Search results for: performance prediction.

Authors: Mahdi Yaghobi, Mohammad Haeri

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The current methods of predictive controllers are utilized for those processes in which the rate of output variations is not high. For such processes, therefore, stability can be achieved by implementing the constrained predictive controller or applying infinite prediction horizon. When the rate of the output growth is high (e.g. for unstable nonminimum phase process) the stabilization seems to be problematic. In order to avoid this, it is suggested to change the method in the way that: first, the prediction error growth should be decreased at the early stage of the prediction horizon, and second, the rate of the error variation should be penalized. The growth of the error is decreased through adjusting its weighting coefficients in the cost function. Reduction in the error variation is possible by adding the first order derivate of the error into the cost function. By studying different examples it is shown that using these two remedies together, the closed-loop stability of unstable nonminimum phase process can be achieved.

Keywords: GPC, Stability, Varying Weighting Coefficients.

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Authors: Ammar Mahjoubi, Ridha Fethi Mechlouch, Belgacem Mahdhaoui, Ammar Ben Brahim

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This purpose of this paper is to develop and validate a model to accurately predict the cell temperature of a PV module that adapts to various mounting configurations, mounting locations, and climates while only requiring readily available data from the module manufacturer. Results from this model are also compared to results from published cell temperature models. The models were used to predict real-time performance from a PV water pumping systems in the desert of Medenine, south of Tunisia using 60-min intervals of measured performance data during one complete year. Statistical analysis of the predicted results and measured data highlight possible sources of errors and the limitations and/or adequacy of existing models, to describe the temperature and efficiency of PV-cells and consequently, the accuracy of performance of PV water pumping systems prediction models.

Keywords: Temperature of a photovoltaic module, Predicted models, PV water pumping systems efficiency, Simulation, Desert of southern Tunisia.

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Authors: Yu Zhao, Min Zhang, Yuanqian Wang, Qiuyu Yu

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The prediction of aesthetics of apparel is helpful for the development of a new type of apparel. This study is to build the quantitative relationship between the aesthetics and its design parameters. In particular, women’s pants have been preliminarily studied. This aforementioned relationship has been carried out by statistical analysis. The contributions of this study include the development of a more personalized apparel design mechanism and the provision of some empirical knowledge for the development of other products in the aspect of aesthetics.

Keywords: Aesthetics, crease line, cropped straight leg pants, knee width.

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Authors: V. G. Rifert, V. V. Gorin, V. V. Sereda, V. V. Treputnev

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The analysis of heat transfer design methods in condensing inside plain tubes under existing influence of shear stress is presented in this paper. The existing discrepancy in more than 30-50% between rating heat transfer coefficients and experimental data has been noted. The analysis of existing theoretical and semi-empirical methods of heat transfer prediction is given. The influence of a precise definition concerning boundaries of phase flow (it is especially important in condensing inside horizontal tubes), shear stress (friction coefficient) and heat flux on design of heat transfer is shown. The substantiation of boundary conditions of the values of parameters, influencing accuracy of rated relationships, is given. More correct relationships for heat transfer prediction, which showed good convergence with experiments made by different authors, are substantiated in this work.

Keywords: Film condensation, heat transfer, plain tube, shear stress.

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Authors: Kamaldeep Kaur, Arvinder Kaur, Ruchika Malhotra

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The aim of this paper is to rank the impact of Object Oriented(OO) metrics in fault prediction modeling using Artificial Neural Networks(ANNs). Past studies on empirical validation of object oriented metrics as fault predictors using ANNs have focused on the predictive quality of neural networks versus standard statistical techniques. In this empirical study we turn our attention to the capability of ANNs in ranking the impact of these explanatory metrics on fault proneness. In ANNs data analysis approach, there is no clear method of ranking the impact of individual metrics. Five ANN based techniques are studied which rank object oriented metrics in predicting fault proneness of classes. These techniques are i) overall connection weights method ii) Garson-s method iii) The partial derivatives methods iv) The Input Perturb method v) the classical stepwise methods. We develop and evaluate different prediction models based on the ranking of the metrics by the individual techniques. The models based on overall connection weights and partial derivatives methods have been found to be most accurate.

Keywords: Artificial Neural Networks (ANNS), Backpropagation, Fault Prediction Modeling.

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Authors: M.O. Makhmalbaf, F. Mohajeri Nav, M. Zabihi Samani

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The recent drive for use of performance-based methodologies in design and assessment of structures in seismic areas has significantly increased the demand for the development of reliable nonlinear inelastic static pushover analysis tools. As a result, the adaptive pushover methods have been developed during the last decade, which unlike their conventional pushover counterparts, feature the ability to account for the effect that higher modes of vibration and progressive stiffness degradation might have on the distribution of seismic storey forces. Even in advanced pushover methods, little attention has been paid to the Unsymmetric structures. This study evaluates the seismic demands for three dimensional Unsymmetric-Plan buildings determined by the Displacement-based Adaptive Pushover (DAP) analysis, which has been introduced by Antoniou and Pinho [2004]. The capability of DAP procedure in capturing the torsional effects due to the irregularities of the structures, is investigated by comparing its estimates to the exact results, obtained from Incremental Dynamic Analysis (IDA). Also the capability of the procedure in prediction the seismic behaviour of the structure is discussed.

Keywords: Nonlinear static procedures, Unsymmetric-PlanBuildings, Torsional effects, IDA.

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Authors: T.A. Adegbola, IEA Aghachi, E.R. Sadiku

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To investigates the effect of fiberglass clamping process improvement on drape simulation prediction. This has great effect on the mould and the fiber during manufacturing process. This also, improves the fiber strain, the quality of the fiber orientation in the area of folding and wrinkles formation during the press-forming process. Drape simulation software tool was used to digitalize the process, noting the formation problems on the contour sensitive part. This was compared with the real life clamping processes using single and double frame set-ups to observe the effects. Also, restrains are introduced by using clips, and the G-clamps with predetermine revolution to; restrain the fabric deformation during the forming process.The incorporation of clamping and fabric restrain deformation improved on the prediction of the simulation tool. Therefore, for effective forming process, incorporation of clamping process into the drape simulation process will assist in the development of fiberglass application in manufacturing process.

Keywords: clamping, fiberglass, drape simulation, pressforming.

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Authors: Sulata Maharana, B Biswas, Adity Ganguly, Ashok Kumar

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In this paper, the requirement for Coke quality prediction, its role in Blast furnaces, and the model output is explained. By applying method of Artificial Neural Networking (ANN) using back propagation (BP) algorithm, prediction model has been developed to predict CSR. Important blast furnace functions such as permeability, heat exchanging, melting, and reducing capacity are mostly connected to coke quality. Coke quality is further dependent upon coal characterization and coke making process parameters. The ANN model developed is a useful tool for process experts to adjust the control parameters in case of coke quality deviations. The model also makes it possible to predict CSR for new coal blends which are yet to be used in Coke Plant. Input data to the model was structured into 3 modules, for tenure of past 2 years and the incremental models thus developed assists in identifying the group causing the deviation of CSR.

Keywords: Artificial Neural Networks, backpropagation, CokeStrength after Reaction, Multilayer Perceptron.

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Authors: Prasit Wonglersak, Prakarnkiat Youngkong, Ittipon Cheowanish

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This paper aims to improve a fine lapping process of hard disk drive (HDD) lapping machines by removing materials from each slider together with controlling the strip height (SH) variation to minimum value. The standard deviation is the key parameter to evaluate the strip height variation, hence it is minimized. In this paper, a design of experiment (DOE) with factorial analysis by twoway analysis of variance (ANOVA) is adopted to obtain a statistically information. The statistics results reveal that initial stripe height patterns affect the final SH variation. Therefore, initial SH classification using a radial basis function neural network is implemented to achieve the proportional gain prediction.

Keywords: Stripe height variation, Two-way analysis ofvariance (ANOVA), Radial basis function neural network, Proportional gain prediction.

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Authors: E. Muzenda

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Group contribution methods such as the UNIFAC are of major interest to researchers and engineers involved synthesis, feasibility studies, design and optimization of separation processes as well as other applications of industrial use. Reliable knowledge of the phase equilibrium behavior is crucial for the prediction of the fate of the chemical in the environment and other applications. The objective of this study was to predict the solubility of selected volatile organic compounds (VOCs) in glycol polymers and biodiesel. Measurements can be expensive and time consuming, hence the need for thermodynamic models. The results obtained in this study for the infinite dilution activity coefficients compare very well those published in literature obtained through measurements. It is suggested that in preliminary design or feasibility studies of absorption systems for the abatement of volatile organic compounds, prediction procedures should be implemented while accurate fluid phase equilibrium data should be obtained from experiment.

Keywords: Volatile organic compounds, Prediction, Phaseequilibrium, Environmental, Infinite dilution.

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Authors: J. Menčík, B. Culek, Jr., L. Beran, J. Mareš

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Large metal and concrete structures suffer by various kinds of deterioration, and accurate prediction of the remaining life is important. This paper informs about two methods for its assessment. One method, suitable for steel bridges and other constructions exposed to fatigue, monitors the loads and damage accumulation using information systems for the operation and the finite element model of the construction. In addition to the operation load, the dead weight of the construction and thermal stresses can be included into the model. The second method is suitable for concrete bridges and other structures, which suffer by carbonatation and other degradation processes, driven by diffusion. The diffusion constant, important for the prediction of future development, can be determined from the depth-profile of pH, obtained by pH measurement at various depths. Comparison with measurements on real objects illustrates the suitability of both methods.

Keywords: Bridges, carbonatation, concrete, diagnostics, fatigue, life prediction, monitoring, railway, simulation, structures.

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Authors: Rehab Abdulmajed, Amr Hamada, Ahmed Elsaid, Hisashi Hayakawa, Ayman Mahrous

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Solar emissions have a high impact on the Earth’s magnetic field, and the prediction of solar events is of high interest. Various techniques have been used in the prediction of the solar wind using mathematical models, MHD models and neural network (NN) models. This study investigates the coronal hole (CH) derived high-speed streams (HSSs) and their correlation to the CH area and create a neural network model to predict the HSSs. Two different algorithms were used to compare different models to find a model that best simulated the HSSs. A dataset of CH synoptic maps through Carrington rotations 1601 to 2185 along with Omni-data set solar wind speed averaged over the Carrington rotations is used, which covers Solar Cycles (SC) 21, 22, 23, and most of 24.

Keywords: Artificial Neural Network, ANN, Coronal Hole Area Feed-Forward neural network models, solar High-Speed Streams, HSSs.

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Authors: Yong Zhao, Jian He, Cheng Zhang

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Cardiovascular disease resulting from hypertension poses a significant threat to human health, and early detection of hypertension can potentially save numerous lives. Traditional methods for detecting hypertension require specialized equipment and are often incapable of capturing continuous blood pressure fluctuations. To address this issue, this study starts by analyzing the principle of heart rate variability (HRV) and introduces the utilization of sliding window and power spectral density (PSD) techniques to analyze both temporal and frequency domain features of HRV. Subsequently, a hypertension prediction network that relies on HRV is proposed, combining Resnet, attention mechanisms, and a multi-layer perceptron. The network leverages a modified ResNet18 to extract frequency domain features, while employing an attention mechanism to integrate temporal domain features, thus enabling auxiliary hypertension prediction through the multi-layer perceptron. The proposed network is trained and tested using the publicly available SHAREE dataset from PhysioNet. The results demonstrate that the network achieves a high prediction accuracy of 92.06% for hypertension, surpassing traditional models such as K Near Neighbor (KNN), Bayes, Logistic regression, and traditional Convolutional Neural Network (CNN).

Keywords: Feature extraction, heart rate variability, hypertension, residual networks.

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Authors: P. Bhramara, V. D. Rao, K. V. Sharma , T. K. K. Reddy

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In designing of condensers, the prediction of pressure drop is as important as the prediction of heat transfer coefficient. Modeling of two phase flow, particularly liquid – vapor flow under diabatic conditions inside a horizontal tube using CFD analysis is difficult with the available two phase models in FLUENT due to continuously changing flow patterns. In the present analysis, CFD analysis of two phase flow of refrigerants inside a horizontal tube of inner diameter, 0.0085 m and 1.2 m length is carried out using homogeneous model under adiabatic conditions. The refrigerants considered are R22, R134a and R407C. The analysis is performed at different saturation temperatures and at different flow rates to evaluate the local frictional pressure drop. Using Homogeneous model, average properties are obtained for each of the refrigerants that is considered as single phase pseudo fluid. The so obtained pressure drop data is compared with the separated flow models available in literature.

Keywords: Adiabatic conditions, CFD analysis, Homogeneousmodel and Liquid – Vapor flow.

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Authors: D. S. C. Soares, D. G. Costa, J. T. S., A. K. S. Abud, T. P. Nunes, A. M. Oliveira Júnior

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Mathematical models of drying are used for the purpose of understanding the drying process in order to determine important parameters for design and operation of the dryer. The jackfruit is a fruit with high consumption in the Northeast and perishability. It is necessary to apply techniques to improve their conservation for longer in order to diffuse it by regions with low consumption. This study aimed to analyze several mathematical models (Page, Lewis, and Midilli) to indicate one that best fits the conditions of convective drying process using performance indicators associated with each model: accuracy (Af) and noise factors (Bf), mean square error (RMSE) and standard error of prediction (% SEP). Jackfruit drying was carried out in convective type tray dryer at a temperature of 50°C for 9 hours. It is observed that the model Midili was more accurate with Af: 1.39, Bf: 1.33, RMSE: 0.01%, and SEP: 5.34. However, the use of the Model Midilli is not appropriate for purposes of control process due to need four tuning parameters. With the performance indicators used in this paper, the Page model showed similar results with only two parameters. It is concluded that the best correlation between the experimental and estimated data is given by the Page’s model.

Keywords: Drying, models, jackfruit.

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Authors: Xiangtuo Chen, Paul-Henry Cournéde

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Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: Crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest.

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Authors: Chau-Kuang Chen, John Hughes, Jr., A. Dexter Samuels

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The prediction models for the United States Medical Licensure Examination (USMLE) Steps 1 and 2 performances were constructed by the Monte Carlo simulation modeling approach via linear regression. The purpose of this study was to build robust simulation models to accurately identify the most important predictors and yield the valid range estimations of the Steps 1 and 2 scores. The application of simulation modeling approach was deemed an effective way in predicting student performances on licensure examinations. Also, sensitivity analysis (a/k/a what-if analysis) in the simulation models was used to predict the magnitudes of Steps 1 and 2 affected by changes in the National Board of Medical Examiners (NBME) Basic Science Subject Board scores. In addition, the study results indicated that the Medical College Admission Test (MCAT) Verbal Reasoning score and Step 1 score were significant predictors of the Step 2 performance. Hence, institutions could screen qualified student applicants for interviews and document the effectiveness of basic science education program based on the simulation results.

Keywords: Prediction Model, Sensitivity Analysis, Simulation Method, USMLE.

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Authors: Nuha Omran Abokhdair, Azizah Bt Abdul Manaf

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Reversible watermarking is a special branch of image watermarking, that is able to recover the original image after extracting the watermark from the image. In this paper, an adaptive prediction-based reversible watermarking scheme is presented, in order to increase the payload capacity of MRI medical images. The scheme divides the image into two parts, Region of Interest (ROI) and Region of Non-Interest (RONI). Two bits are embedded in each embeddable pixel of RONI and one bit is embedded in each embeddable pixel of ROI. The experimental results demonstrate that the proposed scheme is able to achieve high embedding capacity. This is mainly caused by two reasons. First, the pixels that were excluded from data embedding due to overflow/underflow are used for data embedding. Second, large location map that need to be added to watermark data as overhead is eliminated and thus lower data embedding capacity is prevented. Moreover, the scheme provides good visual quality to the watermarked image.

Keywords: Medical image watermarking, reversible watermarking, Difference Expansion, Prediction-Error Expansion.

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Authors: Y. Galerkin, K. Soldatova, A. Drozdov

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The 6th version of Universal modeling method for centrifugal compressor stage calculation is described. Identification of the new mathematical model was made. As a result of identification the uniform set of empirical coefficients is received. The efficiency definition error is 0,86 % at a design point. The efficiency definition error at five flow rate points (except a point of the maximum flow rate) is 1,22 %. Several variants of the stage with 3D impellers designed by 6th version program and quasi threedimensional calculation programs were compared by their gas dynamic performances CFD (NUMECA FINE TURBO). Performance comparison demonstrated general principles of design validity and leads to some design recommendations.

Keywords: Compressor design, loss model, performance prediction, test data, model stages, flow rate coefficient, work coefficient.

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Authors: Arunesh Chandra, Pankaj Chandna, Surinder Deswal, Rajesh Kumar Mishra, Rajender Kumar

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The arm length, hand length, hand breadth and middle finger length of 1540 right-handed industrial workers of Haryana state was used to assess the relationship between the upper limb dimensions and stature. Initially, the data were analyzed using basic univariate analysis and independent t-tests; then simple and multiple linear regression models were used to estimate stature using SPSS (version 17). There was a positive correlation between upper limb measurements (hand length, hand breadth, arm length and middle finger length) and stature (p < 0.01), which was highest for hand length. The accuracy of stature prediction ranged from ± 54.897 mm to ± 58.307 mm. The use of multiple regression equations gave better results than simple regression equations. This study provides new forensic standards for stature estimation from the upper limb measurements of male industrial workers of Haryana (India). The results of this research indicate that stature can be determined using hand dimensions with accuracy, when only upper limb is available due to any reasons likewise explosions, train/plane crashes, mutilated bodies, etc. The regression formula derived in this study will be useful for anatomists, archaeologists, anthropologists, design engineers and forensic scientists for fairly prediction of stature using regression equations.

Keywords: Anthropometric dimensions, Forensic identification, Industrial workers, Stature prediction.

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Authors: Ichihan Tai, Bill Olson, Paul Blessner

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Traditional early warning systems that alarm against crisis are generally based on structured or numerical data; therefore, a system that can make predictions based on unstructured textual data, an uncorrelated data source, is a great complement to the traditional early warning systems. The Chicago Board Options Exchange (CBOE) Volatility Index (VIX), commonly referred to as the fear index, measures the cost of insurance against market crash, and spikes in the event of crisis. In this study, news data is consumed for prediction of whether there will be a market-wide crisis by predicting the movement of the fear index, and the historical references to similar events are presented in an unsupervised manner. Topic modeling-based prediction and representation are made based on daily news data between 1990 and 2015 from The Wall Street Journal against VIX index data from CBOE.

Keywords: Early Warning System, Knowledge Management, Topic Modeling, Market Prediction.

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Authors: Vipul M. Patel, Hemantkumar B. Mehta

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Technological innovations in electronic world demand novel, compact, simple in design, less costly and effective heat transfer devices. Closed Loop Pulsating Heat Pipe (CLPHP) is a passive phase change heat transfer device and has potential to transfer heat quickly and efficiently from source to sink. Thermal performance of a CLPHP is governed by various parameters such as number of U-turns, orientations, input heat, working fluids and filling ratio. The present paper is an attempt to predict the thermal performance of a CLPHP using Artificial Neural Network (ANN). Filling ratio and heat input are considered as input parameters while thermal resistance is set as target parameter. Types of neural networks considered in the present paper are radial basis, generalized regression, linear layer, cascade forward back propagation, feed forward back propagation; feed forward distributed time delay, layer recurrent and Elman back propagation. Linear, logistic sigmoid, tangent sigmoid and Radial Basis Gaussian Function are used as transfer functions. Prediction accuracy is measured based on the experimental data reported by the researchers in open literature as a function of Mean Absolute Relative Deviation (MARD). The prediction of a generalized regression ANN model with spread constant of 4.8 is found in agreement with the experimental data for MARD in the range of ±1.81%.

Keywords: ANN models, CLPHP, filling ratio, generalized regression, spread constant.

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Authors: Siham G. Farag

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The main objective of this paper is to provide a new methodology for road safety assessment in Oman through the development of suitable accident prediction models. GLM technique with Poisson or NBR using SAS package was carried out to develop these models. The paper utilized the accidents data of 31 un-signalized T-intersections during three years. Five goodness-of-fit measures were used to assess the overall quality of the developed models. Two types of models were developed separately; the flow-based models including only traffic exposure functions, and the full models containing both exposure functions and other significant geometry and traffic variables. The results show that, traffic exposure functions produced much better fit to the accident data. The most effective geometric variables were major-road mean speed, minor-road 85th percentile speed, major-road lane width, distance to the nearest junction, and right-turn curb radius. The developed models can be used for intersection treatment or upgrading and specify the appropriate design parameters of T-intersections. Finally, the models presented in this thesis reflect the intersection conditions in Oman and could represent the typical conditions in several countries in the middle east area, especially gulf countries.

Keywords: Accidents Prediction Models (APMs), Generalized Linear Model (GLM), T-intersections, Oman.

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Authors: Bao Thach Nguyen, Abbas Mohajerani

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The California Bearing Ratio (CBR) has been acknowledged as an important parameter to characterize the bearing capacity of earth structures, such as earth dams, road embankments, airport runways, bridge abutments and pavements. Technically, the CBR test can be carried out in the laboratory or in the field. The CBR test is time-consuming and is infrequently performed due to the equipment needed and the fact that the field moisture content keeps changing over time. Over the years, many correlations have been developed for the prediction of CBR by various researchers, including the dynamic cone penetrometer, undrained shear strength and Clegg impact hammer. This paper reports and discusses some of the results from a study on the prediction of CBR. In the current study, the CBR test was performed in the laboratory on some finegrained subgrade soils collected from various locations in Victoria. Based on the test results, a satisfactory empirical correlation was found between the CBR and the physical properties of the experimental soils.

Keywords: California bearing ratio, fine-grained soils, pavement, soil physical properties.

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Authors: Stefano Frassinelli, Alessandro Niccolai, Riccardo E. Zich

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Sport performance analysis is a technique that is becoming every year more important for athletes of every level. Many techniques have been developed to measure and analyse efficiently the performance of athletes in some sports, but in combat sports these techniques found in many times their limits, due to the high interaction between the two opponents during the competition. In this paper the problem will be framed. Moreover the physical performance measurement problem will be analysed and three different techniques to manage it will be presented. All the techniques have been used to analyse the performance of 22 high level Judo athletes.

Keywords: Sport performance, physical performance, judo, performance coefficients.

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Authors: Holger Keitel

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The prediction of long-term deformations of concrete and reinforced concrete structures has been a field of extensive research and several different creep models have been developed so far. Most of the models were developed for constant concrete stresses, thus, in case of varying stresses a specific superposition principle or time-integration, respectively, is necessary. Nowadays, when modeling concrete creep the engineering focus is rather on the application of sophisticated time-integration methods than choosing the more appropriate creep model. For this reason, this paper presents a method to quantify the uncertainties of creep prediction originating from the selection of creep models or from the time-integration methods. By adapting variance based global sensitivity analysis, a methodology is developed to quantify the influence of creep model selection or choice of time-integration method. Applying the developed method, general recommendations how to model creep behavior for varying stresses are given.

Keywords: Concrete creep models, time-integration methods, sensitivity analysis, prediction uncertainty.

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Authors: Helyane B. Borges, Julio Cesar Nievola

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Hierarchical classification is a problem with applications in many areas as protein function prediction where the dates are hierarchically structured. Therefore, it is necessary the development of algorithms able to induce hierarchical classification models. This paper presents experimenters using the algorithm for hierarchical classification called Multi-label Hierarchical Classification using a Competitive Neural Network (MHC-CNN). It was tested in ten datasets the Gene Ontology (GO) Cellular Component Domain. The results are compared with the Clus-HMC and Clus-HSC using the hF-Measure.

Keywords: Hierarchical Classification, Competitive Neural Network, Global Classifier.

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Authors: Mikhail Gritskevich, Sebastian Hohenstein

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The paper presents the results of a detailed assessment of several modern Reynolds Averaged Navier-Stokes (RANS) turbulence models for prediction of C3X vane film cooling at various injection regimes. Three models are considered, namely the Shear Stress Transport (SST) model, the modification of the SST model accounting for the streamlines curvature (SST-CC), and the Explicit Algebraic Reynolds Stress Model (EARSM). It is shown that all the considered models face with a problem in prediction of the adiabatic effectiveness in the vicinity of the cooling holes; however, accounting for the Reynolds stress anisotropy within the EARSM model noticeably increases the solution accuracy. On the other hand, further downstream all the models provide a reasonable agreement with the experimental data for the adiabatic effectiveness and among the considered models the most accurate results are obtained with the use EARMS.

Keywords: Discrete holes film cooling, Reynolds Averaged Navier-Stokes, Reynolds stress tensor anisotropy, turbulent heat transfer.

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Authors: Henok Hailemariam, Frank Wuttke

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Collapsible soils are weak soils that appear to be stable in their natural state, normally dry condition, but rapidly deform under saturation (wetting), thus generating large and unexpected settlements which often yield disastrous consequences for structures unwittingly built on such deposits. In this study, a prediction model for the relative subsidence of stressed collapsible soils based on dielectric permittivity measurement is presented. Unlike most existing methods for soil subsidence prediction, this model does not require moisture content as an input parameter, thus providing the opportunity to obtain accurate estimation of the relative subsidence of collapsible soils using dielectric measurement only. The prediction model is developed based on an existing relative subsidence prediction model (which is dependent on soil moisture condition) and an advanced theoretical frequency and temperature-dependent electromagnetic mixing equation (which effectively removes the moisture content dependence of the original relative subsidence prediction model). For large scale sub-surface soil exploration purposes, the spatial sub-surface soil dielectric data over wide areas and high depths of weak (collapsible) soil deposits can be obtained using non-destructive high frequency electromagnetic (HF-EM) measurement techniques such as ground penetrating radar (GPR). For laboratory or small scale in-situ measurements, techniques such as an open-ended coaxial line with widely applicable time domain reflectometry (TDR) or vector network analysers (VNAs) are usually employed to obtain the soil dielectric data. By using soil dielectric data obtained from small or large scale non-destructive HF-EM investigations, the new model can effectively predict the relative subsidence of weak soils without the need to extract samples for moisture content measurement. Some of the resulting benefits are the preservation of the undisturbed nature of the soil as well as a reduction in the investigation costs and analysis time in the identification of weak (problematic) soils. The accuracy of prediction of the presented model is assessed by conducting relative subsidence tests on a collapsible soil at various initial soil conditions and a good match between the model prediction and experimental results is obtained.

Keywords: Collapsible soil, relative subsidence, dielectric permittivity, moisture content.

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Authors: G. R. Rameshkumar, B. V. A Rao, K. P. Ramachandran

Abstract:

Misalignment and unbalance are the major concerns in rotating machinery. When the power supply to any rotating system is cutoff, the system begins to lose the momentum gained during sustained operation and finally comes to rest. The exact time period from when the power is cutoff until the rotor comes to rest is called Coast Down Time. The CDTs for different shaft cutoff speeds were recorded at various misalignment and unbalance conditions. The CDT reduction percentages were calculated for each fault and there is a specific correlation between the CDT reduction percentage and the severity of the fault. In this paper, radial basis network, a new generation of artificial neural networks, has been successfully incorporated for the prediction of CDT for misalignment and unbalance conditions. Radial basis network has been found to be successful in the prediction of CDT for mechanical faults in rotating machinery.

Keywords: Coast Down Time, Misalignment, Unbalance, Artificial Neural Networks, Radial Basis Network.

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