Vol. 11, no. 9 (2018), p. 1-24
- Full Text:
- Reviewed:
- Description: In the present research work, a neural network model has been developed to predict the exhaust emissions and performance of a compression ignition engine. The significance and novelty of the work, with respect to existing literature, is the application of sensitivity analysis and an artificial neural network (ANN) simultaneously in order to predict the engine parameters. The inputs of the model were engine load (0, 25, 50, 75 and 100%), engine speed (1700, 2100, 2500 and 2900 rpm) and the percent of biodiesel fuel derived from waste cooking oil in diesel fuel (B0, B5, B10, B15 and B20). The relationship between the input parameters and engine cylinder performance and emissions can be determined by the network. The global sensitivity analysis results show that all the investigated factors are effective on the created model and cannot be ignored. In addition, it is found that the most emissions decreased while using biodiesel fuel in the compression ignition engine.
On the security of permutation-only image encryption schemes
- Jolfaei, Alireza, Wu, Xinwen, Muthukkumarasamy, Vallipuram
- Authors: Jolfaei, Alireza , Wu, Xinwen , Muthukkumarasamy, Vallipuram
- Date: 2016
- Type: Text , Journal article
- Relation: IEEE Transactions on Information Forensics and Security Vol. 11, no. 2 (2016), p. 235-246
- Full Text:
- Reviewed:
- Description: Permutation is a commonly used primitive in multimedia (image/video) encryption schemes, and many permutation-only algorithms have been proposed in recent years for the protection of multimedia data. In permutation-only image ciphers, the entries of the image matrix are scrambled using a permutation mapping matrix which is built by a pseudo-random number generator. The literature on the cryptanalysis of image ciphers indicates that the permutation-only image ciphers are insecure against ciphertext-only attacks and/or known/chosenplaintext attacks. However, the previous studies have not been able to ensure the correct retrieval of the complete plaintext elements. In this paper, we revisited the previous works on cryptanalysis of permutation-only image encryption schemes and made the cryptanalysis work on chosen-plaintext attacks complete and more efficient. We proved that in all permutationonly image ciphers, regardless of the cipher structure, the correct permutation mapping is recovered completely by a chosenplaintext attack. To the best of our knowledge, for the first time, this paper gives a chosen-plaintext attack that completely determines the correct plaintext elements using a deterministic method. When the plain-images are of size M × N and with L different color intensities, the number n of required chosen plain-images to break the permutation-only image encryption algorithm is n = logL(MN). The complexity of the proposed attack is O (n · M N) which indicates its feasibility in a polynomial amount of computation time. To validate the performance of the proposed chosen-plaintext attack, numerous experiments were performed on two recently proposed permutation-only image/video ciphers. Both theoretical and experimental results showed that the proposed attack outperforms the state-of-theart cryptanalytic methods.
- Authors: Jolfaei, Alireza , Wu, Xinwen , Muthukkumarasamy, Vallipuram
- Date: 2016
- Type: Text , Journal article
- Relation: IEEE Transactions on Information Forensics and Security Vol. 11, no. 2 (2016), p. 235-246
- Full Text:
- Reviewed:
- Description: Permutation is a commonly used primitive in multimedia (image/video) encryption schemes, and many permutation-only algorithms have been proposed in recent years for the protection of multimedia data. In permutation-only image ciphers, the entries of the image matrix are scrambled using a permutation mapping matrix which is built by a pseudo-random number generator. The literature on the cryptanalysis of image ciphers indicates that the permutation-only image ciphers are insecure against ciphertext-only attacks and/or known/chosenplaintext attacks. However, the previous studies have not been able to ensure the correct retrieval of the complete plaintext elements. In this paper, we revisited the previous works on cryptanalysis of permutation-only image encryption schemes and made the cryptanalysis work on chosen-plaintext attacks complete and more efficient. We proved that in all permutationonly image ciphers, regardless of the cipher structure, the correct permutation mapping is recovered completely by a chosenplaintext attack. To the best of our knowledge, for the first time, this paper gives a chosen-plaintext attack that completely determines the correct plaintext elements using a deterministic method. When the plain-images are of size M × N and with L different color intensities, the number n of required chosen plain-images to break the permutation-only image encryption algorithm is n = logL(MN). The complexity of the proposed attack is O (n · M N) which indicates its feasibility in a polynomial amount of computation time. To validate the performance of the proposed chosen-plaintext attack, numerous experiments were performed on two recently proposed permutation-only image/video ciphers. Both theoretical and experimental results showed that the proposed attack outperforms the state-of-theart cryptanalytic methods.
A 3D object encryption scheme which maintains dimensional and spatial stability
- Jolfaei, Alireza, Wu, Xinwen, Muthukkumarasamy, Vallipuram
- Authors: Jolfaei, Alireza , Wu, Xinwen , Muthukkumarasamy, Vallipuram
- Date: 2015
- Type: Text , Journal article
- Relation: IEEE Transactions on Information Forensics and Security Vol. 10, no. 2 (2015), p. 409-422
- Full Text:
- Reviewed:
- Description: Due to widespread applications of 3D vision technology, the research into 3D object protection is primarily important. To maintain confidentiality, encryption of 3D objects is essential. However, the requirements and limitations imposed by 3D objects indicate the impropriety of conventional cryptosystems for 3D object encryption. This suggests the necessity of designing new ciphers. In addition, the study of prior works indicates that the majority of problems encountered with encrypting 3D objects are about point cloud protection, dimensional and spatial stability, and robustness against surface reconstruction attacks. To address these problems, this paper proposes a 3D object encryption scheme, based on a series of random permutations and rotations, which deform the geometry of the point cloud. Since the inverse of a permutation and a rotation matrix is its transpose, the decryption implementation is very efficient. Our statistical analyses show that within the cipher point cloud, points are randomly distributed. Furthermore, the proposed cipher leaks no information regarding the geometric structure of the plain point cloud, and is also highly sensitive to the changes of the plaintext and secret key. The theoretical and experimental analyses demonstrate the security, effectiveness, and robustness of the proposed cipher against surface reconstruction attacks.
- Authors: Jolfaei, Alireza , Wu, Xinwen , Muthukkumarasamy, Vallipuram
- Date: 2015
- Type: Text , Journal article
- Relation: IEEE Transactions on Information Forensics and Security Vol. 10, no. 2 (2015), p. 409-422
- Full Text:
- Reviewed:
- Description: Due to widespread applications of 3D vision technology, the research into 3D object protection is primarily important. To maintain confidentiality, encryption of 3D objects is essential. However, the requirements and limitations imposed by 3D objects indicate the impropriety of conventional cryptosystems for 3D object encryption. This suggests the necessity of designing new ciphers. In addition, the study of prior works indicates that the majority of problems encountered with encrypting 3D objects are about point cloud protection, dimensional and spatial stability, and robustness against surface reconstruction attacks. To address these problems, this paper proposes a 3D object encryption scheme, based on a series of random permutations and rotations, which deform the geometry of the point cloud. Since the inverse of a permutation and a rotation matrix is its transpose, the decryption implementation is very efficient. Our statistical analyses show that within the cipher point cloud, points are randomly distributed. Furthermore, the proposed cipher leaks no information regarding the geometric structure of the plain point cloud, and is also highly sensitive to the changes of the plaintext and secret key. The theoretical and experimental analyses demonstrate the security, effectiveness, and robustness of the proposed cipher against surface reconstruction attacks.
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
- Full Text:
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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.
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.
What fooled us in the knee may trip us up in the hip: Lessons from arthroscopy
- Kemp, Joanne, Crossley, Kay, Roos, Ewa, Ratzlaff, Charles
- Authors: Kemp, Joanne , Crossley, Kay , Roos, Ewa , Ratzlaff, Charles
- Date: 2014
- Type: Text , Journal article
- Relation: British Journal of Sports Medicine Vol. 48, no. 16 (2014), p. 1200-1201
- Full Text:
- Reviewed:
- Description: Editorial
- Description: C1
- Authors: Kemp, Joanne , Crossley, Kay , Roos, Ewa , Ratzlaff, Charles
- Date: 2014
- Type: Text , Journal article
- Relation: British Journal of Sports Medicine Vol. 48, no. 16 (2014), p. 1200-1201
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- Description: Editorial
- Description: C1
Robust malware defense in industrial IoT applications using machine learning with selective adversarial samples
- Khoda, Mahbub, Imam, Tasadduq, Kamruzzaman, Joarder, Gondal, Iqbal, Rahman, Ashfaqur
- Authors: Khoda, Mahbub , Imam, Tasadduq , Kamruzzaman, Joarder , Gondal, Iqbal , Rahman, Ashfaqur
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Transactions on Industry Applications Vol.56, no 4. (2020), p. 4415-4424
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- Description: Industrial Internet of Things (IIoT) deploys edge devices to act as intermediaries between sensors and actuators and application servers or cloud services. Machine learning models have been widely used to thwart malware attacks in such edge devices. However, these models are vulnerable to adversarial attacks where attackers craft adversarial samples by introducing small perturbations to malware samples to fool a classifier to misclassify them as benign applications. Literature on deep learning networks proposes adversarial retraining as a defense mechanism where adversarial samples are combined with legitimate samples to retrain the classifier. However, existing works select such adversarial samples in a random fashion which degrades the classifier's performance. This work proposes two novel approaches for selecting adversarial samples to retrain a classifier. One, based on the distance from malware cluster center, and the other, based on a probability measure derived from a kernel based learning (KBL). Our experiments show that both of our sample selection methods outperform the random selection method and the KBL selection method improves detection accuracy by 6%. Also, while existing works focus on deep neural networks with respect to adversarial retraining, we additionally assess the impact of such adversarial samples on other classifiers and our proposed selective adversarial retraining approaches show similar performance improvement for these classifiers as well. The outcomes from the study can assist in designing robust security systems for IIoT applications.
- Authors: Khoda, Mahbub , Imam, Tasadduq , Kamruzzaman, Joarder , Gondal, Iqbal , Rahman, Ashfaqur
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Transactions on Industry Applications Vol.56, no 4. (2020), p. 4415-4424
- Full Text:
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- Description: Industrial Internet of Things (IIoT) deploys edge devices to act as intermediaries between sensors and actuators and application servers or cloud services. Machine learning models have been widely used to thwart malware attacks in such edge devices. However, these models are vulnerable to adversarial attacks where attackers craft adversarial samples by introducing small perturbations to malware samples to fool a classifier to misclassify them as benign applications. Literature on deep learning networks proposes adversarial retraining as a defense mechanism where adversarial samples are combined with legitimate samples to retrain the classifier. However, existing works select such adversarial samples in a random fashion which degrades the classifier's performance. This work proposes two novel approaches for selecting adversarial samples to retrain a classifier. One, based on the distance from malware cluster center, and the other, based on a probability measure derived from a kernel based learning (KBL). Our experiments show that both of our sample selection methods outperform the random selection method and the KBL selection method improves detection accuracy by 6%. Also, while existing works focus on deep neural networks with respect to adversarial retraining, we additionally assess the impact of such adversarial samples on other classifiers and our proposed selective adversarial retraining approaches show similar performance improvement for these classifiers as well. The outcomes from the study can assist in designing robust security systems for IIoT applications.
Financial view and profitability evaluation on multistate weighted k-out-of-n:F system reliability
- Khorshidi, Hadi, Gunawan, Indra, Ibrahim, Yousef
- Authors: Khorshidi, Hadi , Gunawan, Indra , Ibrahim, Yousef
- Date: 2014
- Type: Text , Journal article
- Relation: International Journal of Reliability and Safety Vol. 8, no. 2-4 (2014), p. 284-298
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- Description: A financial view is proposed for reliability evaluation of multi-state weighted k-out-of-n:F systems. Failure cost as the cost which is imposed on the components by failures is used to denote the importance weight of each component. The deterioration process of components over time is modelled by Markov chain. System failure behaviour is formulated by Universal Generating Function (UGF). Furthermore, the present value of system failure is calculated by considering time value of money. As a result, the system reliability is demonstrated as cost which is more sensible for managers. A numerical example is presented to illustrate the proposed approach. After that, a way is suggested to transform the system cost present value into system reliability value. MATLAB programming is developed to make a sensitivity analysis on example results. Therefore, the impact of maintenance activities is investigated to show how they can reduce system cost through improving the system reliability. Copyright © 2014 Inderscience Enterprises Ltd.
- Authors: Khorshidi, Hadi , Gunawan, Indra , Ibrahim, Yousef
- Date: 2014
- Type: Text , Journal article
- Relation: International Journal of Reliability and Safety Vol. 8, no. 2-4 (2014), p. 284-298
- Full Text:
- Reviewed:
- Description: A financial view is proposed for reliability evaluation of multi-state weighted k-out-of-n:F systems. Failure cost as the cost which is imposed on the components by failures is used to denote the importance weight of each component. The deterioration process of components over time is modelled by Markov chain. System failure behaviour is formulated by Universal Generating Function (UGF). Furthermore, the present value of system failure is calculated by considering time value of money. As a result, the system reliability is demonstrated as cost which is more sensible for managers. A numerical example is presented to illustrate the proposed approach. After that, a way is suggested to transform the system cost present value into system reliability value. MATLAB programming is developed to make a sensitivity analysis on example results. Therefore, the impact of maintenance activities is investigated to show how they can reduce system cost through improving the system reliability. Copyright © 2014 Inderscience Enterprises Ltd.
Data-Driven System Reliability and Failure Behavior Modeling Using FMECA
- Khorshidi, Hadi, Gunawan, Indra, Ibrahim, Yousef
- Authors: Khorshidi, Hadi , Gunawan, Indra , Ibrahim, Yousef
- Date: 2016
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 12, no. 3 (2016), p. 1253-1260
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- Description: System reliability modeling needs a large amount of data to estimate the parameters. In addition, reliability estimation is associated with uncertainty. This paper aims to propose a new method to evaluate the failure behavior and reliability of a large system using failure modes, effects, and criticality analysis (FMECA). Therefore, qualitative data based on the judgment of experts are used when data are not sufficient. The subjective data of failure modes and causes have been aggregated through the system to develop an overall failure index (OFI). This index not only represents the system reliability behavior, but also prioritizes corrective actions based on improvements in system failure. In addition, two optimization models are presented to select optimal actions subject to budget constraint. The associated costs of each corrective action are considered in risk evaluation. Finally, a case study of a manufacturing line is introduced to verify the applicability of the proposed method in industrial environments. The proposed method is compared with conventional FMECA approach. It is shown that the proposed method has a better performance in risk assessment. A sensitivity analysis is provided on the budget amount and the results are discussed. © 2015 IEEE.
- Authors: Khorshidi, Hadi , Gunawan, Indra , Ibrahim, Yousef
- Date: 2016
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 12, no. 3 (2016), p. 1253-1260
- Full Text:
- Reviewed:
- Description: System reliability modeling needs a large amount of data to estimate the parameters. In addition, reliability estimation is associated with uncertainty. This paper aims to propose a new method to evaluate the failure behavior and reliability of a large system using failure modes, effects, and criticality analysis (FMECA). Therefore, qualitative data based on the judgment of experts are used when data are not sufficient. The subjective data of failure modes and causes have been aggregated through the system to develop an overall failure index (OFI). This index not only represents the system reliability behavior, but also prioritizes corrective actions based on improvements in system failure. In addition, two optimization models are presented to select optimal actions subject to budget constraint. The associated costs of each corrective action are considered in risk evaluation. Finally, a case study of a manufacturing line is introduced to verify the applicability of the proposed method in industrial environments. The proposed method is compared with conventional FMECA approach. It is shown that the proposed method has a better performance in risk assessment. A sensitivity analysis is provided on the budget amount and the results are discussed. © 2015 IEEE.
Rectified softmax loss with all-sided cost sensitivity for age estimation
- Li, Daxiang, Ma, Xuan, Ren, Yaqiong, Teng, Shyh-Wei
- Authors: Li, Daxiang , Ma, Xuan , Ren, Yaqiong , Teng, Shyh-Wei
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 32551-32563
- Full Text:
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- Description: In Convolutional Neural Network (ConvNet) based age estimation algorithms, softmax loss is usually chosen as the loss function directly, and the problems of Cost Sensitivity (CS), such as class imbalance and misclassification cost difference between different classes, are not considered. Focus on these problems, this paper constructs a rectified softmax loss function with all-sided CS, and proposes a novel cost-sensitive ConvNet based age estimation algorithm. Firstly, a loss function is established for each age category to solve the imbalance of the number of training samples. Then, a cost matrix is defined to reflect the cost difference caused by misclassification between different classes, thus constructing a new cost-sensitive error function. Finally, the above methods are merged to construct a rectified softmax loss function for ConvNet model, and a corresponding Back Propagation (BP) training scheme is designed to enable ConvNet network to learn robust face representation for age estimation during the training phase. Simultaneously, the rectified softmax loss is theoretically proved that it satisfies the general conditions of the loss function used for classification. The effectiveness of the proposed method is verified by experiments on face image datasets of different races. © 2013 IEEE.
- Authors: Li, Daxiang , Ma, Xuan , Ren, Yaqiong , Teng, Shyh-Wei
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 32551-32563
- Full Text:
- Reviewed:
- Description: In Convolutional Neural Network (ConvNet) based age estimation algorithms, softmax loss is usually chosen as the loss function directly, and the problems of Cost Sensitivity (CS), such as class imbalance and misclassification cost difference between different classes, are not considered. Focus on these problems, this paper constructs a rectified softmax loss function with all-sided CS, and proposes a novel cost-sensitive ConvNet based age estimation algorithm. Firstly, a loss function is established for each age category to solve the imbalance of the number of training samples. Then, a cost matrix is defined to reflect the cost difference caused by misclassification between different classes, thus constructing a new cost-sensitive error function. Finally, the above methods are merged to construct a rectified softmax loss function for ConvNet model, and a corresponding Back Propagation (BP) training scheme is designed to enable ConvNet network to learn robust face representation for age estimation during the training phase. Simultaneously, the rectified softmax loss is theoretically proved that it satisfies the general conditions of the loss function used for classification. The effectiveness of the proposed method is verified by experiments on face image datasets of different races. © 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
- 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**
- 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**
Classifying transformer winding deformation fault types and degrees using FRA based on support vector machine
- Liu, Jiangnan, Zhao, Zhongyong, Tang, Chao, Yao, Chenguo, Li, Chengxiang, Islam, Syed
- Authors: Liu, Jiangnan , Zhao, Zhongyong , Tang, Chao , Yao, Chenguo , Li, Chengxiang , Islam, Syed
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 112494-112504
- Full Text:
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- Description: As an important part of power system, power transformer plays an irreplaceable role in the process of power transmission. Diagnosis of transformer's failure is of significance to maintain its safe and stable operation. Frequency response analysis (FRA) has been widely accepted as an effective tool for winding deformation fault diagnosis, which is one of the common failures for power transformers. However, there is no standard and reliable code for FRA interpretation as so far. In this paper, support vector machine (SVM) is combined with FRA to diagnose transformer faults. Furthermore, advanced optimization algorithms are also applied to improve the performance of models. A series of winding fault emulating experiments were carried out on an actual model transformer, the key features are extracted from measured FRA data, and the diagnostic model is trained and obtained, to arrive at an outcome for classifying the fault types and degrees of winding deformation faults with satisfactory accuracy. The diagnostic results indicate that this method has potential to be an intelligent, standardized, accurate and powerful tool.
- Authors: Liu, Jiangnan , Zhao, Zhongyong , Tang, Chao , Yao, Chenguo , Li, Chengxiang , Islam, Syed
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 112494-112504
- Full Text:
- Reviewed:
- Description: As an important part of power system, power transformer plays an irreplaceable role in the process of power transmission. Diagnosis of transformer's failure is of significance to maintain its safe and stable operation. Frequency response analysis (FRA) has been widely accepted as an effective tool for winding deformation fault diagnosis, which is one of the common failures for power transformers. However, there is no standard and reliable code for FRA interpretation as so far. In this paper, support vector machine (SVM) is combined with FRA to diagnose transformer faults. Furthermore, advanced optimization algorithms are also applied to improve the performance of models. A series of winding fault emulating experiments were carried out on an actual model transformer, the key features are extracted from measured FRA data, and the diagnostic model is trained and obtained, to arrive at an outcome for classifying the fault types and degrees of winding deformation faults with satisfactory accuracy. The diagnostic results indicate that this method has potential to be an intelligent, standardized, accurate and powerful tool.
Dual cost function model predictive direct speed control with duty ratio optimization for PMSM drives
- Liu, Ming, Hu, Jiefeng, Chan, Ka, Or, Siu, Ho, Siu, Xu, Wenzheng, Zhang, Xian
- Authors: Liu, Ming , Hu, Jiefeng , Chan, Ka , Or, Siu , Ho, Siu , Xu, Wenzheng , Zhang, Xian
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 126637-126647
- Full Text:
- Reviewed:
- Description: Traditional speed control of permanent magnet synchronous motors (PMSMs) includes a cascaded speed loop with proportional-integral (PI) regulators. The output of this outer speed loop, i.e. electromagnetic torque reference, is in turn fed to either the inner current controller or the direct torque controller. This cascaded control structure leads to relatively slow dynamic response, and more importantly, larger speed ripples. This paper presents a new dual cost function model predictive direct speed control (DCF-MPDSC) with duty ratio optimization for PMSM drives. By employing accurate system status prediction, optimized duty ratios between one zero voltage vector and one active voltage vector are firstly deduced based on the deadbeat criterion. Then, two separate cost functions are formulated sequentially to refine the combinations of voltage vectors, which provide two-degree-of-freedom control capability. Specifically, the first cost function results in better dynamic response, while the second one contributes to speed ripple reduction and steady-state offset elimination. The proposed control strategy has been validated by both Simulink simulation and hardware-in-the-loop (HIL) experiment. Compared to existing control methods, the proposed DCF-MPDSC can reach the speed reference rapidly with very small speed ripple and offset. © 2013 IEEE.
- Description: This work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region (HKSAR) Government under Grant R5020-18, and in part by the Innovation and Technology Commission of the HKSAR Government to the Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center under Grant K-BBY1.
- Authors: Liu, Ming , Hu, Jiefeng , Chan, Ka , Or, Siu , Ho, Siu , Xu, Wenzheng , Zhang, Xian
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 126637-126647
- Full Text:
- Reviewed:
- Description: Traditional speed control of permanent magnet synchronous motors (PMSMs) includes a cascaded speed loop with proportional-integral (PI) regulators. The output of this outer speed loop, i.e. electromagnetic torque reference, is in turn fed to either the inner current controller or the direct torque controller. This cascaded control structure leads to relatively slow dynamic response, and more importantly, larger speed ripples. This paper presents a new dual cost function model predictive direct speed control (DCF-MPDSC) with duty ratio optimization for PMSM drives. By employing accurate system status prediction, optimized duty ratios between one zero voltage vector and one active voltage vector are firstly deduced based on the deadbeat criterion. Then, two separate cost functions are formulated sequentially to refine the combinations of voltage vectors, which provide two-degree-of-freedom control capability. Specifically, the first cost function results in better dynamic response, while the second one contributes to speed ripple reduction and steady-state offset elimination. The proposed control strategy has been validated by both Simulink simulation and hardware-in-the-loop (HIL) experiment. Compared to existing control methods, the proposed DCF-MPDSC can reach the speed reference rapidly with very small speed ripple and offset. © 2013 IEEE.
- Description: This work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region (HKSAR) Government under Grant R5020-18, and in part by the Innovation and Technology Commission of the HKSAR Government to the Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center under Grant K-BBY1.