Shuffling algorithms for automatic generator question paper system.

Examination process is important activities for educational institutions to evaluate student performance. Thus the quality of the exam questions would determine the quality of the students produced by the institutions. Preparing exam questions is challenges, tedious and time consuming for the instructors. Usually the instructors keeping their own test bank in some form to help them prepare future exams. Current technologies help the instructors to store the questions in computer databases. The issue arise is how the current technologies would also help the instructors to automatically generate the different sets of questions from time to time without concern about repetition and duplication from the pass exam while the exam bank growing. This paper describes the usage of shuffling algorithm in an Automatic Generator Question paper System (GQS) as a randomization technique for organising sets of exam paper. The results indicate shuffling algorithm could be used to overcome randomization issue for GQS

Collapsibility behaviour of ABS P400 and PMMA used as sacrificial pattern in direct investment casting process

The feasibility of the Investment Casting (IC) process has been choose to be vital route in producing the metal alloy products. However, less report regarding the feasibility of portable Additive Manufacturing (AM) machines to be employed in casting process. Sacrificial wax pattern in casting process has been substitute with the AM material due to its brittleness and higher cost for hard tooling. Due to this constrain, the quality of fabricated AM materials, collapsibility analysis and strain induce was investigated. The patterns were made using ABS P400 and PMMA materials by two different types of technique which are Fused Filament Fabrication (FFF) and Polyjet technique. There were three different types of internal structures which are hollow, square and hexagon patterns. The thermal properties of the materials were studied by thermogravimetry analyzer (TGA) and linear thermal expansion. The collapsibility screening was determined to investigate the behavior of the patterns underneath the expansion. Apparently, patterns made by Polyjet technique shows better accuracy compare to FFF technique. It shows that, the PMMA error lies between -2.2 % until -0.63 % compared to ABS which is -2.4 % until 1.2% for hollow, square and hexagon patterns respectively. The data of the surface roughness were varies whereas internal structures does not play significant role in improving the surface roughness. From the strain analysis, it can be suggested that hexagon internal structure yield less stress compare to square patterns. In terms of collapsibility, hollow and hexagon patterns yield most successful warping whereas it indicates the patterns able to collapse underneath the expansion. Moreover, PMMA material tends to gain higher strain compared to ABS material whereas this can be illustrated by the graph of linear expansion. Nevertheless, to overcome the cracking of ceramic shell due to higher thermal expansion, different build layer thickness was adopted to overcome the issue

Breakdown Characteristics of Unused Transformer Oil and Olive Oil under AC and DC Voltages at Different Temperature Rate

This project aim is to investigate the breakdown characteristics of unused transformer oil and olive oil under AC and DC voltage at different temperature rate. HVAC and HVDC breakdown tests are carried out alongside with the hemisphere electrode arrangements. The high voltage test is done in order to observe the performances of the oil samples to attain the highest breakdown AC and DC voltages. In addition, this project needs to be done to see if olive oil as one of the vegetable oil can be an alternative for the conventional transformer oil. Commonly used transformer oil is made from mineral oil and it is declining day by day as its use increases. So as a precaution studies are done with vegetable oils to replace the mineral oil-based transformer insulation fluid. In this study, each oil sample is tested at different temperature rate and has recorded different value of breakdown voltage from the experiment. The gap distance between electrodes is constant and oil samples are heated at different temperature ranges. More voltage is needed to breakdown at higher temperature rate. Both the unused transformer oil and olive oil have linearly increased AC and DC breakdown voltages when subjected to higher temperatures. However, it is found that the highest AC and DC breakdown voltages are recorded at the highest temperature range and when the insulating medium used is olive oil. Moreover, the obtained AC and DC voltages are then be used to study the electric field in FEMM softwar

Electric Field Characteristics of HDPE-NR Biocomposite Under Breakdown Condition

It is critical to develop new insulating materials that can improve the performance of next generation high voltage cables for creating future electrical networks. The high electric field reduces the resistance of solid insulation and produces partial discharge through imperfections in a dielectric, causing the dielectric to age and eventually fail. Thus, this project seeks to analyse the electric field intensity of High Density Polyethylene (HDPE) in breakdown condition when added with 10g, 20g and 30g of different types of bio-filler such as coconut coir fibre, pineapple leaves fibre, and oil palm empty fruit bunch. This can be achieved by creating a two-dimensional (2D) axisymmetric electrostatic model by using the Finite Element Method Magnetics (FEMM) 4.2 software. The results showed that the unfilled HDPE biocomposites have a higher electric field intensity than 10g, 20g, and 30g biocomposite. This indicates that the maximum electric field intensity changes according to the permittivity and voltage of the bio-filler under breakdown conditions. As a result, the maximum electric field intensity was much lower for HDPE added with a 20g of the pineapple leaves fibre. Hence, pineapple leaves fibre was the best composition as it tends to improve the dielectric properties since it has a lower electric field intensity at the top electrode as compared to other compositions

Electric Field Characteristics of HDPE-NR Biocomposite Under Breakdown Condition

It is critical to develop new insulating materials that can improve the performance of next generation high voltage cables for creating future electrical networks. The high electric field reduces the resistance of solid insulation and produces partial discharge through imperfections in a dielectric, causing the dielectric to age and eventually fail. Thus, this project seeks to analyse the electric field intensity of High Density Polyethylene (HDPE) in breakdown condition when added with 10g, 20g and 30g of different types of bio-filler such as coconut coir fibre, pineapple leaves fibre, and oil palm empty fruit bunch. This can be achieved by creating a two-dimensional (2D) axisymmetric electrostatic model by using the Finite Element Method Magnetics (FEMM) 4.2 software. The results showed that the unfilled HDPE biocomposites have a higher electric field intensity than 10g, 20g, and 30g biocomposite. This indicates that the maximum electric field intensity changes according to the permittivity and voltage of the bio-filler under breakdown conditions. As a result, the maximum electric field intensity was much lower for HDPE added with a 20g of the pineapple leaves fibre. Hence, pineapple leaves fibre was the best composition as it tends to improve the dielectric properties since it has a lower electric field intensity at the top electrode as compared to other compositions

Application of Polarization and Depolarization Current in High Voltage Insulator - A Review

Deterioration of cable insulator can be caused by many factors such as aging, fault and breakdown. These factors are caused by the presence of moisture, impurities and heat due to high temperatures in an insulator. The maintenance and replacement for insulation system in power cables, power transformers and electric machine stator are generally expensive and time consuming. Thus, the performance and ability of an insulator can be monitored through polarization and depolarization current (PDC). Polarization and depolarization current (PDC) is a non-destructive test method that is used to analyse many factors that affects the behaviour of an insulator in time domain. This paper presents the review on application of polarization and depolarization current test method on different type of insulator. The factors that affects the performance of different type of insulator will be discussed in this paper. Moreover, the theory of polarization and depolarization current will be explained in order to understand the behaviour of polarization and depolarization current curve when fault occurs. Last but not least, this paper will provide a review on the different type of factors that affects the performance of different types of insulation

Adaptable service level agreement evaluation framework based on dynamic monitoring interval in cloud computing

Service level agreement (SLA) is a contract between service provider and consumer in relation to the quality of service (OoS) in cloud computing. SLA contains agreed attributes and value of service level objectives (SLO). Service provider should deliver services based on agreed quality in SLA. For example, OoS value of running service exceeds the agreed SLO, service provider ought to bear certain amount of penalty or compound fees for the SLA violation. SLA monitoring tool is unavoidable to assess the agreed SLAs at run time and detect any probable SLA violations. Both service provider and consumer need to monitor and assess OoS to ensure SLA validity. The cost value and benefit value of SLA monitoring systems is a concerned issue in cloud computing. The SLA monitoring systems need resources such as CPU, Memory, or execution storage. The amount of consumed resources by monitoring system is the cost of SLA evaluation. On the other hand, SLA monitoring systems make benefits by detecting SLA violations in a sense that service provider subsequently can adapt the infrastructure to prevent more . numbers of SLA violations and avoid penalty cost. The interval value of monitoring system has a direct impact to the cost and benefit values of monitoring system. Experiment results for CASViD and LoM2HiS frameworks have demonstrated that short measurement intervals negatively affect the overall system performance, whereas long measurement intervals cause heavy undetected SLA violations. Current monitoring systems have often constant interval value and individual monitoring process for all services and SLAs which it decreases the adaptability and cannot make a balance between cost value and benefit value of SLA monitoring systems. Subsequently, service providers face either high monitoring overhead or significant number of undetected SLA violations. This study proposes SLA monitoring framework to evaluate the agreed SLA and detect SLA violations in cloud computing. The proposed framework locates a trusted-party between service provider and service consumer to maintain and evaluate SLAs, and without any manipulation of permission from service provider, the monitoring engine validate the SLA independently for each SLA data collector thread. This study also proposed a dynamic monitoring interval (OMI) to make a balance between cost values and benefit values of SLA monitoring system in different environmental situations (ES) monitored at run time. The experimental testbed is established with one server and four virtual machines (VMs). The developed monitoring tools evaluate the predefined SLA of a selected experiment case and produced results demonstrating that the proposed OMI have the highest profit in normal ES by 23.6% growth compared to the best static interval applied to the other four VMs. Each constant interval gained a high profit in specific situation and had a low profit in other ESs whereas the proposed OMI adapted the interval value successfully and achieved the high profit in all ESs compared to the static intervals

Service level agreement checking in cloud computing in terms of verification and validation concepts

Nowadays, cloud computing is a very interesting environment for researcher to look deep. It is more on computing delivery whereby service rather than a product. It provides infrastructure as a service (IaaS), platform as a service (PaaS), software as a service (SaaS), and storage as a service. This technology is not involved end-user knowledge of the physical location and configuration of the system when users deliver the services. The cloud computing concepts is derived from grid computing, whereby end-user no need to understand the devices and components or infrastructure required in retrieved the service. It is based on virtualization resources. Cloud computing is consider as a new area in research and there have a lots of problem especially regarding to Service Level Agreement (SLA). This research is focusing to verify whether the SLA is follow the contract or violated. All the results are used for validation of the cloud services based on its own SLA

Breakdown Characteristics of Unused Transformer Oil and Olive Oil under AC and DC Voltages at Different Temperature Rate

This project aim is to investigate the breakdown characteristics of unused transformer oil and olive oil under AC and DC voltage at different temperature rate. HVAC and HVDC breakdown tests are carried out alongside with the hemisphere electrode arrangements. The high voltage test is done in order to observe the performances of the oil samples to attain the highest breakdown AC and DC voltages. In addition, this project needs to be done to see if olive oil as one of the vegetable oil can be an alternative for the conventional transformer oil. Commonly used transformer oil is made from mineral oil and it is declining day by day as its use increases. So as a precaution studies are done with vegetable oils to replace the mineral oil-based transformer insulation fluid. In this study, each oil sample is tested at different temperature rate and has recorded different value of breakdown voltage from the experiment. The gap distance between electrodes is constant and oil samples are heated at different temperature ranges. More voltage is needed to breakdown at higher temperature rate. Both the unused transformer oil and olive oil have linearly increased AC and DC breakdown voltages when subjected to higher temperatures. However, it is found that the highest AC and DC breakdown voltages are recorded at the highest temperature range and when the insulating medium used is olive oil. Moreover, the obtained AC and DC voltages are then be used to study the electric field in FEMM softwar

ADAL-NN: Anomaly Detection and Localization Using Deep Relational Learning in Distributed Systems

Modern distributed systems that operate concurrently generate interleaved logs. Identifiers (ID) are always associated with active instances or entities in order to track them in logs. Consequently, log messages with similar IDs can be categorized to aid in the localization and detection of anomalies. Current methods for achieving this are insufficient for overcoming the following obstacles: (1) Log processing is performed in a separate component apart from log mining. (2) In modern software systems, log format evolution is ongoing. It is hard to detect latent technical issues using simple monitoring techniques in a non-intrusive manner. Within the scope of this paper, we present a reliable and consistent method for the detection and localization of anomalies in interleaved unstructured logs in order to address the aforementioned drawbacks. This research examines Log Sequential Anomalies (LSA) for potential performance issues. In this study, IDs are used to group log messages, and ID relation graphs are constructed between distributed components. In addition to that, we offer a data-driven online log parser that does not require any parameters. By utilizing a novel log parser, the bundled log messages undergo a transformation process involving both semantic and temporal embedding. In order to identify instance–granularity anomalies, this study makes use of a heuristic searching technique and an attention-based Bi-LSTM model. The effectiveness, efficiency, and robustness of the paper are supported by the research that was performed on real-world datasets as well as on synthetic datasets. The neural network improves the F1 score by five percent, which is greater than other cutting-edge models