A review on chemical diagnosis techniques for transformer paper insulation degradation
- Authors: Abu Bakar, Norazhar , Abu Siada, Ahmed , Islam, Syed
- Date: 2013
- Type: Text , Conference proceedings , Conference paper
- Relation: 2013 Australasian Universities Power Engineering Conference, AUPEC 2013; Hobart, Australia; 29th September-3rd October 2013 p. 1-6
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- Description: Energized parts within power transformer are isolated using paper insulation and are immersed in insulating oil. Hence, transformer oil and paper insulation are essential sources to detect incipient and fast developing power transformer faults. Several chemical diagnoses techniques are developed to examine the condition of paper insulation such as degree of polymerization, carbon oxides, furanic compounds and methanol. The principle and limitation of these diagnoses are discussed and compared in this paper.
Image processing-based on-line technique to detect power transformer winding faults
- Authors: Abu-Siada, Ahmed , Islam, Syed
- Date: 2013
- Type: Text , Conference proceedings , Conference paper
- Relation: 39th Annual Conference of the IEEE Industrial Electronics Society, IECON 2013; Vienna, Austria; 10th-14th November 2013 p. 1-6
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- Description: Frequency Response Analysis (FRA) has been growing in popularity in recent times as a tool to detect mechanical deformation within power transformers. To conduct the test, the transformer has to be taken out of service which may cause interruption to the electricity grid. Moreover, because FRA relies on graphical analysis, it calls for an expert person to analyse the results as so far, there is no standard code for FRA interpretation worldwide. In this paper an online technique is introduced to detect the internal faults within a power transformer by constructing the voltage-current (V-I) locus diagram to provide a current state of the transformer health condition. The technique does not call for any special equipment as it uses the existing metering devices attached to any power transformer to monitor the input voltage, output voltage and the input current at the power frequency and hence online monitoring can be realised. Various types of faults have been simulated to assess its impact on the proposed locus. A Matlab code based on digital image processing is developed to calculate any deviation of the V-I locus with respect to the reference one and to identify the type of fault.
Investigation of microgrid instability caused by time delay
- Authors: Aghanoori, Navid , Masoum, Mohammad , Islam, Syed , Nethery, Steven
- Date: 2017
- Type: Text , Conference proceedings , Conference paper
- Relation: 10th International Conference on Electrical and Electronics Engineering, ELECO 2017; Bursa, Turkey; 29th-2nd December 2017 Vol. 2018, p. 105-110
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- Description: This paper investigates the impact of time delay in the control of a grid-connected microgrid with renewable energy resources. The considered microgrid has a critical load that needs to be powered and protected in the event of grid voltage disturbance while the microgrid maintains connection to the grid. Three case studies are performed considering three different time delays to indicate the advantages of fast communication system in the performance of renewable microgrids. Detailed simulation results illustrate that the proposed communication system using IEC 61850 substation automation standard provides better voltage and current quality to the critical local load with larger phase and gain margins while keeping the microgid connected to main grid.
Dynamic derivative-droop control for supercapacitor synthetic inertial support
- Authors: Akram, Umer , Mithulananthan, N. , Shah, Rakibuzzaman , Islam, Rabiul
- Date: 2020
- Type: Text , Conference proceedings , Conference paper
- Relation: 2020 IEEE Industry Applications Society Annual Meeting, IAS 2020 Vol. 2020-January
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- Description: Energy storage is recognized as a potential solution to alleviate the impacts of inertia reduction and intermittency due to the integration of inverter based renewable energy sources (RES) in power systems. Out of various rapid responsive energy storage technologies, supercapacitor energy storage (SCES) is the most promising technology for synthetic inertia support. Because the SCES has high power density, very small response time, and large cycle life. In this paper, a dynamic derivative-droop control strategy is developed for SCES to provide the synthetic inertia in low inertia power system. The proposed strategy overcomes the limitations of separately applied derivative and droop controls. In addition, the use of time varying gains (referred as dynamic) instead of fixed gains improves the performance compared to derivative-droop coordinated control. Different types of events are created at different penetration levels of RES to test the robustness of the proposed control. A comparison, based on RoCoF and frequency nadir, between the derivative, droop, derivative-droop coordinated and the proposed controls is presented to show the effectiveness of the proposed control approach. © 2020 IEEE.
Assessing transformer oil quality using deep convolutional networks
- Authors: Alam, Mohammad , Karmakar, Gour , Islam, Syed , Kamruzzaman, Joarder , Chetty, Madhu , Lim, Suryani , Appuhamillage, Gayan , Chattopadhyay, Gopi , Wilcox, Steve , Verheyen, Vincent
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 29th Australasian Universities Power Engineering Conference, AUPEC 2019
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- Description: Electrical power grids comprise a significantly large number of transformers that interconnect power generation, transmission and distribution. These transformers having different MVA ratings are critical assets that require proper maintenance to provide long and uninterrupted electrical service. The mineral oil, an essential component of any transformer, not only provides cooling but also acts as an insulating medium within the transformer. The quality and the key dissolved properties of insulating mineral oil for the transformer are critical with its proper and reliable operation. However, traditional chemical diagnostic methods are expensive and time-consuming. A transformer oil image analysis approach, based on the entropy value of oil, which is inexpensive, effective and quick. However, the inability of entropy to estimate the vital transformer oil properties such as equivalent age, Neutralization Number (NN), dissipation factor (tanδ) and power factor (PF); and many intuitively derived constants usage limit its estimation accuracy. To address this issue, in this paper, we introduce an innovative transformer oil analysis using two deep convolutional learning techniques such as Convolutional Neural Network (ConvNet) and Residual Neural Network (ResNet). These two deep neural networks are chosen for this project as they have superior performance in computer vision. After estimating the equivalent aging year of transformer oil from its image by our proposed method, NN, tanδ and PF are computed using that estimated age. Our deep learning based techniques can accurately predict the transformer oil equivalent age, leading to calculate NN, tanδ and PF more accurately. The root means square error of estimated equivalent age produced by entropy, ConvNet and ResNet based methods are 0.718, 0.122 and 0.065, respectively. ConvNet and ResNet based methods have reduced the error of the oil age estimation by 83% and 91%, respectively compared to that of the entropy method. Our proposed oil image analysis can calculate the equivalent age that is very close to the actual age for all images used in the experiment. © 2019 IEEE.
- Description: E1
Maximising competitive advantage on e-Business websites : A data mining approach
- Authors: Alazab, Ammar , Bevinakoppa, Savitri , Khraisat, Ansam
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018; Langkawi, Malaysia; 21st-22nd November 2018 p. 111-116
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- Description: Many organizations are interested in analyzing and evaluating the web data for their websites because websites are a very important platform to carry out their business. However, website evaluations face many challenges in using analytics, especially with the huge amount of data that the websites are collecting from various sources. This explosive growth in data requires a complex tool for analyzing and automatically convert the data into valuable information. However, without using a proper analysis tool, it is very difficult to understand the user's behaviour, user's interaction patterns on the website and how users involve in the site. This paper explains methods to examine, understand and visualize the huge amounts of stored data collected from the websites. In this paper, a framework is developed for identifying user's behaviours on websites. Firstly, the attributes are extracted from different websites using Google Analytics and other API tools. Secondly, data mining techniques such as clustering, classification and information gain are applied to build this framework. The findings of these study can be used to evaluate the website and provide some guidelines for the web team to increase user engagement on the website and understand the influence of user behaviour. In addition, this framework is able to identify which behaviour features influence user decisions. Our proposed framework for identifying user's behaviours on websites is tested on a large dataset that contains a variety of individual users from different websites. © 2018 IEEE.
Impact of insulating oil degradation on the power transformer frequency response analysis
- Authors: Aljohani, Omar , Abu-Siada, Ahmed , Islam, Syed
- Date: 2015
- Type: Text , Conference proceedings , Conference paper
- Relation: 11th IEEE International Conference on the Properties and Applications of Dielectric Materials, ICPADM 2015; Sydney, Australia; 19th-22nd July 2015 Vol. OCT, p. 396-399
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- Description: Frequency Response Analysis (FRA) has become a reliable tool to detect mechanical deformation within power transformers. Many researchers have investigated the impact of various mechanical winding and core deformations on transformer FRA signature using either simulation analysis or practical testing to establish a standard code for FRA signature interpretation. None of them however, have given attention to the impact of power transformer insulating oil degradation on the transformer FRA signature. This paper investigates the effect of insulating mineral oil degradation on power transformer FRA signature. In this regard, the physical geometrical dimension of a single-phase transformer filled with insulating mineral oil is simulated using three dimensional finite element analysis to emulate the real transformer operation. Transformer FRA signature is measured and analysed with various health conditions of the insulating oil. Results show that, insulating oil degradation has a significant impact on the transformer FRA signature.
Application of digital image processing to diagnose transformer winding deformation using FRA polar plot
- Authors: Aljohani, Omar , Abu-Siada, Ahmed , Islam, Syed
- Date: 2014
- Type: Text , Conference proceedings , Conference paper
- Relation: 2014 International Conference on Condition Monitoring and Diagnosis, CMD 2014; Jeju, Korea; 21st September 2014
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- Description: Digital image processing (DIP) technique has been growing rapidly as an essential tool to interpret various image features for many applications of science and engineering. Condition monitoring and diagnosis are considered the main areas that relay on DIP. Frequency response analysis (FRA) technique has become a popular and reliable diagnostic tool in detecting various winding deformations within power transformers. However, interpretation of FRA signatures still requires high expertise because of its reliance on graphical analysis. This paper presents a new technique for the interpretation of transformers FRA signatures. The proposed technique relies on incorporating both magnitude and angle of the FRA signature in one polar plot, which is manipulated to extract some unique features using DIP techniques. The proposed technique can assess in identifying and quantifying various winding deformation within power transformers. The proposed technique is easy to implement in any frequency response analyser.
Atrial fibrillation analysis for real time patient monitoring
- Authors: Allami, Ragheed , Stranieri, Andrew , Marzbanrad, Faezeh , Balasubramanian, Venki , Jelinek, Herbert
- Date: 2017
- Type: Text , Conference proceedings , Conference paper
- Relation: 44th Computing in Cardiology Conference, CinC 2017 Vol. 44, p. 1-4
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- Description: Atrial Fibrillation (AF) can lead to life-threatening conditions such as stroke and heart failure. The instant recognition of life-threatening cardiac arrhythmias based on a 3-lead ECG to record a Lead II configuration for a few seconds is a challenging problem of clinical significance. Five consecutive ECG beats that were identified by a cardiologist to characterise an AF episode and five consecutive heartbeat intervals representing an irregular RR intervals episode were analysed. The detection and analysis of P waves as the morphological features of AF was executed based on two template matching methods. An AF detector was developed by combining the correlation coefficients based on the template matching methods and the standard deviation of the RR intervals. The AF detector was then applied to classify 5 consecutive beats as AF or non-AF based on thresholding the calculated irregularity. The proposed algorithm was tested on the MIT-BIH Atrial Fibrillation and the Challenge 2017 databases. The proposed method resulted in an improved sensitivity, specificity and accuracy of 97.60%, 98.20% and 99% respectively compared to recent published methods. In addition, the proposed method is suitable for real-time patient monitoring as it is computationally simple and requires only a few seconds of ECG recording to detect an AF rhythm. © 2017 IEEE Computer Society. All rights reserved.
Impact of buckling deformation on the FRA signature of power transformer
- Authors: Amini, Arman , Das, Narottam , Islam, Syed
- Date: 2013
- Type: Text , Conference proceedings , Conference paper
- Relation: 2013 Australasian Universities Power Engineering Conference, AUPEC 2013; Hobart, Australia; 29th September-3rd October 2013 p. 1-4
- Full Text: false
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- Description: Power transformer is an important asset within the electrical power system network. Frequency Response Analysis (FRA) has been developing in popularity in recent years as a tool to detect mechanical deformation of power transformers winding. Since the FRA has been relying on graphical analysis, it calls for an expert person to analyze the results to detect the type of the fault and its location. There are no reliable guidelines for FRA signature in the event that a failure occurs in service and the impact can be far reaching. The concept of FRA has been successfully used as a diagnostic technique to detect the winding deformation, core and clamping structure for the power transformers. This paper investigated the impact of the forced and free winding buckling based on the variation of the electrical parameters to show how these faults vary the FRA signature.
A triangulation-based technique for building boundary identification from point cloud data
- Authors: Awrangjeb, Mohammad , Lu, Guojun
- Date: 2016
- Type: Text , Conference proceedings , Conference paper
- Relation: 2015 International Conference on Image and Vision Computing New Zealand, IVCNZ 2015; Auckland, New Zealand; 23rd-24th November 2015 Vol. 2016-November, p. 1-6
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- Description: Building boundary identification is an essential prerequisite in building outline generation from point cloud data. In this problem, boundary edges that constitute the building boundary are identified. The existing solutions to the identification of boundary edges from the input point set have one or more of the following problems: ineffective in finding appropriate edges in a concave shape, incapable of determining a 'hole' or 'concavity' inside the shape separately, dependant on additional information such as the scan direction that may be unavailable, and incompetent in determining the boundary of a point set from the boundaries of two or more subsets of the point set. This paper proposes a new solution to the identification of building boundary by using the maximum point-to-point distance in the input data. It properly detects the boundary edges for any type of shape and separately recognises holes, if any, inside the shape. The unique feature of the proposed solution is that it can identify the boundary of a point set from the boundaries of two or more subsets of the point set. It does not require any additional information other than the input point set. Experimental results show that the proposed solution can preserve details along the building boundary and offer high area-based completeness and quality, even in low density input data. © 2015 IEEE.
- Description: International Conference Image and Vision Computing New Zealand
Optimal scheduling with dynamic line ratings and intermittent wind power
- Authors: Banerjee, Binayak , Jayaweera, Dilan , Islam, Syed
- Date: 2014
- Type: Text , Conference proceedings , Conference paper
- Relation: 2014 IEEE Power and Energy Society General Meeting; National Harbor, United States; 27th-31st July 2014 Vol. 2014, p. 1-5
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- Description: Limited transmission capacity may lead to wind curtailment during periods of high availability of wind. This paper presents an improved methodology to quantify the latent scheduling capacity of a power system taking into account stochastic variation in line-thermal rating, intermittency of wind, and mitigating the risk of network congestion associated with high penetration of wind. The approach is aimed at strategic planning of power systems in the context of power systems with short to medium length lines with a priori known unit commitment decisions and uses stochastic optimization with a two stage recourse action. Results suggest that a considerable level of wind penetration is possible with dynamic line ratings, without adversely affecting the risk of network congestion.
- Description: IEEE Power and Energy Society General Meeting
Alleviating post-contingency congestion risk of wind integrated systems with dynamic line ratings
- Authors: Banerjee, Binayak , Jayaweera, Dilan , Islam, Syed
- Date: 2014
- Type: Text , Conference proceedings , Conference paper
- Relation: 24th Australasian Universities Power Engineering Conference, AUPEC 2014; Perth, Australia; 28th September-1st October 2014 p. 1-6
- Full Text: false
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- Description: One of the factors hindering the large scale integration of wind power is the post contingency congestion of a network due to limited availability of network capacity and auxiliary constraints. Under such conditions, the network operators can potentially request a curtailment of wind farm output if the remedial strategies fail. The paper investigates this problem in detail and proposes a mathematical framework to capture the post contingency spare capacity of network assets that is required to limit the wind curtailment. The proposed approach incorporates stochastic variation in asset thermal rating; models network congestion, and quantifies the risk of congestion using an extended version of conic-quadratic programming based optimization. The uniqueness of the proposed mathematical model is that it converts conventional thermal constraints to dynamic constraints by using a discretized stochastic penalty function with quadratic approximation of constraint relaxation penalty. The results suggest that the wind utilization can be maximized if the networks are operated 30-50% less than the nominal rating of the assets.
Analysis of Classifiers for Prediction of Type II Diabetes Mellitus
- Authors: Barhate, Rahul , Kulkarni, Pradnya
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 4th International Conference on Computing, Communication Control and Automation, ICCUBEA 2018
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- Description: Diabetes mellitus is a chronic disease and a health challenge worldwide. According to the International Diabetes Federation, 451 million people across the globe have diabetes, with this number anticipated to rise up to 693 million people by 2045. It has been shown that 80% of the complications arising from type II diabetes can be prevented or delayed by early identification of the people who are at risk. Diabetes is difficult to diagnose in the early stages as its symptoms grow subtly and gradually. In a majority of the cases, the patients remain undiagnosed until they are admitted for a heart attack or begin to lose their sight. This paper analyzes the different classification algorithms based on a patient's health history to aid doctors identify the presence of as well as promote early diagnosis and treatment. The experiments were conducted on Pima Indian Diabetes data set. Various classifiers used include K Nearest Neighbors, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, Support Vector Machine and Neural Network. Results demonstrate that Random Forests performed well on the data set giving an accuracy of 79.7%. © 2018 IEEE.
- Description: E1
Apportioning allocations to users of multi-storage water supply systems : A case study of making a complex volume shared system more transparent
- Authors: Barton, Andrew , Wilson, Kym
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 2018 Hydrology and Water Resources Symposium: Water and Communities, HWRS 2018; Melbourne, Australia; 3rd-6th December 2018 p. 60-71
- Full Text: false
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- Description: This paper describes principles for the apportionment of water allocations to users of a multi-reservoir water supply system utilising a volume shared entitlement and allocation framework. The challenge of this problem is that volume shared systems determine the available water for allocation based on a total system approach. The subsequent operational challenge is to then apportion this total volume of available water to specific reservoirs to meet individual user requirements. This is an important problem as entitlement and allocation frameworks usually have the water resource assessment process and high-level water sharing principles enshrined in a set of legally binding orders and instruments. However, some systems still have a subsequent apportionment of allocation problem, not codified in any binding document, where decisions need to be made around how much allocation should be made available from particular reservoirs for the various stakeholders or user groups. In shared systems where contests over water is common, or access to allocation may vary over time, it is desirable that the agency responsible for making the resource decisions uses an objective, fair and equitable method of allocating water. To work through this problem and present the set of principles for apportionment, the Wimmera-Glenelg System located in western Victoria, Australia, is used as a case study. The Wimmera-Glenelg System is a complex water resource system with multiple reservoirs and many different user groups and stakeholders. The region is also subject to a highly variable climate with frequent dry periods and water rationing, creating periods of time where the equitable apportionment of allocation becomes incredibly important. Concepts of capacity sharing have been used to help with the development of the apportionment principles to help maximise the transparency in decision making to stakeholders and because the system does have an emerging water market where commercial and economic certainty is becoming paramount. However, capacity sharing for systems with multiple reservoirs is not common, and so even this has limitations in use. The principles described can be universally applied to reservoir systems of varying complexity, where there are multiple users, and is compatible with both capacity shared systems and newer continuous sharing or continuous accounting systems. Results are shown for the Wimmera-Glenelg System. © CURRAN-CONFERENCE. All rights reserved.
Power transaction management amongst coupled microgrids in remote areas
- Authors: Batool, Munira , Islam, Syed , Shahnia, Farhad
- Date: 2017
- Type: Text , Conference proceedings , Conference paper
- Relation: 7th IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2017;Auckland, New Zealand; 4th-7th December 2017 p. 1-6
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- Description: Large remote areas normally have isolated and self-sufficient electricity supply systems, often referred to as microgrids. These systems also rely on a mix of dispatchable and non-dispatcha- ble distributed energy resources to reduce the overall cost of electricity production. Emergencies such as shortfalls, overloading, and faults can cause problems in the operation of these remote area microgrids. This paper presents a power transaction management scheme amongst a few such microgrids when they are coupled provisionally during emergencies. By definition, power transaction is an instance of buying and selling of electricity amongst problem and healthy microgrids. The developed technique aims to define the suitable power generation from all dispatchable sources and regulate the power transaction amongst the coupled microgrids. To this end, an optimization problem is formulated that aims to define the above parameters while minimizing the costs and technical impacts. A mixed- integer linear programming technique is used to solve the formulated problem. The performance of the proposed management strategy is evaluated by numerical analysis in MATLAB.
Master control unit based power exchange strategy for interconnected microgrids
- Authors: Batool, Munira , Islam, Syed , Shahnia, Farhad
- Date: 2017
- Type: Text , Conference proceedings , Conference paper
- Relation: 2017 Australasian Universities Power Engineering Conference, AUPEC 2017; Melbourne, Australia; 19th-22nd November 2017 Vol. 2017, p. 1-6
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- Description: Large remote area networks normally have self-suffi-cient electricity systems. These systems also rely on non-dispatchable DGs (N-DGs) for overall reduction in cost of electricity production. It is a fact that uncertainties included in the nature of N-DGs as well as load demand can cause cost burden on islanded microgrids (MGs). This paper proposes development of power exchange strategy for an interconnected MGs (IMG) system as part of large remote area network with optimized controls of dispatchable (D-DGs) which are members of master control unit (MCU). MCU analysis includes equal cost increment principle to give idea about the amount of power exchange which could take place with neighbor MGs in case of overloading situation. Sudden changes in N-DGs and load are defined as interruptions and are part of analysis too. Optimization problem is formulated on the basis of MCU adjustment for overloading or under loading situation and suitability of support MG (S-MG) in IMG system for power exchange along with key features of low cost and minimum technical impacts. Mixed integer linear programming (MILP) technique is applied to solve the formulated problem. The impact of proposed strategy is assessed by numerical analysis in MATLAB programming under stochastic environment.
Stochastic modeling of the output power of photovoltaic generators in various weather conditions
- Authors: Batool, Munira , Islam, Syed , Shahnia, Farhad
- Date: 2016
- Type: Text , Conference proceedings , Conference paper
- Relation: 2016 Australasian Universities Power Engineering Conference, AUPEC 2016; Brisbane, Australia; 25th-28th September 2016 p. 1-5
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- Description: The intermittency of solar-powered energy sources prompt the uncertainty of load management. The influence of shading (whatever the reason may be) directly diminishes the feasible output power of the photovoltaic (PV) generators. The major causes of shading are the weather condition changes like the clouds, storms, and rains. Thereby, the dispatchable power for a distinct weather condition at an explicit time frame needs to be quantified. The stochastic modeling of a practical PV system has been performed in this paper. A step-by-step MATLAB-based algorithm is developed for tracking of dispatchable power limit using the Monte Carlo Principle. The proposed algorithm describes the weather condition as a function of cloud presence. The prescribed characteristics consist of the solar irradiance and the ambient temperature. The impact of weather changes on the output power of a PV system is evaluated by this algorithm. The results of this research are concluded by realistic data analysis taken from the Australian bureau of meteorology.
Towards smart online dispute resolution for medical disputes
- Authors: Bellucci, Emilia , Stranieri, Andrew , Venkatraman, Sitalakshmi
- Date: 2020
- Type: Text , Conference proceedings , Conference paper
- Relation: Proceedings of the Australasian Computer Science Week Multiconference (ACSW 2020); Melbourne, Australia; 3rd-7th February 2020. p. 1-5
- Full Text: false
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- Description: With the advancements in technologies, digitization of health records in the healthcare industry is undertaking a rapid revolution. This is further fueled with the entrance of Internet of Things (IoT), where mobile health devices have resulted in an explosion of health data and increased accessibility via wireless communications and sensor networks. With the introduction of an Electronic Health Record (EHR) system as an important venture for the general health and wellbeing of a country's citizens, privacy issues and medical disputes are expected to rise. In addition to critical health information being documented and shared electronically, integrating data from diverse smart medical IoT devices are leading towards increasingly more complex disputes that require immense time and effort to resolve. Online dispute resolution (ODR) programs have been successfully applied to cost-effectively help disputants resolve commercial, insurance and other legal disputes, but as yet have not been applied to healthcare. This paper takes a modest step in this direction, firstly to identify the drivers of medical disputes that include patient empowerment and technology advancements and trends. Secondly, we explore dispute resolution models and identify the status and limitations of current ODR systems.
- Description: This work was funded by the University of Ballarat Deakin University Collaborative Fund. 160134
Novel tire inflating system using extreme learning machine algorithm for efficient tire identification
- Authors: Choudhury, Tanveer , Kahandawa, Gayan , Ibrahim, Yousef , Dzitac, Pavel , Mazid, Abdul Md , Man, Zhihong
- Date: 2017
- Type: Text , Conference proceedings , Conference paper
- Relation: 2017 IEEE International Conference on Mechatronics, ICM 2017; Gippsland, Victoria; 13th-15th February 2017 p. 404-409
- Full Text: false
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- Description: Tire inflators are widely used all around the word and the efficient and accurate operation is essential. The main difficulty in improving the inflation cycle of a tire inflator is the identification of the tire connected for inflation. A robust single hidden layer feed forward neural network (SLFN) is, thus, used in this study to model and predict the correct tire size. The tire size is directly related to the tire inflation cycle. Once the tire size is identified, the inflation process can be optimized to improve performance, speed and accuracy of the inflation system. Properly inflated tire and tire condition is critical to vehicle safety, stability and controllability. The training times of traditional back propagation algorithms, mostly used to model such tire identification processes, are far slower than desired for implementation of an on-line control system. Use of slow gradient based learning methods and iterative tuning of all network parameters during the learning process are the two major causes for such slower learning speed. An extreme learning machine (ELM) algorithm, which randomly selects the input weights and biases and analytically determines the output weights, is used in this work to train the SLFNs. It is found that networks trained with ELM have relatively good generalization performance, much shorter training times and stable performance with regard to the changes in number of hidden layer neurons. The result represents robustness of the trained networks and enhance reliability of the mode. Together with short training time, the algorithm has valuable application in tire identification process. © 2017 IEEE.
- Description: Proceedings - 2017 IEEE International Conference on Mechatronics, ICM 2017