Search results for: bit error probability

Authors: James Purnama, Riri Fitri Sari

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We investigate the machine learning algorithm selection problem in the term of a depth image based eye gaze estimation, with respect to its essential difficulty in reducing the number of required training samples and duration time of training. Statistics based prediction accuracy are increasingly used to assess and evaluate prediction or estimation in gaze estimation. This article evaluates Root Mean Squared Error (RMSE) and R-Squared statistical analysis to assess machine learning methods on depth camera data for gaze estimation. There are 4 machines learning methods have been evaluated: Random Forest Regression, Regression Tree, Support Vector Machine (SVM), and Linear Regression. The experiment results show that the Random Forest Regression has the lowest RMSE and the highest R-Squared, which means that it is the best among other methods.

Keywords: gaze estimation, gaze tracking, eye tracking, kinect, regression model, orange python

Procedia PDF Downloads 530

Authors: Apolinar Picado, Ronald Solís, Rafael Gamero

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The thin-layer drying characteristics of instant coffee solution were investigated in a laboratory tunnel dryer. Drying experiments were carried out at three temperatures (80, 100 and 120 °C) and an air velocity of 1.2 m/s. Drying experimental data obtained are fitted to six (6) thin-layer drying models using the non-linear least squares regression analysis. The acceptability of the thin-layer drying model has been based on a value of the correlation coefficient that should be close to one, and low values for root mean square error (RMSE) and chi-square (x²). According to this evaluation, the most suitable model for describing drying process of thin-layer instant coffee solution is the Page model. Further, the effective moisture diffusivity and the activation energy were computed employing the drying experimental data. The effective moisture diffusivity values varied from 1.6133 × 10⁻⁹ to 1.6224 × 10⁻⁹ m²/s over the temperature range studied and the activation energy was estimated to be 162.62 J/mol.

Keywords: activation energy, diffusivity, instant coffee, thin-layer models

Procedia PDF Downloads 249

Authors: Fernando P. Silva, Valter J. S. Leite, Erivelton G. Nepomuceno

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In this paper, a nonlinear controller design for an omnidirectional mobile is presented. The robot controller consists of an inner-loop controller and an outer-loop controller, the first is designed using state feedback (robust allocation) and the second controller is designed based on Robust Multi Inversion (RMI) approach. The objective of RMI controller is rendering the robust inversion of the dynamic, when the model is affected by uncertainties. A model nonlinear MIMO of an omni-directional robot (small-league of Robocup) is used to simulate the RMI approach. The parameters of linear and nonlinear model are varied to cause modelling uncertainties among the model and the real model (real system) generating an error in inner-loop controller signal that must be compensated by RMI controller. The simulation test results show that the RMI is capable of compensating the uncertainties and keep the system stable and controlled under uncertainties.

Keywords: robust multi inversion, omni-directional robot, robocup, nonlinear control

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Authors: Li Zhang, Yuehong Su

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Lightpipe as a mature solar light tube technique has been employed worldwide. Accurately assessing the performance of lightpipe and evaluate daylighting available has been a challenging topic. Previous research had used regression model and computational simulation methods to estimate the performance of lightpipe. However, due to the nonlinear nature of solar light transferring in lightpipe, the methods mentioned above express inaccurate and time-costing issues. In the present study, a neural network model as an alternative method is investigated to predict the transmittance of lightpipe. Four types of commercial lightpipe with bended angle 0°, 30°, 45° and 60° are discussed under clear, intermediate and overcast sky conditions respectively. The neural network is generated in MATLAB by using the outcomes of an optical software Photopia simulations as targets for networks training and testing. The coefficient of determination (R²) for each model is higher than 0.98, and the mean square error (MSE) is less than 0.0019, which indicate the neural network strong predictive ability and the use of the neural network method could be an efficient technique for determining the performance of lightpipe.

Keywords: neural network, bended lightpipe, transmittance, Photopia

Procedia PDF Downloads 143

Authors: Wayan Widhiada, Cok Indra Partha, Gusti Ngurah Nitya Santhiarsa

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The purpose of this paper is to investigate the sophistication of the use of Proportional Integral and Derivative Control to control the kinematic motion of the mobile robot manipulator. Simulation and experimental methods will be used to investigate the sophistication of PID control to control the mobile robot arm in the collection and placement of several kinds of objects quickly, accurately and correctly. Mathematical modeling will be done by utilizing the integration of Solidworks and MATLAB / Simmechanics software. This method works by converting the physical model file into the xml file. This method is easy, fast and accurate done in modeling and design robotics. The automatic control design of this robot manipulator will be validated in simulations and experimental in control labs as evidence that the mobile robot manipulator gripper control design can achieve the best performance such as the error signal is lower than 5%, small overshoot and get steady signal response as quickly.

Keywords: control analysis, kinematics motion, mobile robot manipulator, performance

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Authors: Piotr A. Kowalski, Kasper Sapala, Wiktor Warchalowski

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PM10 is a suspended dust that primarily has a negative effect on the respiratory system. PM10 is responsible for attacks of coughing and wheezing, asthma or acute, violent bronchitis. Indirectly, PM10 also negatively affects the rest of the body, including increasing the risk of heart attack and stroke. Unfortunately, Poland is a country that cannot boast of good air quality, in particular, due to large PM concentration levels. Therefore, based on the dense network of Airly sensors, it was decided to deal with the problem of prediction of suspended particulate matter concentration. Due to the very complicated nature of this issue, the Machine Learning approach was used. For this purpose, Convolution Neural Network (CNN) neural networks have been adopted, these currently being the leading information processing methods in the field of computational intelligence. The aim of this research is to show the influence of particular CNN network parameters on the quality of the obtained forecast. The forecast itself is made on the basis of parameters measured by Airly sensors and is carried out for the subsequent day, hour after hour. The evaluation of learning process for the investigated models was mostly based upon the mean square error criterion; however, during the model validation, a number of other methods of quantitative evaluation were taken into account. The presented model of pollution prediction has been verified by way of real weather and air pollution data taken from the Airly sensor network. The dense and distributed network of Airly measurement devices enables access to current and archival data on air pollution, temperature, suspended particulate matter PM1.0, PM2.5, and PM10, CAQI levels, as well as atmospheric pressure and air humidity. In this investigation, PM2.5, and PM10, temperature and wind information, as well as external forecasts of temperature and wind for next 24h served as inputted data. Due to the specificity of the CNN type network, this data is transformed into tensors and then processed. This network consists of an input layer, an output layer, and many hidden layers. In the hidden layers, convolutional and pooling operations are performed. The output of this system is a vector containing 24 elements that contain prediction of PM10 concentration for the upcoming 24 hour period. Over 1000 models based on CNN methodology were tested during the study. During the research, several were selected out that give the best results, and then a comparison was made with the other models based on linear regression. The numerical tests carried out fully confirmed the positive properties of the presented method. These were carried out using real ‘big’ data. Models based on the CNN technique allow prediction of PM10 dust concentration with a much smaller mean square error than currently used methods based on linear regression. What's more, the use of neural networks increased Pearson's correlation coefficient (R²) by about 5 percent compared to the linear model. During the simulation, the R² coefficient was 0.92, 0.76, 0.75, 0.73, and 0.73 for 1st, 6th, 12th, 18th, and 24th hour of prediction respectively.

Keywords: air pollution prediction (forecasting), machine learning, regression task, convolution neural networks

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Authors: Rafia Rafique, Mutmina Zainab

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The present study investigated the relationship between fear of negative evaluation (FNE), social support and well-being in people with Vitiligo. It was hypothesized that low level of FNE and greater social support is likely to predict well-being. It was also hypothesized that social support is likely to moderate the relationship between FNE and well-being. Correlational research design was used for the present study. Non-probability purposive sampling technique was used to collect a sample (N=122) of people with Vitiligo. Hierarchical Moderated Regression analysis was used to test prediction and moderation. Brief Fear of Negative Evaluation Scale, Multidimensional Scale of Perceived Social Support (MSPSS) and Mental Health Continuum-Short form (MHC-SF) were used to evaluate the study variables. Fear of negative evaluation negatively predicted well-being (emotional and psychological). Social support from significant others and friends predicted social well-being. Social Support from family predicted emotional and psychological well-being. It was found that social support from significant others moderated the relationship between FNE and emotional well-being and social support from family moderated the relationship between FNE and social well-being. Dermatologists treating people with Vitiligo need to educate them and their families about the buffering role of social support (family and significant others). Future studies need to focus on other important mediating factors that can possibly explain the relationship between fear of negative evaluation and wellbeing.

Keywords: fear of negative evaluation, hierarchical moderated regression, vitiligo, well-being

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Authors: Ceren Vardar Acar, Mine Caglar

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In finance, the price of a volatile asset can be modeled using fractional Brownian motion (fBm) with Hurst parameter H>1/2. The Black-Scholes model for the values of returns of an asset using fBm is given as, 〖Y_t=Y_0 e^((r+μ)t+σB)〗_t^H, 0≤t≤T where Y_0 is the initial value, r is constant interest rate, μ is constant drift and σ is constant diffusion coefficient of fBm, which is denoted by B_t^H where t≥0. Black-Scholes model can be constructed with some Markov processes such as Brownian motion. The advantage of modeling with fBm to Markov processes is its capability of exposing the dependence between returns. The real life data for a volatile asset display long-range dependence property. For this reason, using fBm is a more realistic model compared to Markov processes. Investors would be interested in any kind of information on the risk in order to manage it or hedge it. The maximum possible loss is one way to measure highest possible risk. Therefore, it is an important variable for investors. In our study, we give some theoretical bounds on the distribution of maximum possible loss of fBm. We provide both asymptotical and strong estimates for the tail probability of maximum loss of standard fBm and fBm with drift and diffusion coefficients. In the investment point of view, these results explain, how large values of possible loss behave and its bounds.

Keywords: maximum drawdown, maximum loss, fractional brownian motion, large deviation, Gaussian process

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Authors: Haniye Dehestani, Yadollah Ordokhani

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In this work, we present an efficient approach for solving variable-order time-fractional partial differential equations, which are based on Legendre and Laguerre polynomials. First, we introduced the pseudo-operational matrices of integer and variable fractional order of integration by use of some properties of Riemann-Liouville fractional integral. Then, applied together with collocation method and Legendre-Laguerre functions for solving variable-order time-fractional partial differential equations. Also, an estimation of the error is presented. At last, we investigate numerical examples which arise in physics to demonstrate the accuracy of the present method. In comparison results obtained by the present method with the exact solution and the other methods reveals that the method is very effective.

Keywords: collocation method, fractional partial differential equations, legendre-laguerre functions, pseudo-operational matrix of integration

Procedia PDF Downloads 157

Authors: Belkacem Laimouche

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With the field of artificial intelligence (AI) experiencing exponential growth, fueled by technological advancements that pave the way for increasingly innovative and promising applications, there is an escalating need to develop rigorous methods for assessing their performance in pursuit of transparency and equity. This article proposes a metrology-inspired statistical framework for evaluating bias and explainability in AI systems. Drawing from the principles of metrology, we propose a pioneering approach, using a concrete example, to evaluate the accuracy and precision of AI models, as well as to quantify the sources of measurement uncertainty that can lead to bias in their predictions. Furthermore, we explore a statistical approach for evaluating the explainability of AI systems based on their ability to provide interpretable and transparent explanations of their predictions.

Keywords: artificial intelligence, metrology, measurement uncertainty, prediction error, bias, machine learning algorithms, probabilistic models, interlaboratory comparison, data analysis, data reliability, measurement of bias impact on predictions, improvement of model accuracy and reliability

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Authors: Osarobo Omorogieva Ighodaro, Josephus Otejere

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This paper contains the modelling and simulation of GT13E2 combined cycle gas turbine with the aid of the software EBSILON PROFESSIONAL. The design mode was modeled using guaranteed performance data from the power plant, in the off design, temperature variation of ambient air and fogging (spray water at inlet to compressor) was simulated. The fogging was simulated under two different modes; constant fuel consumption and constant turbine exhaust temperature .The model results were validated using actual operating data by applying error percentage analysis. The validation results obtained ranged from -0.0038% to 0% in design condition while the results varied from -0.9202% to 10.24% The model shows that fogging decreases compressor inlet temperature which in turn decreases the power required to drive the compressor hence improving the simple cycle efficiency and hence increasing power generated.

Keywords: inlet fogging, off design, combined cycle, modelling

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Authors: H. Barati, M. Wu, A. Kharicha, A. Ludwig

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A transient model for nozzle clogging has been developed and successfully validated against a laboratory experiment. Key steps of clogging are considered: transport of particles by turbulent flow towards the nozzle wall; interactions between fluid flow and nozzle wall, and the adhesion of the particle on the wall; the growth of the clog layer and its interaction with the flow. The current paper is to investigate the mesh (size and type) sensitivity of the model in both two and three dimensions. It is found that the algorithm for clog growth alone excluding the flow effect is insensitive to the mesh type and size, but the calculation including flow becomes sensitive to the mesh quality. The use of 2D meshes leads to overestimation of the clog growth because the 3D nature of flow in the boundary layer cannot be properly solved by 2D calculation. 3D simulation with tetrahedron mesh can also lead to an error estimation of the clog growth. A mesh-independent result can be achieved with hexahedral mesh, or at least with triangular prism (inflation layer) for near-wall regions.

Keywords: clogging, continuous casting, inclusion, simulation, submerged entry nozzle

Procedia PDF Downloads 275

Authors: A. Ejah Umraeni Salam, M. Tola, M. Selintung, F. Maricar

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Clean water is an essential and fundamental human need. Therefore, its supply must be assured by maintaining the quality, quantity and water pressure. However the fact is, on its distribution system, leakage happens and becomes a common world issue. One of the technical causes of the leakage is a leaking pipe. The purpose of the research is how to use the Radial Basis Function Neural (RBFNN) model to detect the location and the magnitude of the pipeline leakage rapidly and efficiently. In this study the RBFNN are trained and tested on data from EPANET hydraulic modeling system. Method of Radial Basis Function Neural Network is proved capable to detect location and magnitude of pipeline leakage with of the accuracy of the prediction results based on the value of RMSE (Root Meant Square Error), comparison prediction and actual measurement approaches 0.000049 for the whole pipeline system.

Keywords: radial basis function neural network, leakage pipeline, EPANET, RMSE

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Authors: Shubham Jaiswal

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During modeling of transport phenomena in porous media, many nonlinear partial differential equations (NPDEs) encountered which greatly described the convection, diffusion and reaction process. To solve such types of nonlinear problems, a reliable and efficient technique is needed. In this article, the numerical solution of NPDEs encountered in porous media is derived. Here Jacobi collocation method is used to solve the considered problems which convert the NPDEs in systems of nonlinear algebraic equations that can be solved using Newton-Raphson method. The numerical results of some illustrative examples are reported to show the efficiency and high accuracy of the proposed approach. The comparison of the numerical results with the existing analytical results already reported in the literature and the error analysis for each example exhibited through graphs and tables confirms the exponential convergence rate of the proposed method.

Keywords: nonlinear porous media equation, shifted Jacobi polynomials, operational matrix, spectral collocation method

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Authors: Shourav Ahmed, M. Gulam Kibria, Kais Zaman

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Control charts graphically verify variation in quality parameters. Attribute type control charts deal with quality parameters that can only hold two states, e.g., good or bad, yes or no, etc. At present, p-control chart is most commonly used to deal with attribute type data. In construction of p-control chart using binomial distribution, the value of proportion non-conforming must be known or estimated from limited sample information. As the probability distribution of fraction non-conforming (p) is considered in hyperbinomial distribution unlike a constant value in case of binomial distribution, it reduces the risk of false detection. In this study, a statistical control chart is proposed based on hyperbinomial distribution when prior estimate of proportion non-conforming is unavailable and is estimated from limited sample information. We developed the control limits of the proposed modified p-chart using the mean and variance of hyperbinomial distribution. The proposed modified p-chart can also utilize additional sample information when they are available. The study also validates the use of modified p-chart by comparing with the result obtained using cumulative distribution function of hyperbinomial distribution. The study clearly indicates that the use of hyperbinomial distribution in construction of p-control chart yields much accurate estimate of quality parameters than using binomial distribution.

Keywords: binomial distribution, control charts, cumulative distribution function, hyper binomial distribution

Procedia PDF Downloads 269

Authors: V. Ložar, N. Hadžić, T. Opetuk, R. Keser

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The development of digital twins strongly depends on efficient algorithms and their capability to mirror real-life processes. Nowadays, such efforts are required to establish factories of the future faced with new demands of custom-made production. The ship hull processes face these challenges too. Therefore, it is important to implement design and evaluation approaches based on production system engineering. In this study, the recently developed finite state method is employed to describe the stell hull process as a platform for the implementation of digital twinning technology. The application is justified by comparing the finite state method with the analytical approach. This method is employed to rebuild a model of a real shipyard ship hull process using a combination of serial and splitting lines. The key performance indicators such as the production rate, work in process, probability of starvation, and blockade are calculated and compared to the corresponding results obtained through a simulation approach using the software tool Enterprise dynamics. This study confirms that the finite state method is a suitable tool for digital twinning applications. The conclusion highlights the advantages and disadvantages of methods employed in this context.

Keywords: digital twin, finite state method, production system engineering, shipyard

Procedia PDF Downloads 89

Authors: Marco Avila Lopez, Hasnae Ait-Douchi, Silvia De Los Santos, Badr Eddine Lebrouhi, Pamela Ramírez Vidal

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In the context of the energy transition, fuel cell technology has emerged as a solution for harnessing hydrogen energy and mitigating greenhouse gas emissions. An in-depth study was conducted on a PEMFC-type fuel cell, with an initiation of an analysis of its operational principles and constituent components. Subsequently, the modelling of the fuel cell was undertaken using the Python programming language, encompassing both steady-state and transient regimes. In the case of the steady-state regime, the physical and electrochemical phenomena occurring within the fuel cell were modelled, with the assumption of uniform temperature throughout all cell compartments. Parametric identification was carried out, resulting in a remarkable mean error of only 1.62% when the model results were compared to experimental data documented in the literature. The dynamic model that was developed enabled the scrutiny of the fuel cell's response in terms of temperature and voltage under varying current conditions.

Keywords: fuel cell, modelling, dynamic, thermal model, PEMFC

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Authors: Clinton Chidiebere Anyanwu

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Secondary education is a vital part of a virtuous circle of economic growth within the context of a globalised knowledge economy. The teaching of Business Education entails teaching learners the essentials, rudiments, assumptions, and methods of business. Hence, it was deemed necessary for the study to investigate technology integration in Business Education. Drawing from the theoretical frameworks of technological pedagogical content knowledge (TPACK), and unified theory of acceptance and use of technology (UTAUT), the study observes teachers’ level of technology use in Business Education classrooms. Using a mixed-methods sequential explanatory design, probability, and purposive sampling, the majority of participants were found to be not integrating technology to an acceptable level and a small percentage was. After an analysis of constructs from UTAUT, some of this could be attributed to the lack of facilitating conditions in the teaching and learning of Business Education. The implication of the study findings is that poor investment in technology integration in secondary schools in Nigeria affects pedagogical implementations and effective teaching and learning of Business Education subjects. The study concludes that if facilitating conditions and professional development are considered to address the shortfalls in terms of TPACK, technology integration will become a reality in secondary schools in Nigeria.

Keywords: business education, secondary education, technology integration, TPACK, UTAUT

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Authors: N. Ploysook, R. Rugsaj, C. Suvanjumrat

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The characteristic requirement for producing rectangular shape bottles was a uniform thickness of the plastic bottle wall. Die shaping was a good technique which controlled the wall thickness of bottles. An advance technology which was the finite element method (FEM) for blowing parison to be a rectangular shape bottle was conducted to reduce waste plastic from a trial and error method of a die shaping and parison control method. The artificial intelligent (AI) comprised of artificial neural network and genetic algorithm was selected to optimize the die gap shape from the FEM results. The application of AI technique could optimize the suitable die gap shape for the parison blow molding which did not depend on the parison control method to produce rectangular bottles with the uniform wall. Particularly, this application can be used with cheap blow molding machines without a parison controller therefore it will reduce cost of production in the bottle blow molding process.

Keywords: AI, bottle, die shaping, FEM

Procedia PDF Downloads 232

Authors: Rana I. Abdulghani, Amera I. Melhum

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With the abnormal growth of the usage of computers over networks and under the consideration or agreement of most of the computer security experts who said that the goal of building a secure system is never achieved effectively, all these points led to the design of the intrusion detection systems(IDS). This research adopts a comparison between two techniques for network intrusion detection, The first one used the (Particles Swarm Optimization) that fall within the field (Swarm Intelligence). In this Act, the algorithm Enhanced for the purpose of obtaining the minimum error rate by amending the cluster centers when better fitness function is found through the training stages. Results show that this modification gives more efficient exploration of the original algorithm. The second algorithm used a (Back propagation NN) algorithm. Finally a comparison between the results of two methods used were based on (NSL_KDD) data sets for the construction and evaluation of intrusion detection systems. This research is only interested in clustering the two categories (Normal and Abnormal) for the given connection records. Practices experiments result in intrude detection rate (99.183818%) for EPSO and intrude detection rate (69.446416%) for BP neural network.

Keywords: IDS, SI, BP, NSL_KDD, PSO

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Authors: Annie Gedion, Johan Severinus Tati, Jacinta Caroline Peter

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Syntax error analysis studies have a significant role in English language teaching especially in the second language. This study investigates the syntax errors in written composition by 50 multilingual ESL learners in Politeknik Kota Kinabalu Sabah, Malaysia. The subjects speak their own dialect, Malay as their second language and English as their third or foreign language. Data were collected from the written discourse in the form of descriptive essays. The subjects were asked to write in the classroom within 45 minutes. 15 categories of errors were classified into a set of syntactic categories and were analysed based on the five steps of the syntactic analysis procedure. The findings of the study showed that the mother tongue interference, as well as lack of vocabulary and grammar knowledge, were the major sources of syntax errors in the learners’ written composition. Learners should be exposed to the differentiation of Malay and English grammar to avoid interference and effective learning of second language writing.

Keywords: errors analysis, syntactic analysis, English as a second language, ESL writing

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Authors: Sabrina Azzi, Michal Iglewski, Véronique Nabelsi

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Diagnosis error problem is frequent and one of the most important safety problems today. One of the main objectives of our work is to propose an ontological representation that takes into account the diagnostic criteria in order to improve the diagnostic. We choose pneumonia disease since it is one of the frequent diseases affected by diagnosis errors and have harmful effects on patients. To achieve our aim, we use a semi-automated method to integrate diverse knowledge sources that include publically available pneumonia disease guidelines from international repositories, biomedical ontologies and electronic health records. We follow the principles of the Open Biomedical Ontologies (OBO) Foundry. The resulting ontology covers symptoms and signs, all the types of pneumonia, antecedents, pathogens, and diagnostic testing. The first evaluation results show that most of the terms are covered by the ontology. This work is still in progress and represents a first and major step toward a development of a diagnosis decision support system for pneumonia.

Keywords: Clinical decision support system, Diagnostic errors, Ontology, Pneumonia

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Authors: Minho Kwon, Jeonghee Lim, Yeongseok Jeong, Donghoon Shin, Kiyoung Kim

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Recently, global warming phenomenon has led to natural disasters caused by global environmental changes, and due to abnormal weather events, the frequency and intensity of heavy rain storm typhoons are increasing. Therefore, it is imperative to prepare for future heavy rain storms and typhoons. This study selects arbitrary target bridges and performs numerical analysis to evaluate the safety of bridge piers in the event that the water level changes. The numerical model is based on two-dimensional surface elements. Actual reinforced concrete was simulated by modeling concrete to include reinforcements, and a contact boundary model was applied between the ground and the concrete. The water level applied to the piers was considered at 18 levels between 7.5 m and 16.1 m. The elastic modulus, compressive strength, tensile strength, and yield strength of the reinforced concrete were calculated using 250 random combinations and numerical analysis was carried out for each water level. In the results of analysis, the bridge exceeded the stated limit at 15.0 m. At the maximum water level of 16.1m, the concrete’s failure rate was 35.2%, but the probability that the reinforcement would fail was 61.2%.

Keywords: Monte-Carlo method, pier, water level change, limit state

Procedia PDF Downloads 281

Authors: Justin Reverdi, Sixin Zhang, Serge Gratton, Said Aoues, Thomas Pellegrini

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In Vapor Cycle Systems, the flow sensor plays a key role in different monitoring and control purposes. However, physical sensors can be expensive, inaccurate, heavy, cumbersome, or highly sensitive to vibrations, which is especially problematic when embedded into an aircraft. The conception of a virtual sensor based on other standard sensors is a good alternative. In this paper, a data-driven model using a Convolutional Neural Network is proposed to estimate the flow of the compressor. To fit the model to our dataset, we tested different loss functions. We show in our application that a Dynamic Time Warping based loss function called DILATE leads to better dynamical performance than the vanilla mean squared error (MSE) loss function. DILATE allows choosing a trade-off between static and dynamic performance.

Keywords: deep learning, dynamic time warping, vapor cycle system, virtual sensor

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Authors: Umit Duru

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The Soil and Water Assessment Tool (SWAT) was tested for prediction of water balance and sediment yield in the Ankara gauged basin, Turkey. The overall objective of this study was to evaluate the performance and applicability of the SWAT in this region of Turkey. Thirteen years of monthly stream flow, and suspended sediment, data were used for calibration and validation. This research assessed model performance based on differences between observed and predicted suspended sediment yield during calibration (1987-1996) and validation (1982-1984) periods. Statistical comparisons of suspended sediment produced values for NSE (Nash Sutcliffe efficiency), RE (relative error), and R² (coefficient of determination), of 0.81, -1.55, and 0.93, respectively, during the calibration period, and NSE, RE (%), and R² of 0.77, -2.61, and 0.87, respectively, during the validation period. Based on the analyses, SWAT satisfactorily simulated observed hydrology and sediment yields and can be used as a tool in decision making for water resources planning and management in the basin.

Keywords: calibration, GIS, sediment yield, SWAT, validation

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Authors: Alyaa M. Younes, Nermine Harraz, Mohammad H. Elwany

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Lithium ion batteries are currently used for many applications including satellites, electric vehicles and mobile electronics. Their ability to store relatively large amount of energy in a limited space make them most appropriate for critical applications. Evaluation of the life of these batteries and their reliability becomes crucial to the systems they support. Reliability of Li-Ion batteries has been mainly considered based on its lifetime. However, another important factor that can be considered critical in many applications such as in electric vehicles is the cycle duration. The present work presents the results of an experimental investigation on the degradation behavior of a Laptop Li-ion battery (type TKV2V) and the effect of applied load on the battery cycle time. The reliability was evaluated using an accelerated life test. Least squares linear regression with median rank estimation was used to estimate the Weibull distribution parameters needed for the reliability functions estimation. The probability density function, failure rate and reliability function under each of the applied loads were evaluated and compared. An inverse power model is introduced that can predict cycle time at any stress level given.

Keywords: accelerated life test, inverse power law, lithium-ion battery, reliability evaluation, Weibull distribution

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Authors: The Danh Phan

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House price forecasting is a main topic in the real estate market research. Effective house price prediction models could not only allow home buyers and real estate agents to make better data-driven decisions but may also be beneficial for the property policymaking process. This study investigates the housing market by using machine learning techniques to analyze real historical house sale transactions in Australia. It seeks useful models which could be deployed as an application for house buyers and sellers. Data analytics show a high discrepancy between the house price in the most expensive suburbs and the most affordable suburbs in the city of Melbourne. In addition, experiments demonstrate that the combination of Stepwise and Support Vector Machine (SVM), based on the Mean Squared Error (MSE) measurement, consistently outperforms other models in terms of prediction accuracy.

Keywords: house price prediction, regression trees, neural network, support vector machine, stepwise

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Authors: Elizaveta S. Prokofyeva, Svetlana V. Maltseva, Roman D. Zaitsev

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Rapidly evolving modern data analysis technologies in healthcare play a large role in understanding the operation of the system and its characteristics. Nowadays, one of the key tasks in urban healthcare is to optimize the resource allocation. Thus, the application of data analysis in medical institutions to solve optimization problems determines the significance of this study. The purpose of this research was to establish the dependence between the indicators of the effectiveness of the medical institution and its resources. Hospital discharges by diagnosis; hospital days of in-patients and in-patient average length of stay were selected as the performance indicators and the demand of the medical facility. The hospital beds by type of care, medical technology (magnetic resonance tomography, gamma cameras, angiographic complexes and lithotripters) and physicians characterized the resource provision of medical institutions for the developed models. The data source for the research was an open database of the statistical service Eurostat. The choice of the source is due to the fact that the databases contain complete and open information necessary for research tasks in the field of public health. In addition, the statistical database has a user-friendly interface that allows you to quickly build analytical reports. The study provides information on 28 European for the period from 2007 to 2016. For all countries included in the study, with the most accurate and complete data for the period under review, predictive models were developed based on historical panel data. An attempt to improve the quality and the interpretation of the models was made by cluster analysis of the investigated set of countries. The main idea was to assess the similarity of the joint behavior of the variables throughout the time period under consideration to identify groups of similar countries and to construct the separate regression models for them. Therefore, the original time series were used as the objects of clustering. The hierarchical agglomerate algorithm k-medoids was used. The sampled objects were used as the centers of the clusters obtained, since determining the centroid when working with time series involves additional difficulties. The number of clusters used the silhouette coefficient. After the cluster analysis it was possible to significantly improve the predictive power of the models: for example, in the one of the clusters, MAPE error was only 0,82%, which makes it possible to conclude that this forecast is highly reliable in the short term. The obtained predicted values of the developed models have a relatively low level of error and can be used to make decisions on the resource provision of the hospital by medical personnel. The research displays the strong dependencies between the demand for the medical services and the modern medical equipment variable, which highlights the importance of the technological component for the successful development of the medical facility. Currently, data analysis has a huge potential, which allows to significantly improving health services. Medical institutions that are the first to introduce these technologies will certainly have a competitive advantage.

Keywords: data analysis, demand modeling, healthcare, medical facilities

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Authors: Muluegziabher Semagne Mekonnen

Abstract:

This study was intended to show the irrigation water requirements and evaluation of canal hydraulics steady state conditions to improve on scheme performance of the Meki-Ziway irrigation project. The methodology used was the CROPWAT 8.0 model to estimate the irrigation water requirements of five major crops irrigated in the study area. The results showed that for the whole existing and potential irrigation development area of 2000 ha and 2599 ha, crop water requirements were 3,339,200 and 4,339,090.4 m³, respectively. Hydraulic simulation models are fundamental tools for understanding the hydraulic flow characteristics of irrigation systems. Hydraulic simulation models are fundamental tools for understanding the hydraulic flow characteristics of irrigation systems. In this study Hydraulic Analysis of Irrigation Canals Using HEC-RAS Model was conducted in Meki-Ziway Irrigation Scheme. The HEC-RAS model was tested in terms of error estimation and used to determine canal capacity potential.

Keywords: HEC-RAS, irrigation, hydraulic. canal reach, capacity

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Authors: Mohammad Irfan

Abstract:

The purpose of this paper is to investigate the relationship among the key macroeconomic variables and Islamic stock market in India. This study is based on the time series data of financial years 2009-2015 to explore the consistency of relationship between macroeconomic variables and Shariah Indices. The ADF (Augmented Dickey–Fuller Test Statistic) and PP (Phillips–Perron Test Statistic) tests are employed to check stationarity of the data. The study depicts the long run relationship between Shariah indices and macroeconomic variables by using the Johansen Co-integration test. BSE Shariah and Nifty Shariah have uni-direct Granger causality. The outcome of VECM is significantly confirming the applicability of best fitted model. Thus, Islamic stock indices are proficiently working for the development of Indian economy. It suggests that by keeping eyes on Islamic stock market which will be more interactive in the future with other macroeconomic variables.

Keywords: Indian Shariah Indices, macroeconomic variables, co-integration, Granger causality, vector error correction model (VECM)

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