Search results for: heuristic trend prediction.

Authors: Melodi Mbuyi Mata, Blessing Olamide Taiwo, Afolabi Ayodele David

Abstract:

The purpose of this research is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo state aggregate quarries. In addition, an Artificial Neural Network (ANN) model and multivariate predicting models for granite profitability were developed in the study. A formal survey questionnaire was used to collect data for the study. The data extracted from the case study mine for this study include granite marketing operations, royalty, production costs, and mine production information. The following methods were used to achieve the goal of this study: descriptive statistics, MATLAB 2017, and SPSS16.0 software in analyzing and modeling the data collected from granite traders in the study areas. The ANN and Multi Variant Regression models' prediction accuracy was compared using a coefficient of determination (R2), Root Mean Square Error (RMSE), and mean square error (MSE). Due to the high prediction error, the model evaluation indices revealed that the ANN model was suitable for predicting generated profit in a typical quarry. More quarries in Nigeria's southwest region and other geopolitical zones should be considered to improve ANN prediction accuracy.

Keywords: National development, granite, profitability assessment, ANN models.

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Authors: Abdullah Al Faruque, Ram Balachandar

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Series of laboratory tests were carried out to study the extent of scour caused by a three-dimensional wall jets exiting from a square cross-section nozzle and into a non-cohesive sand beds. Previous observations have indicated that the effect of the tail water depth was significant for densimetric Froude number greater than ten. However, the present results indicate that the cut off value could be lower depending on the value of grain size-to-nozzle width ratio. Numbers of equations are drawn out for a better scaling of numerous scour parameters. Also suggested the empirical prediction of scour to predict the scour centre line profile and plan view of scour profile at any particular time.

Keywords: Densimetric Froude Number, Jets, Nozzle, Sand, Scour, Tailwater, Time.

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Authors: Nieves M. C. Talavera-Prieto, Abel G. M. Ferreira, António T. G. Portugal, Rui J. Moreira, Jaime B. Santos

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The knowledge of biodiesel density over large ranges of temperature and pressure is important for predicting the behavior of fuel injection and combustion systems in diesel engines, and for the optimization of such systems. In this study, cottonseed oil was transesterified into biodiesel and its density was measured at temperatures between 288 K and 358 K and pressures between 0.1 MPa and 30 MPa, with expanded uncertainty estimated as ±1.6 kg⋅m- 3. Experimental pressure-volume-temperature (pVT) cottonseed data was used along with literature data relative to other 18 biodiesels, in order to build a database used to test the correlation of density with temperarure and pressure using the Goharshadi–Morsali–Abbaspour equation of state (GMA EoS). To our knowledge, this is the first that density measurements are presented for cottonseed biodiesel under such high pressures, and the GMA EoS used to model biodiesel density. The new tested EoS allowed correlations within 0.2 kg·m-3 corresponding to average relative deviations within 0.02%. The built database was used to develop and test a new full predictive model derived from the observed linear relation between density and degree of unsaturation (DU), which depended from biodiesel FAMEs profile. The average density deviation of this method was only about 3 kg.m-3 within the temperature and pressure limits of application. These results represent appreciable improvements in the context of density prediction at high pressure when compared with other equations of state.

Keywords: Biodiesel, Correlation, Density, Equation of state, Prediction.

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Authors: C. Nallusamy, A. Sabari, K. Suganya

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Geographical routing protocol requires node physical location information to make forwarding decision. Geographical routing uses location service or position service to obtain the position of a node. The geographical information is a geographic coordinates or can be obtained through reference points on some fixed coordinate system. Link can be formed between two nodes. Link lifetime plays a crucial role in MANET. Link lifetime represent how long the link is stable without any failure between the nodes. Link failure may occur due to mobility and because of link failure energy of nodes can be drained. Thus this paper proposes survey about link lifetime prediction using geographical information.

Keywords: MANET, Geographical routing, Link lifetime, Link stability.

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Authors: Liang Zhao, Jili Zhang, Chongquan Zhong

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Energy consumption data, in particular those involving public buildings, are impacted by many factors: the building structure, climate/environmental parameters, construction, system operating condition, and user behavior patterns. Traditional methods for data analysis are insufficient. This paper delves into the data mining technology to determine its application in the analysis of building energy consumption data including energy consumption prediction, fault diagnosis, and optimal operation. Recent literature are reviewed and summarized, the problems faced by data mining technology in the area of energy consumption data analysis are enumerated, and research points for future studies are given.

Keywords: Data mining, data analysis, prediction, optimization, building operational performance.

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Authors: Omar M. Elmabrouk., Roaa Y. Taha., Najat M. Ebrahim, Sabbreen A. Mohammed

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Power transformers are the most crucial part of power electrical system, distribution and transmission grid. This part is maintained using predictive or condition-based maintenance approach. The diagnosis of power transformer condition is performed based on Dissolved Gas Analysis (DGA). There are five main methods utilized for analyzing these gases. These methods are International Electrotechnical Commission (IEC) gas ratio, Key Gas, Roger gas ratio, Doernenburg, and Duval Triangle. Moreover, due to the importance of the transformers, there is a need for an accurate technique to diagnose and hence predict the transformer condition. The main objective of this technique is to avoid the transformer faults and hence to maintain the power electrical system, distribution and transmission grid. In this paper, the DGA was utilized based on the data collected from the transformer records available in the General Electricity Company of Libya (GECOL) which is located in Benghazi-Libya. The Fuzzy Logic (FL) technique was implemented as a diagnostic approach based on IEC gas ratio method. The FL technique gave better results and approved to be used as an accurate prediction technique for power transformer faults. Also, this technique is approved to be a quite interesting for the readers and the concern researchers in the area of FL mathematics and power transformer.

Keywords: Fuzzy logic, dissolved gas-in-oil analysis, DGA, prediction, power transformer.

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Authors: Ikmal Hisyam Mohamad Paris, Lilly Suriani Affendey, Norwati Mustapha

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In this paper we compare the accuracy of data mining methods to classifying students in order to predicting student-s class grade. These predictions are more useful for identifying weak students and assisting management to take remedial measures at early stages to produce excellent graduate that will graduate at least with second class upper. Firstly we examine single classifiers accuracy on our data set and choose the best one and then ensembles it with a weak classifier to produce simple voting method. We present results show that combining different classifiers outperformed other single classifiers for predicting student performance.

Keywords: Classification, Data Mining, Prediction, Combination of Multiple Classifiers.

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Authors: Mohammad Asif

Abstract:

An accurate prediction of the minimum fluidization velocity is a crucial hydrodynamic aspect of the design of fluidized bed reactors. Common approaches for the prediction of the minimum fluidization velocities of binary-solid fluidized beds are first discussed here. The data of our own careful experimental investigation involving a binary-solid pair fluidized with water is presented. The effect of the relative composition of the two solid species comprising the fluidized bed on the bed void fraction at the incipient fluidization condition is reported and its influence on the minimum fluidization velocity is discussed. In this connection, the capability of packing models to predict the bed void fraction is also examined.

Keywords: Bed void fraction, Binary solid mixture, Minimumfluidization velocity, Packing models

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Authors: Dariush Jafari, S. Mostafa Nowee

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In this study a ternary system containing sodium chloride as solute, water as primary solvent and ethanol as the antisolvent was considered to investigate the application of artificial neural network (ANN) in prediction of sodium solubility in the mixture of water as the solvent and ethanol as the antisolvent. The system was previously studied using by Extended UNIQUAC model by the authors of this study. The comparison between the results of the two models shows an excellent agreement between them (R2=0.99), and also approves the capability of ANN to predict the thermodynamic behavior of ternary electrolyte systems which are difficult to model.

Keywords: Thermodynamic modeling, ANN, solubility, ternary electrolyte system.

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Authors: Chalermsak Leetrakool, Komson Jirapattarasilp

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This research was to study effect of rotational speed and eccentric factors, which were affected on looseness of bearing. The experiment was conducted on three rotational speeds and five eccentric distances with 5 replications. The results showed that influenced factor affected to looseness of bearing was rotational speed and eccentric distance which showed statistical significant. Higher rotational speed would cause on high looseness. Moreover, more eccentric distance, more looseness of bearing. Using bearing at high rotational with high eccentric of shaft would be affected bearing fault more than lower rotational speed. The prediction equation of looseness was generated by regression analysis. The prediction has an effected to the looseness of bearing at 91.5%.

Keywords: Bearing, Looseness, Rotational speed, Eccentric

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Authors: H. B. Mehta, Vipul M. Patel, Jyotirmay Banerjee

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The present paper discusses the prediction of gas-liquid two-phase frictional pressure drop in a 2.12 mm horizontal circular minichannel using Artificial Neural Network (ANN). The experimental results are obtained with air as gas phase and water as liquid phase. The superficial gas velocity is kept in the range of 0.0236 m/s to 0.4722 m/s while the values of 0.0944 m/s, 0.1416 m/s and 0.1889 m/s are considered for superficial liquid velocity. The experimental results are predicted using different Artificial Neural Network (ANN) models. Networks used for prediction are radial basis, generalised regression, linear layer, cascade forward back propagation, feed forward back propagation, feed forward distributed time delay, layer recurrent, and Elman back propagation. Transfer functions used for networks are Linear (PURELIN), Logistic sigmoid (LOGSIG), tangent sigmoid (TANSIG) and Gaussian RBF. Combination of networks and transfer functions give different possible neural network models. These models are compared for Mean Absolute Relative Deviation (MARD) and Mean Relative Deviation (MRD) to identify the best predictive model of ANN.

Keywords: Minichannel, Two-Phase Flow, Frictional Pressure Drop, ANN, MARD, MRD.

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Authors: Pawit Mongkolprasit, Proud Arunrangsiwed

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Lately, many media organizations in Thailand have started to produce 3D animation, so the quality of personnel should be identified. As an instructor in the school of Animation and Multimedia, the researchers have to prepare the students, suitable for the need of industry. The current study used exploratory research design to establish the knowledge of about this issue, including the required qualification of employees and the potential of animation industry in Thailand. The interview sessions involved three key informants from three well-known organizations. The interview data was used to design a questionnaire for the confirmation phase. The overall results showed that the industry needed an individual with 3D animation skill, computer graphic skills, good communication skills, a high responsibility, and an ability to finish the project on time. Moreover, it is also found that there were currently various kinds of media where 3D animation has been involved, such as films, TV variety, TV advertising, online advertising, and application on mobile device.

Keywords: Animation, marketing trend, animation industry, Thailand animation.

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Authors: Mohd Shahrom Ismail, B.T. Hang Tuah Baharudin, K.K.B. Hon

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In this paper, a methodology of a model based on predicting the tool forces oblique machining are introduced by adopting the orthogonal technique. The applied analytical calculation is mostly based on Devries model and some parts of the methodology are employed from Amareggo-Brown model. Model validation is performed by comparing experimental data with the prediction results on machining titanium alloy (Ti-6Al-4V) based on micro-cutting tool perspective. Good agreements with the experiments are observed. A detailed friction form that affected the tool forces also been examined with reasonable results obtained.

Keywords: dynamics machining, micro cutting tool, Tool forces

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Authors: Yusuf S. Dambatta, Ahmed A. D. Sarhan

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Fused deposition modelling (FDM) is one of the most prominent rapid prototyping (RP) technologies which is being used to efficiently fabricate CAD 3D geometric models. However, the process is coupled with many drawbacks, of which the surface quality of the manufactured RP parts is among. Hence, studies relating to improving the surface roughness have been a key issue in the field of RP research. In this work, a technique of modelling the surface roughness in FDM is presented. Using experimentally measured surface roughness response of the FDM parts, an ANFIS prediction model was developed to obtain the surface roughness in the FDM parts using the main critical process parameters that affects the surface quality. The ANFIS model was validated and compared with experimental test results.

Keywords: Surface roughness, fused deposition modelling, adaptive neuro fuzzy inference system, ANFIS, orientation.

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Authors: D. Zabala, N. Sánchez, J. Pinto

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A transient heat transfer mathematical model for the prediction of temperature distribution in the car body during primer baking has been developed by considering the thermal radiation and convection in the furnace chamber and transient heat conduction governing equations in the car framework. The car cockpit is considered like a structure with six flat plates, four vertical plates representing the car doors and the rear and front panels. The other two flat plates are the car roof and floor. The transient heat conduction in each flat plate is modeled by the lumped capacitance method. Comparison with the experimental data shows that the heat transfer model works well for the prediction of thermal behavior of the car body in the curing furnace, with deviations below 5%.

Keywords: Transient heat transfer, car body, lumpedcapacitance, primer baking.

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Authors: Antoni Wibowo, Harry Pujianto, Dewi Retno Sari Saputro

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The stock market can provide huge profits in a relatively short time in financial sector; however, it also has a high risk for investors and traders if they are not careful to look the factors that affect the stock market. Therefore, they should give attention to the dynamic fluctuations and movements of the stock market to optimize profits from their investment. In this paper, we present a nonlinear autoregressive exogenous model (NARX) to predict the movements of stock market; especially, the movements of the closing price index. As case study, we consider to predict the movement of the closing price in Indonesia composite index (IHSG) and choose the best structures of NARX for IHSG’s prediction.

Keywords: NARX, prediction, stock market, time series.

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Authors: Amiel Satvedi, Richard Pyne

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Injuries in professional sports occur on a regular basis. Some may be minor while others can cause huge impact on a player’s career and earning potential. In soccer, there is a high risk of players picking up injuries during game time. This research work seeks to help soccer players reduce the risk of getting injured by predicting the likelihood of injury while playing in the near future and then providing recommendations for intervention. The injury prediction tool will use a soccer player’s number of minutes played on the field, number of appearances, distance covered and performance data for the current and previous seasons as variables to conduct statistical analysis and provide injury predictive results using a machine learning linear regression model.

Keywords: Injury predictor, soccer injury prevention, machine learning in soccer, big data in soccer.

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Authors: Raja Kannusamy

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The research focuses on the applicable technologies in the automobile industry and their effects on the productivity and annual revenue of the industry. A study has been conducted on six major automobile manufacturing industries represented in this research as M1, M2, M3, M4, M5 and M6. The results indicate that M1, which is a pioneer in technological applications, remains the market leader, followed by M5 and M2 taking the second and third positions, respectively. M3, M6 and M4 are the followers and are placed next in positions. It has also been observed that M1 and M2 have entered into an agreement to share the basic structural technologies and they maintain long-term and trusted relationships with their suppliers through the Keiretsu system. With technological giants such as Apple, Microsoft, Uber and Google entering the automobile industry in recent years, an upward trend is expected in the futuristic market with self-driving cars to dominate the automobile sector. To keep up with the market trend, it is essential for automobile manufacturers to understand the importance of developing technological capabilities and skills to be competitive in the marketplace.

Keywords: automobile manufacturing industries, competitiveness, performance improvement, technological applications

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Authors: A. H. Akhaveissy

Abstract:

Nonlinear finite element method with eight noded isoparametric quadrilateral element is used for prediction of loaddeformation behavior including bearing capacity of foundations. Modified generalized plasticity model with non-associated flow rule is applied for analysis of soil-footing system. Also Von Mises and Tresca criterions are used for simulation of soil behavior. Modified generalized plasticity model is able to simulate load-deformation including softening behavior. Localization phenomena are considered by different meshes. Localization phenomena have not been seen in the examples. Predictions by modified generalized plasticity model show good agreement with laboratory data and theoretical prediction in comparison the other models.

Keywords: Localization phenomena, Generalized plasticity, Non-associated Flow Rule

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Authors: Fatih Kagnici

Abstract:

Improvement in CAE methods has an important role for shortening of the vehicle product development time. It is provided that validation of the design and improvements in terms of durability can be done without hardware prototype production. In recent years, several different methods have been developed in order to investigate fatigue damage of the vehicle. The intended goal among these methods is prediction of fatigue damage in a short time with reduced costs. This study developed a new fatigue damage prediction method in the automotive sector using power spectrum densities of accelerations. This study also confirmed that the weak region in vehicle can be easily detected with the method developed in this study which results were compared with conventional method.

Keywords: Fatigue damage, Power spectrum density, Vibration induced fatigue, Vehicle development

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Authors: Danilo López, Johana Hernández, Edwin Rivas

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The use of the Multilayer Perceptron Neural Networks (MLPNN) technique is presented to estimate the future state of use of a licensed channel by primary users (PUs); this will be useful at the spectral decision stage in cognitive radio networks (CRN) to determine approximately in which time instants of future may secondary users (SUs) opportunistically use the spectral bandwidth to send data through the primary wireless network. To validate the results, sequences of occupancy data of channel were generated by simulation. The results show that the prediction percentage is greater than 60% in some of the tests carried out.

Keywords: Cognitive radio, neural network, prediction, primary user.

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Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

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In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: Deep learning, indoor quality, metabolism, predictive model.

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Authors: Lokendra K. Balyan

Abstract:

In this paper, we present an algorithm for computing a Schur factorization of a real nonsymmetric matrix with ordered diagonal blocks such that upper left blocks contains the largest magnitude eigenvalues. Especially in case of multiple eigenvalues, when matrix is non diagonalizable, we construct an invariant subspaces with few additional tricks which are heuristic and numerical results shows the stability and accuracy of the algorithm.

Keywords: Schur Factorization, Eigenvalues of nonsymmetric matrix, Orthoganal matrix.

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Authors: Irene Luque Ruiz, Manuel Urbano Cuadrado, Miguel Ángel Gómez-Nieto

Abstract:

New graph similarity methods have been proposed in this work with the aim to refining the chemical information extracted from molecules matching. For this purpose, data fusion of the isomorphic and nonisomorphic subgraphs into a new similarity measure, the Approximate Similarity, was carried out by several approaches. The application of the proposed method to the development of quantitative structure-activity relationships (QSAR) has provided reliable tools for predicting several pharmacological parameters: binding of steroids to the globulin-corticosteroid receptor, the activity of benzodiazepine receptor compounds, and the blood brain barrier permeability. Acceptable results were obtained for the models presented here.

Keywords: Graph similarity, Nonisomorphic dissimilarity, Approximate similarity, Drug activity prediction.

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Authors: S.I Sulaiman, T.K Abdul Rahman, I. Musirin, S. Shaari

Abstract:

This paper presents performance analysis of the Evolutionary Programming-Artificial Neural Network (EPANN) based technique to optimize the architecture and training parameters of a one-hidden layer feedforward ANN model for the prediction of energy output from a grid connected photovoltaic system. The ANN utilizes solar radiation and ambient temperature as its inputs while the output is the total watt-hour energy produced from the grid-connected PV system. EP is used to optimize the regression performance of the ANN model by determining the optimum values for the number of nodes in the hidden layer as well as the optimal momentum rate and learning rate for the training. The EPANN model is tested using two types of transfer function for the hidden layer, namely the tangent sigmoid and logarithmic sigmoid. The best transfer function, neural topology and learning parameters were selected based on the highest regression performance obtained during the ANN training and testing process. It is observed that the best transfer function configuration for the prediction model is [logarithmic sigmoid, purely linear].

Keywords: Artificial neural network (ANN), Correlation coefficient (R), Evolutionary programming-ANN (EPANN), Photovoltaic (PV), logarithmic sigmoid and tangent sigmoid.

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Authors: H.Mohammadi Majd, M.Jalali Azizpour, A.V. Hoseini

Abstract:

In this paper back-propagation artificial neural network (BPANN) is employed to predict the limiting drawing ratio (LDR) of the deep drawing process. To prepare a training set for BPANN, some finite element simulations were carried out. die and punch radius, die arc radius, friction coefficient, thickness, yield strength of sheet and strain hardening exponent were used as the input data and the LDR as the specified output used in the training of neural network. As a result of the specified parameters, the program will be able to estimate the LDR for any new given condition. Comparing FEM and BPANN results, an acceptable correlation was found.

Keywords: Back-propagation artificial neural network(BPANN), deep drawing, prediction, limiting drawing ratio (LDR).

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Authors: Jan Stodt, Christoph Reich

Abstract:

The usage of machine learning models for prediction is growing rapidly and proof that the intended requirements are met is essential. Audits are a proven method to determine whether requirements or guidelines are met. However, machine learning models have intrinsic characteristics, such as the quality of training data, that make it difficult to demonstrate the required behavior and make audits more challenging. This paper describes an ML audit framework that evaluates and reviews the risks of machine learning applications, the quality of the training data, and the machine learning model. We evaluate and demonstrate the functionality of the proposed framework by auditing an steel plate fault prediction model.

Keywords: Audit, machine learning, assessment, metrics.

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Authors: Zane Turner, Kevin Labille, Susan Gauch

Abstract:

Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We present a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: Lexicon, sentiment analysis, stock movement prediction., computational finance.

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Authors: Chin-Pin, Chen, Shi-Chi, Shiao, Ting-Hao, Lin

Abstract:

Current industry is facing the rapid development of new technology in the world and fierce changes of economic environment in the society so that the industry development trend gradually does not focus on labor, but leads the industry and the academic circle with innovation and creativity. The development trend in machinery industry presents the same situation. Based on the aim of Creativity White Paper, Ministry of Education in Taiwan promotes and develops various creativity contests to cope with the industry trend. Domestic students and enterprises have good performance on domestic and international creativity contests in recent years. There must be important creative elements in such creative works to win the award among so many works. Literature review and in-depth interview with five creativity contest awarded instructors are first proceeded to conclude 15 machinery creative elements, which are further compared with the creative elements of machinery awarded creative works in past five years to understand the relationship between awarded works and creative elements. The statistical analysis results show that IDEA (Industrial Design Excellence Award) contains the most creative elements among four major international creativity contests. That is, most creativity review focuses on creative elements that are comparatively stricter. Concerning the groups participating in creativity contests, enterprises consider more creative elements of the creative works than other two elements for contests. From such contest works, creative elements of “replacement or improvement”, “convenience”, and “modeling” present higher significance. It is expected that the above findings could provide domestic colleges and universities with reference for participating in creativity related contests in the future.

Keywords: Machinery, creativity contest, creative elements, creativity works.

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Authors: J. Hodicky, P. Frantis

Abstract:

Command and Control (C2) system and its interfacethe Common Operational Picture (COP) are main means that supports commander in its decision making process. COP contains information about friendly and enemy unit positions. The friendly position is gathered via tactical network. In the case of tactical network failure the information about units are not available. The tactical simulator can be used as a tool that is capable to predict movements of units in respect of terrain features. Article deals with an experiment that was based on Czech C2 system that is in the case of connectivity lost fed by VR Forces simulator. Article analyzes maximum time interval in which the position created by simulator is still usable and truthful for commander in real time.

Keywords: command and control system, movement prediction, simulation

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