Search results for: price prediction.

Authors: Parvinder S. Sandhu, Satish Kumar Dhiman, Anmol Goyal

Abstract:

Fault-proneness of a software module is the probability that the module contains faults. A correlation exists between the fault-proneness of the software and the measurable attributes of the code (i.e. the static metrics) and of the testing (i.e. the dynamic metrics). Early detection of fault-prone software components enables verification experts to concentrate their time and resources on the problem areas of the software system under development. This paper introduces Genetic Algorithm based software fault prediction models with Object-Oriented metrics. The contribution of this paper is that it has used Metric values of JEdit open source software for generation of the rules for the classification of software modules in the categories of Faulty and non faulty modules and thereafter empirically validation is performed. The results shows that Genetic algorithm approach can be used for finding the fault proneness in object oriented software components.

Keywords: Genetic Algorithms, Software Fault, Classification, Object Oriented Metrics.

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Authors: Ethar H. Khalil, Bahaa I. Kazem

Abstract:

In this work a software simulation model has been proposed for two driven wheels mobile robot path planning; that can navigate in dynamic environment with static distributed obstacles. The work involves utilizing Bezier curve method in a proposed N order matrix form; for engineering the mobile robot path. The Bezier curve drawbacks in this field have been diagnosed. Two directions: Up and Right function has been proposed; Probability Recursive Function (PRF) to overcome those drawbacks. PRF functionality has been developed through a proposed; obstacle detection function, optimization function which has the capability of prediction the optimum path without comparison between all feasible paths, and N order Bezier curve function that ensures the drawing of the obtained path. The simulation results that have been taken showed; the mobile robot travels successfully from starting point and reaching its goal point. All obstacles that are located in its way have been avoided. This navigation is being done successfully using the proposed PRF techniques.

Keywords: Mobile robot, path planning, Bezier curve.

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Authors: Asar Khan, Peter D. Widdop, Andrew J. Day, Aliaster S. Wood, Steve, R. Mounce, John Machell

Abstract:

This paper describes an automated event detection and location system for water distribution pipelines which is based upon low-cost sensor technology and signature analysis by an Artificial Neural Network (ANN). The development of a low cost failure sensor which measures the opacity or cloudiness of the local water flow has been designed, developed and validated, and an ANN based system is then described which uses time series data produced by sensors to construct an empirical model for time series prediction and classification of events. These two components have been installed, tested and verified in an experimental site in a UK water distribution system. Verification of the system has been achieved from a series of simulated burst trials which have provided real data sets. It is concluded that the system has potential in water distribution network management.

Keywords: Detection, leakage, neural networks, sensors, water distribution networks

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Authors: Marco Actis Grande, Somlak Wannarumon

Abstract:

This paper proposes the numerical simulation of the investment casting of gold jewelry. It aims to study the behavior of fluid flow during mould filling and solidification and to optimize the process parameters, which lead to predict and control casting defects such as gas porosity and shrinkage porosity. A finite difference method, computer simulation software FLOW-3D was used to simulate the jewelry casting process. The simplified model was designed for both numerical simulation and real casting production. A set of sensor acquisitions were allocated on the different positions of the wax tree of the model to detect filling times, while a set of thermocouples were allocated to detect the temperature during casting and cooling. Those detected data were applied to validate the results of the numerical simulation to the results of the real casting. The resulting comparisons signify that the numerical simulation can be used as an effective tool in investment-casting-process optimization and casting-defect prediction.

Keywords: Computer fluid dynamic, Investment casting, Jewelry, Mould filling, Simulation.

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Authors: Hossein Mamusi, Abolfazl Masoumi, Ramezanali Mahdavinezhad

Abstract:

Tailor-welded Blanks (TWBs) are tailor made for different complex component designs by welding multiple metal sheets with different thicknesses, shapes, coatings or strengths prior to forming. In this study the Hemispherical Die Stretching (HDS) test (out-of-plane stretching) of TWBs were simulated via ABAQUS/Explicit to obtain the Forming Limit Diagrams (FLDs) of Stainless steel (AISI 304) laser welded blanks with different thicknesses. Two criteria were used to detect the start of necking to determine the FLD for TWBs and parent sheet metals. These two criteria are the second derivatives of the major and thickness strains that are given from the strain history of simulation. In the other word, in these criteria necking starts when the second derivative of thickness or major strain reaches its maximum. With having the time of onset necking, one can measure the major and minor strains at the critical area and determine the forming limit curve.

Keywords: TWB, Forming Limit Diagram, Necking criteria, ABAQUS/Explicit

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Authors: A. Pedram, M. R. Jamali, T. Pedram, S. M. Fakhraie, C. Lucas

Abstract:

Local Linear Neuro-Fuzzy Models (LLNFM) like other neuro- fuzzy systems are adaptive networks and provide robust learning capabilities and are widely utilized in various applications such as pattern recognition, system identification, image processing and prediction. Local linear model tree (LOLIMOT) is a type of Takagi-Sugeno-Kang neuro fuzzy algorithm which has proven its efficiency compared with other neuro fuzzy networks in learning the nonlinear systems and pattern recognition. In this paper, a dedicated reconfigurable and parallel processing hardware for LOLIMOT algorithm and its applications are presented. This hardware realizes on-chip learning which gives it the capability to work as a standalone device in a system. The synthesis results on FPGA platforms show its potential to improve the speed at least 250 of times faster than software implemented algorithms.

Keywords: LOLIMOT, hardware, neurofuzzy systems, reconfigurable, parallel.

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Authors: Lu Si, Jie Yu, Shasha Li, Jun Ma, Lei Luo, Qingbo Wu, Yongqi Ma, Zhengji Liu

Abstract:

Instance selection (IS) technique is used to reduce the data size to improve the performance of data mining methods. Recently, to process very large data set, several proposed methods divide the training set into some disjoint subsets and apply IS algorithms independently to each subset. In this paper, we analyze the limitation of these methods and give our viewpoint about how to divide and conquer in IS procedure. Then, based on fast condensed nearest neighbor (FCNN) rule, we propose a large data sets instance selection method with MapReduce framework. Besides ensuring the prediction accuracy and reduction rate, it has two desirable properties: First, it reduces the work load in the aggregation node; Second and most important, it produces the same result with the sequential version, which other parallel methods cannot achieve. We evaluate the performance of FCNN-MR on one small data set and two large data sets. The experimental results show that it is effective and practical.

Keywords: Instance selection, data reduction, MapReduce, kNN.

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Authors: Sasitharan Ambicapathy, J. Vignesh, P. Sivaraj, Godfrey Derek Sams, K. Sabarinath, V. R. Sanal Kumar

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The external flow simulation of a flying car at take off phase is a daunting task owing to the fact that the prediction of the transient unsteady flow features during its deployment phase is very complex. In this paper 3D numerical simulations of external flow of Ferrari F430 proposed flying car with different NACA 9618 rectangular wings have been carried. Additionally, the aerodynamics characteristics have been generated for optimizing its geometry for achieving the minimum take off velocity with better overall performance in both road and air. The three-dimensional standard k-omega turbulence model has been used for capturing the intrinsic flow physics during the take off phase. In the numerical study, a fully implicit finite volume scheme of the compressible, Reynolds-Averaged, Navier-Stokes equations is employed. Through the detailed parametric analytical studies we have conjectured that Ferrari F430 flying car facilitated with high wings having three different deployment histories during the take off phase is the best choice for accomplishing its better performance for the commercial applications.

Keywords: Aerodynamics of flying car, air taxi, negative lift. roadable airplane.

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Authors: Vitalijs Lusis, Andrejs Krasnikovs

Abstract:

Fiber reinforced concrete is important material for load bearing structural elements. Usually fibers are homogeneously distributed in a concrete body having arbitrary spatial orientations. At the same time, in many situations, fiber concrete with oriented fibers is more optimal. Is obvious, that is possible to create constructions with oriented short fibers in them, in different ways. Present research is devoted to one of such approaches- fiber reinforced concrete prisms having dimensions 100mm ×100mm ×400mmwith layers of non-homogeneously distributed fibers inside them were fabricated.

Simultaneously prisms with homogeneously dispersed fibers were produced for reference as well. Prisms were tested under four point bending conditions. During the tests vertical deflection at the center of every prism and crack opening were measured (using linear displacements transducers in real timescale). Prediction results were discussed.

Keywords: Fiber reinforced concrete, 4-point bending, steel fiber.

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Authors: Çiğdem Bakır, Mecit Yüzkat

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With the increasing emphasis on water quality worldwide, the search for and expanding the market for new and intelligent monitoring systems has increased. The current method is the laboratory process, where samples are taken from bodies of water, and tests are carried out in laboratories. This method is time-consuming, a waste of manpower and uneconomical. To solve this problem, we used machine learning methods to detect water pollution in our study. We created decision trees with the Orange3 software used in the study and tried to determine all the factors that cause water pollution. An automatic prediction model based on water quality was developed by taking many model inputs such as water temperature, pH, transparency, conductivity, dissolved oxygen, and ammonia nitrogen with machine learning methods. The proposed approach consists of three stages: Preprocessing of the data used, feature detection and classification. We tried to determine the success of our study with different accuracy metrics and the results were presented comparatively. In addition, we achieved approximately 98% success with the decision tree.

Keywords: Decision tree, water quality, water pollution, machine learning.

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Authors: Ali Alotaibi, Francis Edum-Fotwe, Andrew Price /

Abstract:

Mega construction projects create buildings and major infrastructure to respond to work and life requirements while playing a vital role in promoting any nation’s economy. However, the industry is often criticised for not balancing economic, environmental and social dimensions of their projects, with emphasis typically on one aspect to the detriment of the others. This has resulted in many negative impacts including environmental pollution, waste throughout the project lifecycle, low productivity, and avoidable accidents. The identification of comprehensive Social Responsibility (SR) indicators, which combine social, environmental and economic aspects, is urgently needed. This is particularly the case in the context of the Kingdom of Saudi Arabia (KSA), which often has mega public construction projects. The aim of this paper is to develop a set of wide-ranging SR indicators which encompass social, economic and environmental aspects unique to the KSA. A qualitative approach was applied to explore relevant indicators through a review of the existing literature, international standards and reports. A list of appropriate indicators was developed, and its comprehensiveness was corroborated by interviews with experts on mega construction projects working with SR concepts in the KSA. The findings present 39 indicators and their metrics, covering 10 economic, 12 environmental and 17 social aspects of SR mapped against their references. These indicators are a valuable reference for decision-makers and academics in the KSA to understand factors related to SR in mega construction projects. The indicators are related to mega construction projects within the KSA and require validation in a real case scenario or within a different industry to demonstrate their generalisability.

Keywords: Social responsibility, construction projects, economic, social, environmental, indicators.

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Authors: Ramandeep S. Sidhu, Sunil Khullar, Parvinder S. Sandhu, R. P. S. Bedi, Kiranbir Kaur

Abstract:

In this paper, subtractive clustering based fuzzy inference system approach is used for early detection of faults in the function oriented software systems. This approach has been tested with real time defect datasets of NASA software projects named as PC1 and CM1. Both the code based model and joined model (combination of the requirement and code based metrics) of the datasets are used for training and testing of the proposed approach. The performance of the models is recorded in terms of Accuracy, MAE and RMSE values. The performance of the proposed approach is better in case of Joined Model. As evidenced from the results obtained it can be concluded that Clustering and fuzzy logic together provide a simple yet powerful means to model the earlier detection of faults in the function oriented software systems.

Keywords: Subtractive clustering, fuzzy inference system, fault proneness.

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Authors: Mohamed Hafid, Marcel Lacroix

Abstract:

This study presents an inverse analysis for predicting the thermal conductivities and the heat flux of a high-temperature metallurgical reactor simultaneously. Once these thermal parameters are predicted, the time-varying thickness of the protective phase-change bank that covers the inside surface of the brick walls of a metallurgical reactor can be calculated. The enthalpy method is used to solve the melting/solidification process of the protective bank. The inverse model rests on the Levenberg-Marquardt Method (LMM) combined with the Broyden method (BM). A statistical analysis for the thermal parameter estimation is carried out. The effect of the position of the temperature sensors, total number of measurements and measurement noise on the accuracy of inverse predictions is investigated. Recommendations are made concerning the location of temperature sensors.

Keywords: Inverse heat transfer, phase change, metallurgical reactor, Levenberg–Marquardt method, Broyden method, bank thickness.

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Authors: Piyali Chaudhuri, Peter T. Martin, Aleksandar Z. Stevanovic, Chongkai Zhu

Abstract:

Loop detectors report traffic characteristics in real time. They are at the core of traffic control process. Intuitively, one would expect that as density of detection increases, so would the quality of estimates derived from detector data. However, as detector deployment increases, the associated operating and maintenance cost increases. Thus, traffic agencies often need to decide where to add new detectors and which detectors should continue receiving maintenance, given their resource constraints. This paper evaluates the effect of detector spacing on freeway travel time estimation. A freeway section (Interstate-15) in Salt Lake City metropolitan region is examined. The research reveals that travel time accuracy does not necessarily deteriorate with increased detector spacing. Rather, the actual location of detectors has far greater influence on the quality of travel time estimates. The study presents an innovative computational approach that delivers optimal detector locations through a process that relies on Genetic Algorithm formulation.

Keywords: Detector, Freeway, Genetic algorithm, Travel timeestimate.

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Authors: Arturs Macanovskis, Vitalijs Lusis, Andrejs Krasnikovs

Abstract:

This work had three stages. In the first stage was examined pull-out process for steel fiber was embedded into a concrete by one end and was pulled out of concrete under the angle to pulling out force direction. Angle was varied. On the obtained forcedisplacement diagrams were observed jumps. For such mechanical behavior explanation, fiber channel in concrete surface microscopical experimental investigation, using microscope KEYENCE VHX2000, was performed. At the second stage were obtained diagrams for load- crack opening displacement for breaking homogeneously reinforced and layered fiberconcrete prisms (with dimensions 10x10x40cm) subjected to 4-point bending. After testing was analyzed main crack. At the third stage elaborated prediction model for the fiberconcrete beam, failure under bending, using the following data: a) diagrams for fibers pulling out at different angles; b) experimental data about steel-straight fibers locations in the main crack. Experimental and theoretical (modeling) data were compared.

Keywords: Fiberconcrete, pull-out, fiber channel, layered fiberconcrete.

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Authors: J. Menčík

Abstract:

Actual load, material characteristics and other quantities often differ from the design values. This can cause worse function, shorter life or failure of a civil engineering structure, a machine, vehicle or another appliance. The paper shows main causes of the uncertainties and deviations and presents a systematic approach and efficient tools for their elimination or mitigation of consequences. Emphasis is put on the design stage, which is most important for reliability ensuring. Principles of robust design and important tools are explained, including FMEA, sensitivity analysis and probabilistic simulation methods. The lifetime prediction of long-life objects can be improved by long-term monitoring of the load response and damage accumulation in operation. The condition evaluation of engineering structures, such as bridges, is often based on visual inspection and verbal description. Here, methods based on fuzzy logic can reduce the subjective influences.

Keywords: Design, fuzzy methods, Monte Carlo, reliability, robust design, sensitivity analysis, simulation, uncertainties.

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Authors: Ajoy Kumar Das, Prasenjit Dey

Abstract:

Heat transfer due to forced convection of copper water based nanofluid has been predicted by Artificial Neural network (ANN). The present nanofluid is formed by mixing copper nanoparticles in water and the volume fractions are considered here are 0% to 15% and the Reynolds number are kept constant at 100. The back propagation algorithm is used to train the network. The present ANN is trained by the input and output data which has been obtained from the numerical simulation, performed in finite volume based Computational Fluid Dynamics (CFD) commercial software Ansys Fluent. The numerical simulation based results are compared with the back propagation based ANN results. It is found that the forced convection heat transfer of water based nanofluid can be predicted correctly by ANN. It is also observed that the back propagation ANN can predict the heat transfer characteristics of nanofluid very quickly compared to standard CFD method.

Keywords: Forced convection, Square cylinder, nanofluid, neural network.

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Authors: M. Chandrasekaran, D. Devarasiddappa

Abstract:

Metal matrix composites have been increasingly used as materials for components in automotive and aerospace industries because of their improved properties compared with non-reinforced alloys. During machining the selection of appropriate machining parameters to produce job for desired surface roughness is of great concern considering the economy of manufacturing process. In this study, a surface roughness prediction model using fuzzy logic is developed for end milling of Al-SiCp metal matrix composite component using carbide end mill cutter. The surface roughness is modeled as a function of spindle speed (N), feed rate (f), depth of cut (d) and the SiCp percentage (S). The predicted values surface roughness is compared with experimental result. The model predicts average percentage error as 4.56% and mean square error as 0.0729. It is observed that surface roughness is most influenced by feed rate, spindle speed and SiC percentage. Depth of cut has least influence.

Keywords: End milling, fuzzy logic, metal matrix composites, surface roughness

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Authors: Md A. Hossain, Shabab M. Chowdhury, Yamin Rekhu, Khandakar S. Faraz, Monzur Ul Islam

Abstract:

With the growth of modern civilization and industrialization in worldwide, the demand for energy is increasing day by day. Majority of the world-s energy needs are met through fossil fuels and natural gas. As a result the amount of fossil fuels is on diminishing from year to year. Since the fossil fuel is nonrenewable, so fuel price is gouging as a consequence of spiraling demand and diminishing supply. At present the power generation of our country is mainly depends on imported fossil fuels. To reduce the dependency on imported fuel, the use of renewable sources has become more popular. In Bangladesh coconut is widely growing tree. Especially in the southern part of the country a large area will be found where coconut tree is considered as natural asset. So, our endeavor was to use the coconut oil as a renewable and alternative fuel. This article shows the prospect of coconut oil as a renewable and alternative fuel of diesel fuel. Since diesel engine has a versatile uses including small electricity generation, an experimental set up is then made to study the performance of a small diesel engine using different blends of bio diesel converted from coconut oil. It is found that bio diesel has slightly different properties than diesel. With biodiesel the engine is capable of running without difficulty. Different blends of bio diesel (i.e. B80, B60, and B 50 etc.) have been used to avoid complicated modification of the engine or the fuel supply system. Finally, a comparison of engine performance for different blends of biodiesel has been carried out to determine the optimum blend for different operating conditions.

Keywords: Biodiesel, Bio-fuel, Renewable Energy, Transesterification

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Authors: Sagir M. Yusuf, Chris Baber

Abstract:

We describe issues bedeviling the coordination of heterogeneous (different sensors carrying agents) multi-agent missions such as belief conflict, situation reasoning, etc. We applied Bayesian and agents' presumptions inferential reasoning to solve the outlined issues with the heterogeneous multi-agent belief variation and situational-base reasoning. Bayesian Belief Network (BBN) was used in modeling the agents' belief conflict due to sensor variations. Simulation experiments were designed, and cases from agents’ missions were used in training the BBN using gradient descent and expectation-maximization algorithms. The output network is a well-trained BBN for making inferences for both agents and human experts. We claim that the Bayesian learning algorithm prediction capacity improves by the number of training data and argue that it enhances multi-agents robustness and solve agents’ sensor conflicts.

Keywords: Distributed constraint optimization problem, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence.

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Authors: Amir Azizi, Amir Yazid b. Ali, Loh Wei Ping, Mohsen Mohammadzadeh

Abstract:

Uncertainties of a serial production line affect on the production throughput. The uncertainties cannot be prevented in a real production line. However the uncertain conditions can be controlled by a robust prediction model. Thus, a hybrid model including autoregressive integrated moving average (ARIMA) and multiple polynomial regression, is proposed to model the nonlinear relationship of production uncertainties with throughput. The uncertainties under consideration of this study are demand, breaktime, scrap, and lead-time. The nonlinear relationship of production uncertainties with throughput are examined in the form of quadratic and cubic regression models, where the adjusted R-squared for quadratic and cubic regressions was 98.3% and 98.2%. We optimized the multiple quadratic regression (MQR) by considering the time series trend of the uncertainties using ARIMA model. Finally the hybrid model of ARIMA and MQR is formulated by better adjusted R-squared, which is 98.9%.

Keywords: ARIMA, multiple polynomial regression, production throughput, uncertainties

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Authors: Mohammad S. Laghari, Qurban A. Memon, Gulzar A. Khuwaja

Abstract:

The paper describes a knowledge based system for analysis of microscopic wear particles. Wear particles contained in lubricating oil carry important information concerning machine condition, in particular the state of wear. Experts (Tribologists) in the field extract this information to monitor the operation of the machine and ensure safety, efficiency, quality, productivity, and economy of operation. This procedure is not always objective and it can also be expensive. The aim is to classify these particles according to their morphological attributes of size, shape, edge detail, thickness ratio, color, and texture, and by using this classification thereby predict wear failure modes in engines and other machinery. The attribute knowledge links human expertise to the devised Knowledge Based Wear Particle Analysis System (KBWPAS). The system provides an automated and systematic approach to wear particle identification which is linked directly to wear processes and modes that occur in machinery. This brings consistency in wear judgment prediction which leads to standardization and also less dependence on Tribologists.

Keywords: Computer vision, knowledge based systems, morphology, wear particles.

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Authors: M. Hossein Ghadiri, Mohd Farid Mohamed, N. Lukman N. Ibrahim

Abstract:

Wind catchers are traditional natural ventilation systems attached to buildings in order to ventilate the indoor air. The most common type of wind catcher is four sided one which is capable to catch wind in all directions. CFD simulation is the perfect way to evaluate the wind catcher performance. The accuracy of CFD results is the issue of concern, so sensitivity analyses is crucial to find out the effect of different settings of CFD on results. This paper presents a series of 3D steady RANS simulations for a generic isolated four-sided wind catcher attached to a room subjected to wind direction ranging from 0º to 180º with an interval of 45º. The CFD simulations are validated with detailed wind tunnel experiments. The influence of an extensive range of computational parameters is explored in this paper, including the resolution of the computational grid, the size of the computational domain and the turbulence model. This study found that CFD simulation is a reliable method for wind catcher study, but it is less accurate in prediction of models with non perpendicular wind directions.

Keywords: Wind catcher, CFD, natural ventilation, sensitivity study.

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Authors: Nasrin Farajiparvar

Abstract:

In the present article, a new method has been developed to enhance the application of equipment monitoring, which in turn results in improving condition-based maintenance economic impact in an automobile parts manufacturing factory. This study also describes how an effective software with a simple database can be utilized to achieve cost-effective improvements in maintenance performance. The most important results of this project are indicated here: 1. 63% reduction in direct and indirect maintenance costs. 2. Creating a proper database to analyse failures. 3. Creating a method to control system performance and develop it to similar systems. 4. Designing a software to analyse database and consequently create technical knowledge to face unusual condition of the system. Moreover, the results of this study have shown that the concept and philosophy of maintenance has not been understood in most Iranian industries. Thus, more investment is strongly required to improve maintenance conditions.

Keywords: Condition-based maintenance, Economic savings, Iran industries, Machine life prediction software.

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Authors: Ping Du, Masaki Sagehashi, Akihiko Terada, Masaaki Hosomi

Abstract:

Prediction of benzene transport in soil and volatilization from soil to the atmosphere is important for the preservation of human health and management of contaminated soils. The adequacy of a simple numerical model, assuming two-phase diffusion and equilibrium of liquid/solid adsorption, was investigated by experimental data of benzene concentration in a flux chamber (with headspace) where Andosol and sand were filled. Adsorption experiment for liquid phase was performed to determine an adsorption coefficient. Furthermore, adequacy of vapor phase adsorption was also studied through two runs of experiment using sand with different water content. The results show that the model adequately predicted benzene transport and volatilization from Andosol and sand with water content of 14.0%. In addition, the experiment additionally revealed that vapor phase adsorption should be considered in diffusion model for sand with very low water content.

Keywords: Benzene; Transport Model, Adsorption, Soil Contaminant.

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Authors: Zongyou He, Bashu Tsai, Chinhung Ko, Tainchi Lu

Abstract:

A procedural-animation-based approach which rapidly synthesize the adaptive locomotion for quadruped characters that they can walk or run in any directions on an uneven terrain within a dynamic environment was proposed. We devise practical motion models of the quadruped animals for adapting to a varied terrain in a real-time manner. While synthesizing locomotion, we choose the corresponding motion models by means of the footstep prediction of the current state in the dynamic environment, adjust the key-frames of the motion models relying on the terrain-s attributes, calculate the collision-free legs- trajectories, and interpolate the key-frames according to the legs- trajectories. Finally, we apply dynamic time warping to each part of motion for seamlessly concatenating all desired transition motions to complete the whole locomotion. We reduce the time cost of producing the locomotion and takes virtual characters to fit in with dynamic environments no matter when the environments are changed by users.

Keywords: Dynamic environment, motion synthesis, procedural animation, quadruped locomotion

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Authors: Ming-Hui Cheng, Jyh-Horng Jeng

Abstract:

In this study, a classification-based video super-resolution method using artificial neural network (ANN) is proposed to enhance low-resolution (LR) to high-resolution (HR) frames. The proposed method consists of four main steps: classification, motion-trace volume collection, temporal adjustment, and ANN prediction. A classifier is designed based on the edge properties of a pixel in the LR frame to identify the spatial information. To exploit the spatio-temporal information, a motion-trace volume is collected using motion estimation, which can eliminate unfathomable object motion in the LR frames. In addition, temporal lateral process is employed for volume adjustment to reduce unnecessary temporal features. Finally, ANN is applied to each class to learn the complicated spatio-temporal relationship between LR and HR frames. Simulation results show that the proposed method successfully improves both peak signal-to-noise ratio and perceptual quality.

Keywords: Super-resolution, classification, spatio-temporal information, artificial neural network.

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Authors: Tahseen A. Jilani, Huda Yasin, Madiha Yasin, C. Ardil

Abstract:

In this paper we use data mining techniques to investigate factors that contribute significantly to enhancing the risk of acute coronary syndrome. We assume that the dependent variable is diagnosis – with dichotomous values showing presence or  absence of disease. We have applied binary regression to the factors affecting the dependent variable. The data set has been taken from two different cardiac hospitals of Karachi, Pakistan. We have total sixteen variables out of which one is assumed dependent and other 15 are independent variables. For better performance of the regression model in predicting acute coronary syndrome, data reduction techniques like principle component analysis is applied. Based on results of data reduction, we have considered only 14 out of sixteen factors.

Keywords: Acute coronary syndrome (ACS), binary logistic regression analyses, myocardial ischemia (MI), principle component analysis, unstable angina (U.A.).

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Authors: Jerome T. Tolentino, Ma. Victoria Rejuso, Jara Kaye Villanueva, Loureal Camille Inocencio, Ma. Rosario Concepcion O. Ang

Abstract:

Wind energy is rapidly emerging as the primary source of electricity in the Philippines, although developing an accurate wind resource model is difficult. In this study, Weather Research and Forecasting (WRF) Model, an open source mesoscale Numerical Weather Prediction (NWP) model, was used to produce a 1-year atmospheric simulation with 4 km resolution on the Ilocos Region of the Philippines. The WRF output (netCDF) extracts the annual mean wind speed data using a Python-based Graphical User Interface. Lastly, wind resource assessment was produced using a GIS software. Results of the study showed that it is more flexible to use Python scripts than using other post-processing tools in dealing with netCDF files. Using WRF Model, Python, and Geographic Information Systems, a reliable wind resource map is produced.

Keywords: Wind resource assessment, Weather Research and Forecasting (WRF) Model, python, GIS software.

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Authors: Y. Yılmaz, C. Ünal, A. Dursun

Abstract:

Distribution channels play significant role for hotels. Direct and indirect selling options of hotel rooms have been increased especially with the help of new technologies, i.e. hotel’s own web sites and online booking sites. Although these options emerged as tools for diversifying the distribution channels, vast number of hotels -mostly resort hotels- is still heavily dependent upon international tour operators when selling their products. On the other hand, hotel sector is so vulnerable against crises. Economic, political or any other crisis can affect hotels very badly and so it is critical to have the right balance of distribution channel to avoid the adverse impacts of a crisis. In this study, it is aimed to search the impacts of a general crisis on the selling prices of hotels which have different weights of distribution channels. The study was done in Turkey where various crises occurred in 2015 and 2016 which had great negative impacts on Turkish tourism and led enormous occupancy rate and selling price reductions. 112 upscale resort hotel in Antalya, which is the most popular tourism destination of Turkey, joined to the research. According to the results, hotels with high dependency to international tour operators are more forced to reduce their room prices in crisis time compared to the ones which use their own web sites more. It was also found that the decline in room prices is limited for hotels which are working with national tour operators and travel agencies in crisis time.

Keywords: Marketing channels, crisis, hotel, international tour operators, online travel agencies.

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