Extended Advancing Front Technique for the Initial Triangular Mesh Construction on a Single Coil for Radiative Heat Transfer

Radiative heat distribution inside an ethylene cracker furnace is often modeled using the finite volume and finite element methods. In both cases, meshes in the form of rectangles and triangles are needed to form the approximating points in the domain. In this paper, a new method called extended advancing Front technique (XAFT) is proposed for meshing the domain inside the cracker furnace, integrated with the deployment of sensors on the wall to obtain the required boundary values. XAFT is the extended version of standard advancing Front technique (SAFT) where the two normal cases in SAFT are extended with six cases of study for the element creation procedure in order to generate the initial mesh. The focus of this method is to construct triangular meshes with the requirements of having the location of sensors deployed along the wall as boundary nodes and to generate nodes at certain boundaries with linearly different lengths. It is also our objective to construct the triangular element iteratively without having to re-order the Front or delete the existing element. There are two contributions from the paper. First is the introduction of six extended cases for the element creation procedure, and second is the layer concept to generate edges with linearly different lengths. XAFT provides the framework for the heat to be approximated using the discrete ordinate method, which is a variant of the finite volume method. Simulation results produced using FLUENT support the findings for effectively approximating the radiation intensity and temperature values inside the furnace

Extended Advancing Front Technique for the Initial Triangular Mesh Construction on a Single Coil for Radiative Heat Transfer

Radiative heat distribution inside an ethylene cracker furnace is often modeled using the finite volume and finite element methods. In both cases, meshes in the form of rectangles and triangles are needed to form the approximating points in the domain. In this paper, a new method called extended advancing Front technique (XAFT) is proposed for meshing the domain inside the cracker furnace, integrated with the deployment of sensors on the wall to obtain the required boundary values. XAFT is the extended version of standard advancing Front technique (SAFT) where the two normal cases in SAFT are extended with six cases of study for the element creation procedure in order to generate the initial mesh. The focus of this method is to construct triangular meshes with the requirements of having the location of sensors deployed along the wall as boundary nodes and to generate nodes at certain boundaries with linearly different lengths. It is also our objective to construct the triangular element iteratively without having to re-order the Front or delete the existing element. There are two contributions from the paper. First is the introduction of six extended cases for the element creation procedure, and second is the layer concept to generate edges with linearly different lengths. XAFT provides the framework for the heat to be approximated using the discrete ordinate method, which is a variant of the finite volume method. Simulation results produced using FLUENT support the findings for effectively approximating the radiation intensity and temperature values inside the furnace

Modelling the Deceleration Rate in the Train Braking Profile

This paper deals with the analysis of deceleration rate in the train braking profile for one of major transportations company in the Europe The aim is to establish the relation between the declaration rate and the factor preferred by the client. Of all the factors, the most preferred factor was an average gradient experienced by each train. The method used in this paper is hard technique of Operational Research. Mathematical calculation is used to generate the average gradient experienced by each train which will be used to match which with the deceleration rate to established the relation between these two variables using regression analysis. As a conclusion, there was a relation between deceleration rate and average gradient experienced by the train and it was noticeable that driver's actual braking performance of applying deceleration rate was affected by the varying gradient more than constant gradient. As an additional work, the relation between braking distance and deceleration rate is also established. The model can be used as an initial study to determine the distance when the driver should start to brake optimally in further study

Combination of Candlestick Pattern and Stochastic to Detect Trend Reversal in Forex Market

A variety of ways traders do to determine the decision to buy/sell on the forex market. It bases one that is popular on candle patterns. Some strategies that use candle patterns include: pin bar, engulfing, and inside the bar. But the strategy used is still limited to determining buying/selling decisions. This research will use a combination of candle pattern strategies and stochastic moving average to determine the level of risk that exists in each buy/sell decision on the forex market. By using this combination, the results are good in Eur/USD pairs

A Review Of Training Data Selection In Software Defect Prediction

The publicly available dataset poses a challenge in selecting the suitable data to train a defect prediction model to predict defect on other projects. Using a cross-project training dataset without a careful selection will degrade the defect prediction performance. Consequently, training data selection is an essential step to develop a defect prediction model. This paper aims to synthesize the state-of-the-art for training data selection methods published from 2009 to 2019. The existing approaches addressing the training data selection issue fall into three groups, which are nearest neighbour, cluster-based, and evolutionary method. According to the results in the literature, the cluster-based method tends to outperform the nearest neighbour method. On the other hand, the research on evolutionary techniques gives promising results but is still scarce. Therefore, the review concludes that there is still some open area for further investigation in training data selection. We also present research direction within this are

Systematic literature review on enhancing recommendation system by eliminating data sparsity

The aim of this project is to develop an approach using machine learning and matrix factorization to improve recommendation system. Nowadays, recommendation system has become an important part of our lives. It has helped us to make our decision-making process easier and faster as it could recommend us products that are similar with our taste. These systems can be seen everywhere such as online shopping or browsing through film catalogues. Unfortunately, the system still has its weakness where it faced difficulty in recommending products if there are insufficient reviews left by the users on products. It is difficult for the system to recommend said products because it is difficult to pinpoint what kind of users would be interested in the products. Research studies have used matrix factorization as the standard to solve this issue but lately, machine learning has come up as a good alternative to solve data sparsity. This project compares results of the recommendation system using RMSE to see how each proposed methods performs using three different datasets from MovieLens. We have selected two models – matrix factorization with SVD and deep learning-based model to evaluate these approaches and understand why they are popular solution to data sparsity. We have found that SVD brought in a lower RMSE as compared to deep learning. The reason behind this was discussed in the latter chapter of this thesis. We have also found possible research in capitalising categorical variables in recommendation system and the experiment achieved a lower RMSE score as compared to SVD and deep learning, showing the many possibilities of the future directions of the research in recommendation system

Modeling Nurse Time For School Health Service Using System Dynamics

One of the national primary health care services in Malaysia is school health care. This care is very crucial as it ensures that, countrywide, the health of students from the age of five to fifteen is in a good condition. In Malaysia, nurses hold a major responsibility for delivering the school health service. However, there is no solid research investigating the nursing time required to deliver school health services. This paper presents a system dynamics model representing the specific school health services delivered by nurses. System Dynamics is a computer-aided approach to policy analysis and design. In this paper, the system dynamics model are represented by several causal loop diagrams which covers all the school health activities and is able to determine the projected total nurse time required in delivering the service. The baseline simulation result of the nurse time required for delivering school health services is about 1080000 hours in year 2030, which is equivalent to 680 full time equivalent (FTE) nurses. Furthermore, various what-if analyses are tested with the model, as it is important for policy makers to investigate various scenarios for an effective decision-making process. In other words, the theme of the study is to understand the implication of the changes in school population size and the modification of certain activities in the school health program on the nurse time spent delivering school health service by developing a dedicated forecasting system dynamics model for school health. The time horizon for the forecasting is from 2018 until 2030

Stop hunt detection using indicators and expert advisors in the forex market

Foreign exchange trading activities are one of the businesses that can generate big profits, and provide freedom for business people without the need to provide a large capital. Traders often suffer losses due to uncertainty in the market. One of them is market manipulation carried out by brokers or banks. For this reason, this research was conducted to detect any manipulation that occurred in the foreign exchange market. This research tries to combine trading systems, indicators and expert advisors that aim to help traders detect fake market price movements to minimize losses that occur due to errors in making transaction decisions. The results of the study produce an indicator that is able to detect the potential of certain patterns used by the market maker to reverse the direction of market prices and is supported by the presence of expert advisors who are able to pinpoint potential market manipulation, so traders can avoid large losses

A brief review of surface meshing in medical images for biomedical computing and visualization

A visual representation of the interior of a body is important for clinical analysis and medical intervention. The technique, process and art of creating this visual representation are called medical imaging. The images produced from medical imaging need to be analyses by using Finite Element Method (FEM) especially for intraoperative registration and biomechanical modeling of the tissues. This medical model ranges from the smallest vascular to bones and the complex brain. In order to use FEM, the images need to go through surface meshing generator. Although numerous mesh generation methods have been described to date, there is a few which can deal with medical data input. In this paper, a briefing review of surface meshing that can deal in medical images is presented especially in biomedical computing and visualization. Some automatic mesh generators software used in medical imaging is also discussed such as ScanIP, MIMICS, TETGEN, NetGen, BioMesh3D,CUBITMesh and Gmsh

Implementing Bisection Method on Forex Trading Database for Early Diagnosis of Inflection Point

Many people are trading in the forex market during the COVID-19 pandemic with the hope of earning money, but they are experiencing shortages due to the lack of information and technology-based tools for existing daily data. Sometimes traders only use moving averages in trading data, even though this information needs to be processed again to get the right inflection point. The objective of this research is to find inflection points based on Forex trading database. Another algorithm can also be used to determine the inflection point between two points on a moving average. This can be supported by the Bisection method used because it can guarantee that convergence will occur. The results show that the points resulting from the bisection calculation on the moving average provide a fairly accurate decision support for the location where the inflection point is located. From 10,000 data there is a standard deviation of 0.71 points which is very small compared to an average of 20 pips (points used as the difference in price values in forex). The use of the bisection method provides an accuracy of the results in seeing the inflection point of 87