A comprehensive spectrum trading scheme based on market competition, reputation and buyer specific requirements
- Hassan, Md Rakib, Karmakar, Gour, Kamruzzaman, Joarder, Srinivasan, Bala
- Authors: Hassan, Md Rakib , Karmakar, Gour , Kamruzzaman, Joarder , Srinivasan, Bala
- Date: 2015
- Type: Text , Journal article
- Relation: Computer Networks Vol. 84, no. (2015), p. 17-31
- Full Text:
- Reviewed:
- Description: In the exclusive-use model of spectrum trading, cognitive radio devices or secondary users can buy spectrum resources from licensed users or primary users for a short or long period of time. Considering such spectrum access, a trading model is introduced where a buyer can select a set of candidate sellers based on their reputation and their offers in fulfilling its requirements, namely, offered signal quality, contract duration, coverage and bandwidth. Similarly, a seller can assess a buyer as a potential trading partner considering the buyer's reliability, which the seller can derive from the buyer's reputation and financial profile. In our scheme, seller reputation or buyer reliability can be either obtained from a reputation brokerage service, if one exists, or calculated using our model. Since in a competitive market, the price of a seller depends on that of other sellers, game theory is used to model the competition among multiple sellers. An optimization technique is used by a buyer to select the best seller(s) and optimize purchase to maximize its utility. This may result in buying from multiple sellers of certain amount of bandwidth from each, depending on price and meeting requirements and budget constraints. Stability of the model is analyzed and performance evaluation shows that it benefits sellers and buyers in terms of profit and throughput, respectively. © 2015 Elsevier B.V. All rights reserved.
- Authors: Hassan, Md Rakib , Karmakar, Gour , Kamruzzaman, Joarder , Srinivasan, Bala
- Date: 2015
- Type: Text , Journal article
- Relation: Computer Networks Vol. 84, no. (2015), p. 17-31
- Full Text:
- Reviewed:
- Description: In the exclusive-use model of spectrum trading, cognitive radio devices or secondary users can buy spectrum resources from licensed users or primary users for a short or long period of time. Considering such spectrum access, a trading model is introduced where a buyer can select a set of candidate sellers based on their reputation and their offers in fulfilling its requirements, namely, offered signal quality, contract duration, coverage and bandwidth. Similarly, a seller can assess a buyer as a potential trading partner considering the buyer's reliability, which the seller can derive from the buyer's reputation and financial profile. In our scheme, seller reputation or buyer reliability can be either obtained from a reputation brokerage service, if one exists, or calculated using our model. Since in a competitive market, the price of a seller depends on that of other sellers, game theory is used to model the competition among multiple sellers. An optimization technique is used by a buyer to select the best seller(s) and optimize purchase to maximize its utility. This may result in buying from multiple sellers of certain amount of bandwidth from each, depending on price and meeting requirements and budget constraints. Stability of the model is analyzed and performance evaluation shows that it benefits sellers and buyers in terms of profit and throughput, respectively. © 2015 Elsevier B.V. All rights reserved.
Simplifying and improving ant-based clustering
- Tan, Swee, Ting, Kaiming, Teng, Shyh
- Authors: Tan, Swee , Ting, Kaiming , Teng, Shyh
- Date: 2011
- Type: Text , Conference paper
- Relation: 11th International Conference on Computational Science, ICCS 2011; Singapore, Singapore; 1st-3rd June 2011, published in Procedia Computer Science Vol. 4, p. 46-55
- Full Text:
- Reviewed:
- Description: Ant-based clustering (ABC) is a data clustering approach inspired from cemetery formation activities observed in real ant colonies. Building upon the premise of collective intelligence, such an approach uses multiple ant-like agents and a mixture of heuristics, in order to create systems that are capable of clustering real-world data. Many recently proposed ABC systems have shown competitive results, but these systems are geared towards adding new heuristics, resulting in increasingly complex systems that are harder to understand and improve. In contrast to this direction, we demonstrate that a state-of-the-art ABC system can be systematically evaluated and then simplified. The streamlined model, which we call SABC, differs fundamentally from traditional ABC systems as it does not use the ant-colony and several key components. Yet, our empirical study shows that SABC performs more effectively and effciently than the state-of-the-art ABC system.
- Authors: Tan, Swee , Ting, Kaiming , Teng, Shyh
- Date: 2011
- Type: Text , Conference paper
- Relation: 11th International Conference on Computational Science, ICCS 2011; Singapore, Singapore; 1st-3rd June 2011, published in Procedia Computer Science Vol. 4, p. 46-55
- Full Text:
- Reviewed:
- Description: Ant-based clustering (ABC) is a data clustering approach inspired from cemetery formation activities observed in real ant colonies. Building upon the premise of collective intelligence, such an approach uses multiple ant-like agents and a mixture of heuristics, in order to create systems that are capable of clustering real-world data. Many recently proposed ABC systems have shown competitive results, but these systems are geared towards adding new heuristics, resulting in increasingly complex systems that are harder to understand and improve. In contrast to this direction, we demonstrate that a state-of-the-art ABC system can be systematically evaluated and then simplified. The streamlined model, which we call SABC, differs fundamentally from traditional ABC systems as it does not use the ant-colony and several key components. Yet, our empirical study shows that SABC performs more effectively and effciently than the state-of-the-art ABC system.
A method to improve transparency of electronic election process without identification
- Alamuti, Roghayeh, Barjini, Hassan, Khandelwal, Manoj, Jafarabad, Mohammad
- Authors: Alamuti, Roghayeh , Barjini, Hassan , Khandelwal, Manoj , Jafarabad, Mohammad
- Date: 2015
- Type: Text , Conference proceedings
- Full Text:
- Description: Transparency of bank accounts, nowadays, is an undeniable necessity, but no one denies that definite transparency throughout election process is not realized thus far in the world. This calls for fundamental changes in traditional electronic election methods. The new method must close the way for any complaints by the candidate as to the voting process as the public completely trusts in the voting mechanism. Synchronizing voting and votes counting improves the public's trust in the results of election. The proposed secure room-corridor of electronic voting employs election watchers and reports real time results of election along with observance of confidentiality of the votes. © 2015 The Authors.
- Authors: Alamuti, Roghayeh , Barjini, Hassan , Khandelwal, Manoj , Jafarabad, Mohammad
- Date: 2015
- Type: Text , Conference proceedings
- Full Text:
- Description: Transparency of bank accounts, nowadays, is an undeniable necessity, but no one denies that definite transparency throughout election process is not realized thus far in the world. This calls for fundamental changes in traditional electronic election methods. The new method must close the way for any complaints by the candidate as to the voting process as the public completely trusts in the voting mechanism. Synchronizing voting and votes counting improves the public's trust in the results of election. The proposed secure room-corridor of electronic voting employs election watchers and reports real time results of election along with observance of confidentiality of the votes. © 2015 The Authors.
Multi-agent systems in ICT enabled smart grid : A status update on technology framework and applications
- Shawon, Mohammad, Muyeen, S., Ghosh, Arindam, Islam, Syed, Baptista, Murilo
- Authors: Shawon, Mohammad , Muyeen, S. , Ghosh, Arindam , Islam, Syed , Baptista, Murilo
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 97959-97973
- Full Text:
- Reviewed:
- Description: Multi-agent-based smart grid applications have gained much attention in recent times. At the same time, information and communication technology (ICT) has become a crucial part of the smart grid infrastructure. The key intention of this work is to present a comprehensive review of the literature and technological frameworks for the application of multi-agent system (MAS) and ICT infrastructure usages in smart grid implementations. In the smart grid, agents are defined as intelligent entities with the ability to take decisions and acting flexibly and autonomously according to their built-in intelligence utilizing previous experiences. Whereas, ICT enables conventional grid turned into the smart grid through data and information exchange. This paper summarizes the multi-agent concept of smart grid highlighting their applications through a detailed and extensive literature survey on the related topics. In addition to the above, a particular focus has been put on the ICT standards, including IEC 61850 incorporating ICT with MAS. Finally, a laboratory framework concepts have been added highlighting the implementation of IEC 61850.
- Authors: Shawon, Mohammad , Muyeen, S. , Ghosh, Arindam , Islam, Syed , Baptista, Murilo
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 97959-97973
- Full Text:
- Reviewed:
- Description: Multi-agent-based smart grid applications have gained much attention in recent times. At the same time, information and communication technology (ICT) has become a crucial part of the smart grid infrastructure. The key intention of this work is to present a comprehensive review of the literature and technological frameworks for the application of multi-agent system (MAS) and ICT infrastructure usages in smart grid implementations. In the smart grid, agents are defined as intelligent entities with the ability to take decisions and acting flexibly and autonomously according to their built-in intelligence utilizing previous experiences. Whereas, ICT enables conventional grid turned into the smart grid through data and information exchange. This paper summarizes the multi-agent concept of smart grid highlighting their applications through a detailed and extensive literature survey on the related topics. In addition to the above, a particular focus has been put on the ICT standards, including IEC 61850 incorporating ICT with MAS. Finally, a laboratory framework concepts have been added highlighting the implementation of IEC 61850.
Diagnosing transformer winding deformation faults based on the analysis of binary image obtained from FRA signature
- Zhao, Zhongyong, Yao, Chenguo, Tang, Chao, Li, Chengxiang, Yan, Fayou, Islam, Syed
- Authors: Zhao, Zhongyong , Yao, Chenguo , Tang, Chao , Li, Chengxiang , Yan, Fayou , Islam, Syed
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 40463-40474
- Full Text:
- Reviewed:
- Description: Frequency response analysis (FRA) has been widely accepted as a diagnostic tool for power transformer winding deformation faults. Typically, both amplitude-frequency and phase-frequency signatures are obtained by an FRA analyzer. However, most existing FRA analyzers use only the information on amplitude-frequency signature, while phase-frequency information is neglected. It is also found that in some cases, the diagnostic results obtained by FRA amplitude-frequency signatures do not comply with some hard evidence. This paper introduces a winding deformation diagnostic method based on the analysis of binary images obtained from FRA signatures to improve FRA outcomes. The digital image processing technique is used to process the binary image and obtain a diagnostic indicator, to arrive at an outcome for interpreting winding faults with improved accuracy.
- Authors: Zhao, Zhongyong , Yao, Chenguo , Tang, Chao , Li, Chengxiang , Yan, Fayou , Islam, Syed
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 40463-40474
- Full Text:
- Reviewed:
- Description: Frequency response analysis (FRA) has been widely accepted as a diagnostic tool for power transformer winding deformation faults. Typically, both amplitude-frequency and phase-frequency signatures are obtained by an FRA analyzer. However, most existing FRA analyzers use only the information on amplitude-frequency signature, while phase-frequency information is neglected. It is also found that in some cases, the diagnostic results obtained by FRA amplitude-frequency signatures do not comply with some hard evidence. This paper introduces a winding deformation diagnostic method based on the analysis of binary images obtained from FRA signatures to improve FRA outcomes. The digital image processing technique is used to process the binary image and obtain a diagnostic indicator, to arrive at an outcome for interpreting winding faults with improved accuracy.
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
- 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.
- 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.
Continuous patient monitoring with a patient centric agent : A block architecture
- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Access Vol. 6, no. (2018), p. 32700-32726
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including continuous remote patient monitoring (RPM). However, the complexity of RPM architectures, the size of data sets generated and limited power capacity of devices make RPM challenging. In this paper, we propose a tier-based End to End architecture for continuous patient monitoring that has a patient centric agent (PCA) as its center piece. The PCA manages a blockchain component to preserve privacy when data streaming from body area sensors needs to be stored securely. The PCA based architecture includes a lightweight communication protocol to enforce security of data through different segments of a continuous, real time patient monitoring architecture. The architecture includes the insertion of data into a personal blockchain to facilitate data sharing amongst healthcare professionals and integration into electronic health records while ensuring privacy is maintained. The blockchain is customized for RPM with modifications that include having the PCA select a Miner to reduce computational effort, enabling the PCA to manage multiple blockchains for the same patient, and the modification of each block with a prefix tree to minimize energy consumption and incorporate secure transaction payments. Simulation results demonstrate that security and privacy can be enhanced in RPM with the PCA based End to End architecture.
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Access Vol. 6, no. (2018), p. 32700-32726
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including continuous remote patient monitoring (RPM). However, the complexity of RPM architectures, the size of data sets generated and limited power capacity of devices make RPM challenging. In this paper, we propose a tier-based End to End architecture for continuous patient monitoring that has a patient centric agent (PCA) as its center piece. The PCA manages a blockchain component to preserve privacy when data streaming from body area sensors needs to be stored securely. The PCA based architecture includes a lightweight communication protocol to enforce security of data through different segments of a continuous, real time patient monitoring architecture. The architecture includes the insertion of data into a personal blockchain to facilitate data sharing amongst healthcare professionals and integration into electronic health records while ensuring privacy is maintained. The blockchain is customized for RPM with modifications that include having the PCA select a Miner to reduce computational effort, enabling the PCA to manage multiple blockchains for the same patient, and the modification of each block with a prefix tree to minimize energy consumption and incorporate secure transaction payments. Simulation results demonstrate that security and privacy can be enhanced in RPM with the PCA based End to End architecture.
A technique for parallel share-frequent sensor pattern mining from wireless sensor networks
- Rashid, Md. Mamunur, Gondal, Iqbal, Kamruzzaman, Joarder
- Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2014
- Type: Text , Conference paper
- Relation: 14th Annual International Conference on Computational Science, ICCS 2014; Cairns, Australia; 10th-12th June 2014; published in Procedia Computer Science p. 124-133
- Full Text:
- Reviewed:
- Description: WSNs generate huge amount of data in the form of streams and mining useful knowledge from these streams is a challenging task. Existing works generate sensor association rules using occurrence frequency of patterns with binary frequency (either absent or present) or support of a pattern as a criterion. However, considering the binary frequency or support of a pattern may not be a sufficient indicator for finding meaningful patterns from WSN data because it only reflects the number of epochs in the sensor data which contain that pattern. The share measure of sensorsets could discover useful knowledge about numerical values associated with sensor in a sensor database. Therefore, in this paper, we propose a new type of behavioral pattern called share-frequent sensor patterns by considering the non-binary frequency values of sensors in epochs. To discover share-frequent sensor patterns from sensor dataset, we propose a novel parallel technique. In this technique, we develop a novel tree structure, called parallel share-frequent sensor pattern tree (PShrFSP-tree) that is constructed at each local node independently, by capturing the database contents to generate the candidate patterns using a pattern growth technique with a single scan and then merges the locally generated candidate patterns at the final stage to generate global share-frequent sensor patterns. Comprehensive experimental results show that our proposed model is very efficient for mining share-frequent patterns from WSN data in terms of time and scalability.
- Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2014
- Type: Text , Conference paper
- Relation: 14th Annual International Conference on Computational Science, ICCS 2014; Cairns, Australia; 10th-12th June 2014; published in Procedia Computer Science p. 124-133
- Full Text:
- Reviewed:
- Description: WSNs generate huge amount of data in the form of streams and mining useful knowledge from these streams is a challenging task. Existing works generate sensor association rules using occurrence frequency of patterns with binary frequency (either absent or present) or support of a pattern as a criterion. However, considering the binary frequency or support of a pattern may not be a sufficient indicator for finding meaningful patterns from WSN data because it only reflects the number of epochs in the sensor data which contain that pattern. The share measure of sensorsets could discover useful knowledge about numerical values associated with sensor in a sensor database. Therefore, in this paper, we propose a new type of behavioral pattern called share-frequent sensor patterns by considering the non-binary frequency values of sensors in epochs. To discover share-frequent sensor patterns from sensor dataset, we propose a novel parallel technique. In this technique, we develop a novel tree structure, called parallel share-frequent sensor pattern tree (PShrFSP-tree) that is constructed at each local node independently, by capturing the database contents to generate the candidate patterns using a pattern growth technique with a single scan and then merges the locally generated candidate patterns at the final stage to generate global share-frequent sensor patterns. Comprehensive experimental results show that our proposed model is very efficient for mining share-frequent patterns from WSN data in terms of time and scalability.
A machine vision based automatic optical inspection system for measuring drilling quality of printed circuit boards
- Wang, Wei, Chen, Shang-Liang, Chen, Liang-Bi, Chang, Wan-Jung
- Authors: Wang, Wei , Chen, Shang-Liang , Chen, Liang-Bi , Chang, Wan-Jung
- Date: 2017
- Type: Text , Journal article
- Relation: IEEE Access Vol. 5, no. (2017), p. 10817-10833
- Full Text:
- Reviewed:
- Description: In this paper, we develop and put into practice an automatic optical inspection (AOI) system based on machine vision to check the holes on a printed circuit board (PCB). We incorporate the hardware and software. For the hardware part, we combine a PC, the three-axis positioning system, a lighting device, and charge-coupled device cameras. For the software part, we utilize image registration, image segmentation, drill numbering, drill contrast, and defect displays to achieve this system. Results indicated that an accuracy of 5 mu m could be achieved in errors of the PCB holes allowing comparisons to be made. This is significant in inspecting the missing, the multi-hole, and the incorrect location of the holes. However, previous work only focuses on one or other feature of the holes. Our research is able to assess multiple features: missing holes, incorrectly located holes, and excessive holes. Equally, our results could be displayed as a bar chart and target plot. This has not been achieved before. These displays help users to analyze the causes of errors and immediately correct the problems. In addition, this AOI system is valuable for checking a large number of holes and finding out the defective ones on a PCB. Meanwhile, we apply a 0.1-mm image resolution, which is better than others used in industry. We set a detecting standard based on 2-mm diameter of circles to diagnose the quality of the holes within 10 s.
- Authors: Wang, Wei , Chen, Shang-Liang , Chen, Liang-Bi , Chang, Wan-Jung
- Date: 2017
- Type: Text , Journal article
- Relation: IEEE Access Vol. 5, no. (2017), p. 10817-10833
- Full Text:
- Reviewed:
- Description: In this paper, we develop and put into practice an automatic optical inspection (AOI) system based on machine vision to check the holes on a printed circuit board (PCB). We incorporate the hardware and software. For the hardware part, we combine a PC, the three-axis positioning system, a lighting device, and charge-coupled device cameras. For the software part, we utilize image registration, image segmentation, drill numbering, drill contrast, and defect displays to achieve this system. Results indicated that an accuracy of 5 mu m could be achieved in errors of the PCB holes allowing comparisons to be made. This is significant in inspecting the missing, the multi-hole, and the incorrect location of the holes. However, previous work only focuses on one or other feature of the holes. Our research is able to assess multiple features: missing holes, incorrectly located holes, and excessive holes. Equally, our results could be displayed as a bar chart and target plot. This has not been achieved before. These displays help users to analyze the causes of errors and immediately correct the problems. In addition, this AOI system is valuable for checking a large number of holes and finding out the defective ones on a PCB. Meanwhile, we apply a 0.1-mm image resolution, which is better than others used in industry. We set a detecting standard based on 2-mm diameter of circles to diagnose the quality of the holes within 10 s.
Improved method to obtain the online impulse frequency response signature of a power transformer by multi scale complex CWT
- Zhao, Zhongyong, Tang, Chao, Yao, Chenguo, Zhou, Qu, Xu, Lingna, Gui, Yingang, Islam, Syed
- Authors: Zhao, Zhongyong , Tang, Chao , Yao, Chenguo , Zhou, Qu , Xu, Lingna , Gui, Yingang , Islam, Syed
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Access Vol. 6, no. (2018), p. 48934-48945
- Full Text:
- Reviewed:
- Description: Online impulse frequency response analysis (IFRA) has proven to be a promising method to detect and diagnose the transformer winding mechanical faults when the transformer is in service. However, the existing fast Fourier transform (FFT) is actually not suitable for processing the transient signals in online IFRA. The field test result also shows that the IFRA signature obtained by FFT is easily distorted by noise. An improved method to obtain the online IFRA signature based on multi-scale complex continuous wavelet transform is proposed. The electrical model simulation and online experiment indicate the superiority of the wavelet transform compared with FFT. This paper provides guidance on the actual application of the online IFRA method.
- Authors: Zhao, Zhongyong , Tang, Chao , Yao, Chenguo , Zhou, Qu , Xu, Lingna , Gui, Yingang , Islam, Syed
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Access Vol. 6, no. (2018), p. 48934-48945
- Full Text:
- Reviewed:
- Description: Online impulse frequency response analysis (IFRA) has proven to be a promising method to detect and diagnose the transformer winding mechanical faults when the transformer is in service. However, the existing fast Fourier transform (FFT) is actually not suitable for processing the transient signals in online IFRA. The field test result also shows that the IFRA signature obtained by FFT is easily distorted by noise. An improved method to obtain the online IFRA signature based on multi-scale complex continuous wavelet transform is proposed. The electrical model simulation and online experiment indicate the superiority of the wavelet transform compared with FFT. This paper provides guidance on the actual application of the online IFRA method.
Identification of coherent generators by support vector clustering with an embedding strategy
- Babaei, Mehdi, Muyeen, S., Islam, Syed
- Authors: Babaei, Mehdi , Muyeen, S. , Islam, Syed
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 105420-105431
- Full Text:
- Reviewed:
- Description: Identification of coherent generators (CGs) is necessary for the area-based monitoring and protection system of a wide area power system. Synchrophasor has enabled smarter monitoring and control measures to be devised; hence, measurement-based methodologies can be implemented in online applications to identify the CGs. This paper presents a new framework for coherency identification that is based on the dynamic coupling of generators. A distance matrix that contains the dissimilarity indices between any pair of generators is constructed from the pairwise dynamic coupling of generators after the post-disturbance data are obtained by phasor measurement units (PMUs). The dataset is embedded in Euclidean space to produce a new dataset with a metric distance between the points, and then the support vector clustering (SVC) technique is applied to the embedded dataset to identify the final clusters of generators. Unlike other clustering methods that need a priori knowledge about the number of clusters or the parameters of clustering, this information is set in an automatic search procedure that results in the optimal number of clusters. The algorithm is verified by time-domain simulations of defined scenarios in 39 bus and 118 bus test systems. Finally, the clustering result of 39 bus systems is validated by cluster validity measures, and a comparative study investigates the efficacy of the proposed algorithm to cluster the generators with an optimal number of clusters and also its computational efficiency compared with other clustering methods.
- Authors: Babaei, Mehdi , Muyeen, S. , Islam, Syed
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 105420-105431
- Full Text:
- Reviewed:
- Description: Identification of coherent generators (CGs) is necessary for the area-based monitoring and protection system of a wide area power system. Synchrophasor has enabled smarter monitoring and control measures to be devised; hence, measurement-based methodologies can be implemented in online applications to identify the CGs. This paper presents a new framework for coherency identification that is based on the dynamic coupling of generators. A distance matrix that contains the dissimilarity indices between any pair of generators is constructed from the pairwise dynamic coupling of generators after the post-disturbance data are obtained by phasor measurement units (PMUs). The dataset is embedded in Euclidean space to produce a new dataset with a metric distance between the points, and then the support vector clustering (SVC) technique is applied to the embedded dataset to identify the final clusters of generators. Unlike other clustering methods that need a priori knowledge about the number of clusters or the parameters of clustering, this information is set in an automatic search procedure that results in the optimal number of clusters. The algorithm is verified by time-domain simulations of defined scenarios in 39 bus and 118 bus test systems. Finally, the clustering result of 39 bus systems is validated by cluster validity measures, and a comparative study investigates the efficacy of the proposed algorithm to cluster the generators with an optimal number of clusters and also its computational efficiency compared with other clustering methods.
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:
- 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.
- 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.
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
- Full Text:
- Reviewed:
- 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:
- Reviewed:
- 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.
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.
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:
- 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.
- 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 mechanical port machine based hybrid electric vehicle using reduced switch converters
- Bizhani, Hamed, Yao, Gang, Muyeen, S., Islam, Syed, Ben-Brahim, Lazhar
- Authors: Bizhani, Hamed , Yao, Gang , Muyeen, S. , Islam, Syed , Ben-Brahim, Lazhar
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 33665-33676
- Full Text:
- Reviewed:
- Description: Due to the increased environmental pollution, hybrid vehicles have attracted enormous attention in today's society. The two most important factors in designing these vehicles are size and weight. For this purpose, some researchers have presented the use of the dual-mechanical-port machine (DMPM) in hybrid electric vehicles (HEVs). This paper presents two modified converter topologies with a reduced number of switching devices for use on DMPM-based HEVs, with the goal of reducing the overall size and weight of the system. Beside the design of the DMPM in the series-parallel HEV structure along with the energy management unit, the conventional back-to-back (BB) converter is replaced with nine-switch (NS) and five-leg (FL) converters. These converters have never been examined for the DMPM-based HEV, and therefore, the objective of this paper is to reveal the operational characteristics and power flow mechanism of this machine using the NS and FL converters. The simulation analysis is carried out using MATLAB/Simulink considering all HEV operational modes. In addition, two proposed and the conventional converters are compared in terms of losses, maximum achievable voltages, required dc-link voltages, the rating of the components, and torque ripple, and finally, a recommendation is made based on the obtained results.
- Authors: Bizhani, Hamed , Yao, Gang , Muyeen, S. , Islam, Syed , Ben-Brahim, Lazhar
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 33665-33676
- Full Text:
- Reviewed:
- Description: Due to the increased environmental pollution, hybrid vehicles have attracted enormous attention in today's society. The two most important factors in designing these vehicles are size and weight. For this purpose, some researchers have presented the use of the dual-mechanical-port machine (DMPM) in hybrid electric vehicles (HEVs). This paper presents two modified converter topologies with a reduced number of switching devices for use on DMPM-based HEVs, with the goal of reducing the overall size and weight of the system. Beside the design of the DMPM in the series-parallel HEV structure along with the energy management unit, the conventional back-to-back (BB) converter is replaced with nine-switch (NS) and five-leg (FL) converters. These converters have never been examined for the DMPM-based HEV, and therefore, the objective of this paper is to reveal the operational characteristics and power flow mechanism of this machine using the NS and FL converters. The simulation analysis is carried out using MATLAB/Simulink considering all HEV operational modes. In addition, two proposed and the conventional converters are compared in terms of losses, maximum achievable voltages, required dc-link voltages, the rating of the components, and torque ripple, and finally, a recommendation is made based on the obtained results.
Low-power wide-area networks : design goals, architecture, suitability to use cases and research challenges
- Buurman, Ben, Kamruzzaman, Joarder, Karmakar, Gour, Islam, Syed
- Authors: Buurman, Ben , Kamruzzaman, Joarder , Karmakar, Gour , Islam, Syed
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 17179-17220
- Full Text:
- Reviewed:
- Description: Previous survey articles on Low-Powered Wide-Area Networks (LPWANs) lack a systematic analysis of the design goals of LPWAN and the design decisions adopted by various commercially available and emerging LPWAN technologies, and no study has analysed how their design decisions impact their ability to meet design goals. Assessing a technology's ability to meet design goals is essential in determining suitable technologies for a given application. To address these gaps, we have analysed six prominent design goals and identified the design decisions used to meet each goal in the eight LPWAN technologies, ranging from technical consideration to business model, and determined which specific technique in a design decision will help meet each goal to the greatest extent. System architecture and specifications are presented for those LPWAN solutions, and their ability to meet each design goal is evaluated. We outline seventeen use cases across twelve domains that require large low power network infrastructure and prioritise each design goal's importance to those applications as Low, Moderate, or High. Using these priorities and each technology's suitability for meeting design goals, we suggest appropriate LPWAN technologies for each use case. Finally, a number of research challenges are presented for current and future technologies. © 2013 IEEE.
- Authors: Buurman, Ben , Kamruzzaman, Joarder , Karmakar, Gour , Islam, Syed
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 17179-17220
- Full Text:
- Reviewed:
- Description: Previous survey articles on Low-Powered Wide-Area Networks (LPWANs) lack a systematic analysis of the design goals of LPWAN and the design decisions adopted by various commercially available and emerging LPWAN technologies, and no study has analysed how their design decisions impact their ability to meet design goals. Assessing a technology's ability to meet design goals is essential in determining suitable technologies for a given application. To address these gaps, we have analysed six prominent design goals and identified the design decisions used to meet each goal in the eight LPWAN technologies, ranging from technical consideration to business model, and determined which specific technique in a design decision will help meet each goal to the greatest extent. System architecture and specifications are presented for those LPWAN solutions, and their ability to meet each design goal is evaluated. We outline seventeen use cases across twelve domains that require large low power network infrastructure and prioritise each design goal's importance to those applications as Low, Moderate, or High. Using these priorities and each technology's suitability for meeting design goals, we suggest appropriate LPWAN technologies for each use case. Finally, a number of research challenges are presented for current and future technologies. © 2013 IEEE.
Impact of load ramping on power transformer dissolved gas analysis
- Cui, Huize, Yang, Liuging, Li, Shengtao, Qu, Guanghao, Wang, Hao, Abu-Siada, Ahmed, Islam, Syed
- Authors: Cui, Huize , Yang, Liuging , Li, Shengtao , Qu, Guanghao , Wang, Hao , Abu-Siada, Ahmed , Islam, Syed
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 170343-170351
- Full Text:
- Reviewed:
- Description: Dissolved gas in oil analysis (DGA) is one of the most reliable condition monitoring techniques, which is currently used by the industry to detect incipient faults within the power transformers. While the technique is well matured since the development of various offline and online measurement techniques along with various interpretation methods, no much attention was given so far to the oil sampling time and its correlation with the transformer loading. A power transformer loading is subject to continuous daily and seasonal variations, which is expected to increase with the increased penetration level of renewable energy sources of intermittent characteristics, such as photovoltaic (PV) and wind energy into the current electricity grids. Generating unit transformers also undergoes similar loading variations to follow the demand, particularly in the new electricity market. As such, the insulation system within the power transformers is expected to exhibit operating temperature variations due to the continuous ramping up and down of the generation and load. If the oil is sampled for the DGA measurement during such ramping cycles, results will not be accurate, and a fault may be reported due to a gas evolution resulting from such temporarily loading variation. This paper is aimed at correlating the generation and load ramping with the DGA measurements through extensive experimental analyses. The results reveal a strong correlation between the sampling time and the generation/load ramping. The experimental results show the effect of load variations on the gas generation and demonstrate the vulnerabilities of misinterpretation of transformer faults resulting from temporary gas evolution. To achieve accurate DGA, transformer loading profile during oil sampling for the DGA measurement should be available. Based on the initial investigation in this paper, the more accurate DGA results can be achieved after a ramping down cycle of the load. This sampling time could be defined as an optimum oil sampling time for transformer DGA.
- Authors: Cui, Huize , Yang, Liuging , Li, Shengtao , Qu, Guanghao , Wang, Hao , Abu-Siada, Ahmed , Islam, Syed
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 170343-170351
- Full Text:
- Reviewed:
- Description: Dissolved gas in oil analysis (DGA) is one of the most reliable condition monitoring techniques, which is currently used by the industry to detect incipient faults within the power transformers. While the technique is well matured since the development of various offline and online measurement techniques along with various interpretation methods, no much attention was given so far to the oil sampling time and its correlation with the transformer loading. A power transformer loading is subject to continuous daily and seasonal variations, which is expected to increase with the increased penetration level of renewable energy sources of intermittent characteristics, such as photovoltaic (PV) and wind energy into the current electricity grids. Generating unit transformers also undergoes similar loading variations to follow the demand, particularly in the new electricity market. As such, the insulation system within the power transformers is expected to exhibit operating temperature variations due to the continuous ramping up and down of the generation and load. If the oil is sampled for the DGA measurement during such ramping cycles, results will not be accurate, and a fault may be reported due to a gas evolution resulting from such temporarily loading variation. This paper is aimed at correlating the generation and load ramping with the DGA measurements through extensive experimental analyses. The results reveal a strong correlation between the sampling time and the generation/load ramping. The experimental results show the effect of load variations on the gas generation and demonstrate the vulnerabilities of misinterpretation of transformer faults resulting from temporary gas evolution. To achieve accurate DGA, transformer loading profile during oil sampling for the DGA measurement should be available. Based on the initial investigation in this paper, the more accurate DGA results can be achieved after a ramping down cycle of the load. This sampling time could be defined as an optimum oil sampling time for transformer DGA.
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
- 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.
- 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.
Quantifying success in science : an overview
- Bai, Xiaomei, Pan, Habxiao, Hou, Jie, Guo, Teng, Lee, Ivan, Xia, Feng
- Authors: Bai, Xiaomei , Pan, Habxiao , Hou, Jie , Guo, Teng , Lee, Ivan , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 123200-123214
- Full Text:
- Reviewed:
- Description: Quantifying success in science plays a key role in guiding funding allocations, recruitment decisions, and rewards. Recently, a significant amount of progresses have been made towards quantifying success in science. This lack of detailed analysis and summary continues a practical issue. The literature reports the factors influencing scholarly impact and evaluation methods and indices aimed at overcoming this crucial weakness. We focus on categorizing and reviewing the current development on evaluation indices of scholarly impact, including paper impact, scholar impact, and journal impact. Besides, we summarize the issues of existing evaluation methods and indices, investigate the open issues and challenges, and provide possible solutions, including the pattern of collaboration impact, unified evaluation standards, implicit success factor mining, dynamic academic network embedding, and scholarly impact inflation. This paper should help the researchers obtaining a broader understanding of quantifying success in science, and identifying some potential research directions. © 2013 IEEE.
- Description: This work was supported in part by the Liaoning Provincial Key Research and Development Guidance Project under Grant 2018104021, and in part by the Liaoning Provincial Natural Fund Guidance Plan under Grant 20180550011.
- Authors: Bai, Xiaomei , Pan, Habxiao , Hou, Jie , Guo, Teng , Lee, Ivan , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 123200-123214
- Full Text:
- Reviewed:
- Description: Quantifying success in science plays a key role in guiding funding allocations, recruitment decisions, and rewards. Recently, a significant amount of progresses have been made towards quantifying success in science. This lack of detailed analysis and summary continues a practical issue. The literature reports the factors influencing scholarly impact and evaluation methods and indices aimed at overcoming this crucial weakness. We focus on categorizing and reviewing the current development on evaluation indices of scholarly impact, including paper impact, scholar impact, and journal impact. Besides, we summarize the issues of existing evaluation methods and indices, investigate the open issues and challenges, and provide possible solutions, including the pattern of collaboration impact, unified evaluation standards, implicit success factor mining, dynamic academic network embedding, and scholarly impact inflation. This paper should help the researchers obtaining a broader understanding of quantifying success in science, and identifying some potential research directions. © 2013 IEEE.
- Description: This work was supported in part by the Liaoning Provincial Key Research and Development Guidance Project under Grant 2018104021, and in part by the Liaoning Provincial Natural Fund Guidance Plan under Grant 20180550011.