Search results for: multivariate responses prediction

Authors: Deepika Christopher, Garima Anand

Abstract:

To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. According to the data, the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition.

Keywords: attrition, retention, predictive modeling, customer segmentation, telecommunications

Procedia PDF Downloads 55

Authors: Housein Deli, Loui Al-Shrouf, Hammoud Al Joumaa, Mohieddine Jelali

Abstract:

When forming metallic materials, fluctuations in material properties, process conditions, and wear lead to deviations in the component geometry. Several hundred features sometimes need to be measured, especially in the case of functional and safety-relevant components. These can only be measured offline due to the large number of features and the accuracy requirements. The risk of producing components outside the tolerances is minimized but not eliminated by the statistical evaluation of process capability and control measurements. The inspection intervals are based on the acceptable risk and are at the expense of productivity but remain reactive and, in some cases, considerably delayed. Due to the considerable progress made in the field of condition monitoring and measurement technology, permanently installed sensor systems in combination with machine learning and artificial intelligence, in particular, offer the potential to independently derive forecasts for component geometry and thus eliminate the risk of defective products - actively and preventively. The reliability of forecasts depends on the quality, completeness, and timeliness of the data. Measuring all geometric characteristics is neither sensible nor technically possible. This paper, therefore, uses the example of car seat rail production to discuss the necessary first step of feature selection and reduction by correlation analysis, as otherwise, it would not be possible to forecast components in real-time and inline. Four different car seat rails with an average of 130 features were selected and measured using a coordinate measuring machine (CMM). The run of such measuring programs alone takes up to 20 minutes. In practice, this results in the risk of faulty production of at least 2000 components that have to be sorted or scrapped if the measurement results are negative. Over a period of 2 months, all measurement data (> 200 measurements/ variant) was collected and evaluated using correlation analysis. As part of this study, the number of characteristics to be measured for all 6 car seat rail variants was reduced by over 80%. Specifically, direct correlations for almost 100 characteristics were proven for an average of 125 characteristics for 4 different products. A further 10 features correlate via indirect relationships so that the number of features required for a prediction could be reduced to less than 20. A correlation factor >0.8 was assumed for all correlations.

Keywords: long-term SHM, condition monitoring, machine learning, correlation analysis, component prediction, wear prediction, regressions analysis

Procedia PDF Downloads 39

Authors: Xia Chen, Yulan Wang

Abstract:

purpose: To compare the accuracy of Haigis, SRK/T, T2, Holladay 1, Hoffer Q, Barrett Universal II, Emmetropia Verifying Optical (EVO) and Kane for intraocular lens power calculation in patients with axial length (AL) ≥ 28 mm. Methods: In this retrospective single-center study, 50 eyes of 41 patients with AL ≥ 28 mm that underwent uneventful cataract surgery were enrolled. The actual postoperative refractive results were compared to the predicted refraction calculated with different formulas (Haigis, SRK/T, T2, Holladay 1, Hoffer Q, Barrett Universal II, EVO and Kane). The mean absolute prediction errors (MAE) 1 month postoperatively were compared. Results: The MAE of different formulas were as follows: Haigis (0.509), SRK/T (0.705), T2 (0.999), Holladay 1 (0.714), Hoffer Q (0.583), Barrett Universal II (0.552), EVO (0.463) and Kane (0.441). No significant difference was found among the different formulas (P = .122). The Kane and EVO formulas achieved the lowest level of mean prediction error (PE) and median absolute error (MedAE) (p < 0.05). Conclusion: The Kane and EVO formulas had a better success rate than others in predicting IOL power in high myopic eyes with AL longer than 28 mm in this study.

Keywords: cataract, power calculation formulas, intraocular lens, long axial length

Procedia PDF Downloads 79

Authors: Aarif Rasheed, Joseph Bulbulia

Abstract:

Western democracies exhibit signs of distinctive anti-Muslim prejudice, but little is known about its causes and effects on Muslim minorities. Here, drawing on nine years of responses from a nationally representative longitudinal sample of New Zealanders (New Zealand Attitudes and Values Study, N > 31,000), we systematically investigate the demographic and ideological predictors of factors that predict both positive and negative change in Muslim attitudes. First, we find that that education, moderate and liberal political ideology, and positive views about religion predict greater Muslim acceptance. Second, we find a there though there is a general trend for increasing acceptance over nine years, we find evidence of increasing extremism at the margins. Third, focusing on the Muslim sub-sample and comparing it to other religious sub-groups, we find substantially higher reports of perceived anti-religious prejudice. Collectively, these results point to serious challenges to the health of New Zealand as a democracy where people can worship freely without discrimination. Finally, we find consistency in our responses with the reported experiences of victims of the Christchurch attacks, in terms of harassment, assault, slurs, and other hostile behaviour both before and after the attacks.

Keywords: democracy, longitudinal, Muslim, panel data, prejudice

Procedia PDF Downloads 141

Authors: Y. Khulief, S. Bashmal, S. Said, D. Al-Otaibi, K. Mansour

Abstract:

The prediction of flow rates at which the vibration-induced instability takes place in tubular heat exchangers due to cross-flow is of major importance to the performance and service life of such equipment. In this paper, the semi-analytical model for square tube arrays was extended and utilized to study the triangular tube patterns. A laboratory test rig with instrumented test section is used to measure the fluidelastic coefficients to be used for tuning the mathematical model. The test section can be made of any bundle pattern. In this study, two test sections were constructed for both the normal triangular and the rotated triangular tube arrays. The developed scheme is utilized in predicting the onset of flow-induced instability in the two triangular tube arrays. The results are compared to those obtained for two other bundle configurations. The results of the four different tube patterns are viewed in the light of TEMA predictions. The comparison demonstrated that TEMA guidelines are more conservative in all configurations considered

Keywords: fluid-structure interaction, cross-flow, heat exchangers,

Procedia PDF Downloads 275

Authors: Mohammed Saleh Rezk, Reza Kashani

Abstract:

Auxiliary damping has gained popularity in recent years, especially in structures such as mid- and high-rise buildings. Distributed damping systems (typically viscous and viscoelastic) or reactive damping systems (such as tuned mass dampers) are the two types of damping choices for such structures. Distributed VE dampers are normally configured as braces or damping panels, which are engaged through relatively small movements between the structural members when the structure sways under wind or earthquake loading. In addition to being used as stand-alone dampers in distributed damping applications, VE dampers can also be incorporated into the suspension element of tuned mass dampers (TMDs). In this study, analytical and numerical tools for modeling and design of multilayer viscoelastic damping devices to be used in dampening the vibration of large structures are developed. Considering the limitations of analytical models for the synthesis and analysis of realistic, large, multilayer VE dampers, the emphasis of the study has been on numerical modeling using the finite element method. To verify the finite element models, a two-layer VE damper using ½ inch synthetic viscoelastic urethane polymer was built, tested, and the measured parameters were compared with the numerically predicted ones. The numerical model prediction and experimentally evaluated damping and stiffness of the test VE damper were in very good agreement. The effectiveness of VE dampers in adding auxiliary damping to larger structures is numerically demonstrated by chevron bracing one such damper numerically into the model of a massive frame subject to an abrupt lateral load. A comparison of the responses of the frame to the aforementioned load, without and with the VE damper, clearly shows the efficacy of the damper in lowering the extent of frame vibration.

Keywords: viscoelastic, damper, distributed damping, tuned mass damper

Procedia PDF Downloads 104

Authors: M. R. Dahmardeh Ghaleno, M. Nohtani, S. Khaledi

Abstract:

Flood simulation and prediction is one of the most active research areas in surface water management. WetSpa is a distributed, continuous, and physical model with daily or hourly time step that explains precipitation, runoff, and evapotranspiration processes for both simple and complex contexts. This model uses a modified rational method for runoff calculation. In this model, runoff is routed along the flow path using Diffusion-Wave equation which depends on the slope, velocity, and flow route characteristics. Golestan Dam Basin is located in Golestan province in Iran and it is passing over coordinates 55° 16´ 50" to 56° 4´ 25" E and 37° 19´ 39" to 37° 49´ 28"N. The area of the catchment is about 224 km², and elevations in the catchment range from 414 to 2856 m at the outlet, with average slope of 29.78%. Results of the simulations show a good agreement between calculated and measured hydrographs at the outlet of the basin. Drawing upon Nash-Sutcliffe model efficiency coefficient for calibration periodic model estimated daily hydrographs and maximum flow rate with an accuracy up to 59% and 80.18%, respectively.

Keywords: watershed simulation, WetSpa, stream flow, flood prediction

Procedia PDF Downloads 240

Authors: Zhaoguo Liu, Cunsi Shen, Yin Lu

Abstract:

Growing evidence has shown that menthol has potent anticancer activity in various human cancers. However, its effect on skin cancer remains largely unknown. In the present study, we investigated the chemopreventive potential of menthol against 7, 12-dimethylbenz[a] anthracene(DMBA)/12-O-tetradecanoylphorbol 13-acetate (TPA)-induced skin tumorigenesis in ICR mice. Our results showed that menthol significantly inhibited TPA-induced inflammatory responses and pro-inflammatory cytokine release. We also found that menthol treatment significantly inhibited TPA-induced lipid peroxidation (LPO), mouse UDP-glucumno-syltransferase (UGT), mouse NADH Dehydrogenase, Quinone 1 (NQO1) release. Furthermore, we found menthol treatment significantly inhibited the tumor incidence and number of tumors (P < 0.001). Interestingly, we observed that menthol treatment significantly inhibited TPA-induced altered activity of NF-κB in skin tumor. Consistently, menthol-treated tumors also showed significantly suppressed the Ras-Raf-ERK signaling pathway. Thus, our results suggest that menthol inhibits DMBA/TPA-induced skin tumorigenesis by attenuating the Ras and inhibiting NF-κB activity via inhibition of inflammation responses and pro-inflammatory cytokine release.

Keywords: DMBA/TPA, NF-κB, Ras-Raf-ERK, skin tumorigenesis

Procedia PDF Downloads 310

Authors: Firas M. Tuaimah, Huda M. Abdul Abbas

Abstract:

Accurate Short Term Load Forecasting (STLF) is essential for a variety of decision making processes. However, forecasting accuracy can drop due to the presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. Interval Type 2 Fuzzy Logic System (IT2 FLS), with additional degrees of freedom, gives an excellent tool for handling uncertainties and it improved the prediction accuracy. The training data used in this study covers the period from January 1, 2012 to February 1, 2012 for winter season and the period from July 1, 2012 to August 1, 2012 for summer season. The actual load forecasting period starts from January 22, till 28, 2012 for winter model and from July 22 till 28, 2012 for summer model. The real data for Iraqi power system which belongs to the Ministry of Electricity.

Keywords: short term load forecasting, prediction interval, type 2 fuzzy logic systems, electric, computer systems engineering

Procedia PDF Downloads 393

Authors: Reza Zakerinejad

Abstract:

Gully erosion is a serious problem that threading the sustainability of agricultural area and rangeland and water in a large part of Iran. This type of water erosion is the main source of sedimentation in many catchment areas in the north of Iran. Since in many national assessment approaches just qualitative models were applied the aim of this study is to predict the spatial distribution of gully erosion processes by means of detail terrain analysis and GIS -based logistic regression in the loess deposition in a case study in the Golestan Province. This study the DEM with 25 meter result ion from ASTER data has been used. The Landsat ETM data have been used to mapping of land use. The TreeNet model as a stochastic modeling was applied to prediction the susceptible area for gully erosion. In this model ROC we have set 20 % of data as learning and 20 % as learning data. Therefore, applying the GIS and satellite image analysis techniques has been used to derive the input information for these stochastic models. The result of this study showed a high accurate map of potential for gully erosion.

Keywords: TreeNet model, terrain analysis, Golestan Province, Iran

Procedia PDF Downloads 532

Authors: Rashima Mahajan, Dipali Bansal, Shweta Singh

Abstract:

Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, speech, text, gestures, and human physiological responses have also been discussed. Focus has been paid to explore the ability of EEG (Electroencephalogram) signals to portray thoughts, feelings, and unspoken words. An automated workflow-based protocol to design an EEG-based real time Brain Computer Interface system for analysis and classification of human emotions elicited by external audio/visual stimuli has been proposed. The front end hardware includes a cost effective and portable Emotive EEG Neuroheadset unit, a personal computer and a set of external stimulators. Primary signal analysis and processing of real time acquired EEG shall be performed using MATLAB based advanced brain mapping toolbox EEGLab/BCILab. This shall be followed by the development of MATLAB based self-defined algorithm to capture and characterize temporal and spectral variations in EEG under emotional stimulations. The extracted hybrid feature set shall be used to classify emotional states using artificial intelligence tools like Artificial Neural Network. The final system would result in an inexpensive, portable and more intuitive Brain Computer Interface in real time scenario to control prosthetic devices by translating different brain states into operative control signals.

Keywords: brain computer interface, electroencephalogram, EEGLab, BCILab, emotive, emotions, interval features, spectral features, artificial neural network, control applications

Procedia PDF Downloads 313

Authors: Pedro Llanos, Diego García

Abstract:

Spaceflight is considered the last frontier in terms of science, technology, and engineering. But it is also the next frontier in terms of human physiology and performance. After more than 200,000 years humans have evolved under earth’s gravity and atmospheric conditions, spaceflight poses environmental stresses for which human physiology is not adapted. Hypoxia, accelerations, and radiation are among such stressors, our research involves suborbital flights aiming to develop effective countermeasures in order to assure sustainable human space presence. The physiologic baseline of spaceflight participants is subject to great variability driven by age, gender, fitness, and metabolic reserve. The objective of the present study is to characterize different physiologic variables in a population of STEM practitioners during an aerobatic flight. Cardiovascular and pulmonary responses were determined in Science Astronaut Candidates (SACs) during unusual attitude aerobatic flight indoctrination. Physiologic data recordings from 20 subjects participating in high-G flight training were analyzed. These recordings were registered by wearable sensor-vest that monitored electrocardiographic tracings (ECGs), signs of dysrhythmias or other electric disturbances during all the flight. The same cardiovascular parameters were also collected approximately 10 min pre-flight, during each high-G/unusual attitude maneuver and 10 min after the flights. The ratio (pre-flight/in-flight/post-flight) of the cardiovascular responses was calculated for comparison of inter-individual differences. The resulting tracings depicting the cardiovascular responses of the subjects were compared against the G-loads (Gs) during the aerobatic flights to analyze cardiovascular variability aspects and fluid/pressure shifts due to the high Gs. In-flight ECG revealed cardiac variability patterns associated with rapid Gs onset in terms of reduced heart rate (HR) and some scattered dysrhythmic patterns (15% premature ventricular contractions-type) that were considered as triggered physiological responses to high-G/unusual attitude training and some were considered as instrument artifact. Variation events were observed in subjects during the +Gz and –Gz maneuvers and these may be due to preload and afterload, sudden shift. Our data reveal that aerobatic flight influenced the breathing rate of the subject, due in part by the various levels of energy expenditure due to the increased use of muscle work during these aerobatic maneuvers. Noteworthy was the high heterogeneity in the different physiological responses among a relatively small group of SACs exposed to similar aerobatic flights with similar Gs exposures. The cardiovascular responses clearly demonstrated that SACs were subjected to significant flight stress. Routine ECG monitoring during high-G/unusual attitude flight training is recommended to capture pathology underlying dangerous dysrhythmias in suborbital flight safety. More research is currently being conducted to further facilitate the development of robust medical screening, medical risk assessment approaches, and suborbital flight training in the context of the evolving commercial human suborbital spaceflight industry. A more mature and integrative medical assessment method is required to understand the physiology state and response variability among highly diverse populations of prospective suborbital flight participants.

Keywords: g force, aerobatic maneuvers, suborbital flight, hypoxia, commercial astronauts

Procedia PDF Downloads 126

Authors: Alam Ali, Ashok Kumar Pathak

Abstract:

Nowadays, copulas have attracted significant attention for modeling multivariate observations, and the foremost feature of copula functions is that they give us the liberty to study the univariate marginal distributions and their joint behavior separately. The copula parameter apprehends the intrinsic dependence among the marginal variables, and it can be estimated using parametric, semiparametric, or nonparametric techniques. This work aims to compare the coverage rates between an Elliptical and an Archimedean family of copulas via a fully parametric estimation technique.

Keywords: elliptical copula, archimedean copula, estimation, coverage rate

Procedia PDF Downloads 62

Authors: Lidija R. Jevrić, Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević

Abstract:

The present study deals with the selection of the most suitable quantitative structure-retention relationship (QSRR) models which should be used in prediction of the retention behavior of basic, neutral, acidic and phenolic pesticides which belong to different classes: fungicides, herbicides, metabolites, insecticides and plant growth regulators. Sum of ranking differences (SRD) approach can give a different point of view on selection of the most consistent QSRR model. SRD approach can be applied not only for ranking of the QSRR models, but also for detection of similarity or dissimilarity among them. Applying the SRD analysis, the most similar models can be found easily. In this study, selection of the best model was carried out on the basis of the reference ranking (“golden standard”) which was defined as the row average values of logarithm of retention time (logtr) defined by high performance liquid chromatography (HPLC). Also, SRD analysis based on experimental logtr values as reference ranking revealed similar grouping of the established QSRR models already obtained by hierarchical cluster analysis (HCA).

Keywords: chemometrics, chromatography, pesticides, sum of ranking differences

Procedia PDF Downloads 371

Authors: Ana Vieira, Fernando Lau, João Pedro Mortágua, Luís Cruz, Rui Santos

Abstract:

The air transport impact on environment is more than ever a limitative obstacle to the aeronautical industry continuous growth. Over the last decades, considerable effort has been carried out in order to obtain quieter aircraft solutions, whether by changing the original design or investigating more silent maneuvers. The noise propagated by rotating surfaces is one of the most important sources of annoyance, being present in most aerial vehicles. Bearing this is mind, CEIIA developed a new computational chain for noise prediction with in-house software tools to obtain solutions in relatively short time without using excessive computer resources. This work is based on the new acoustic tool, which aims to predict the rotor noise generated during steady and maneuvering flight, making use of the flexibility of the C language and the advantages of GPU programming in terms of velocity. The acoustic tool is based in the Formulation 1A of Farassat, capable of predicting two important types of noise: the loading and thickness noise. The present work describes the most important features of the acoustic tool, presenting its most relevant results and framework analyses for helicopters and UAV quadrotors.

Keywords: rotor noise, acoustic tool, GPU Programming, UAV noise

Procedia PDF Downloads 400

Authors: Emel Kızılkaya Aydogan, Mihrimah Ozmen, Yılmaz Delice

Abstract:

In recent days, there is a change and the ongoing development of the telecommunications sector in the global market. In this sector, churn analysis techniques are commonly used for analysing why some customers terminate their service subscriptions prematurely. In addition, customer churn is utmost significant in this sector since it causes to important business loss. Many companies make various researches in order to prevent losses while increasing customer loyalty. Although a large quantity of accumulated data is available in this sector, their usefulness is limited by data quality and relevance. In this paper, a cost-sensitive feature selection framework is developed aiming to obtain the feature reducts to predict customer churn. The framework is a cost based optional pre-processing stage to remove redundant features for churn management. In addition, this cost-based feature selection algorithm is applied in a telecommunication company in Turkey and the results obtained with this algorithm.

Keywords: churn prediction, data mining, decision-theoretic rough set, feature selection

Procedia PDF Downloads 443

Authors: Ghazwan Al-Haji, Adeyemi Adedokun

Abstract:

Traffic safety at intersections is highly represented, given the fact that accidents occur randomly in time and space. It is necessary to judge whether the intersection is dangerous or not based on short-term observations, and not waiting for many years of assessing historical accident data. There are active and pro-active road infrastructure safety methods for assessing safety at intersections. This study aims to investigate the use of quantitative and qualitative pre-observational methods as the best practice for accident prediction, future black spot identification, and treatment. Historical accident data from STRADA (the Swedish Traffic Accident Data Acquisition) was used within Norrkoping city in Sweden. The ADT (Average Daily Traffic), capacity and speed were used to predict accident rates. Locations with the highest accident records and predicted accident counts were identified and hence audited qualitatively by using Street Audit. The results from these quantitative and qualitative methods were analyzed, validated and compared. The paper provides recommendations on the used methods as well as on how to reduce the accident occurrence at the chosen intersections.

Keywords: intersections, traffic conflict, traffic safety, street audit, accidents predictions

Procedia PDF Downloads 231

Authors: Gabriele Borg, Alexei Debono, Charlie Abela

Abstract:

There on a constant rise in the availability of high volumes of data gathered from multiple sources, resulting in an abundance of unprocessed information that can be used to monitor patterns and trends in user behaviour. Similarly, year after year, Malta is also constantly experiencing ongoing population growth and an increase in mobilization demand. This research takes advantage of data which is continuously being sourced and converting it into useful information related to the traffic problem on the Maltese roads. The scope of this paper is to provide a methodology to create a custom dataset (MalTra - Malta Traffic) compiled from multiple participants from various locations across the island to identify the most common routes taken to expose the main areas of activity. This use of big data is seen being used in various technologies and is referred to as ITSs (Intelligent Transportation Systems), which has been concluded that there is significant potential in utilising such sources of data on a nationwide scale. Furthermore, a series of traffic prediction graph neural network models are conducted to compare MalTra to large-scale traffic datasets.

Keywords: graph neural networks, traffic management, big data, mobile data patterns

Procedia PDF Downloads 124

Authors: Ziyan Zhang

Abstract:

Although the Synthetic Control Method (SCM) is now widely applied, its most commonly- used inference method, placebo test, is often problematic, especially when the treatment is not uniquely assigned. This paper discusses the problems with the placebo test under the multivariate treatment case. And, to improve the power of inferences, I further propose an Andrews-type procedure as it potentially solves some drawbacks of the placebo test. Simulations are conducted to show the Andrews’ test is often valid and powerful, compared with the placebo test.

Keywords: Synthetic Control Method, Multiple treatments, Andrews' test, placebo test

Procedia PDF Downloads 162

Authors: Daniel James Pitchforth, Timothy James Rogers, Ulf Tyge Tygesen, Elizabeth Jane Cross

Abstract:

Wave loading is a primary cause of fatigue within offshore structures and its quantification presents a challenging and important subtask within the SHM framework. The accurate representation of physics in such environments is difficult, however, driving the development of data-driven techniques in recent years. Within many industrial applications, empirical laws remain the preferred method of wave loading prediction due to their low computational cost and ease of implementation. This paper aims to develop an approach that combines data-driven Gaussian process models with physical empirical solutions for wave loading, including Morison’s Equation. The aim here is to incorporate physics directly into the covariance function (kernel) of the Gaussian process, enforcing derived behaviors whilst still allowing enough flexibility to account for phenomena such as vortex shedding, which may not be represented within the empirical laws. The combined approach has a number of advantages, including improved performance over either component used independently and interpretable hyperparameters.

Keywords: offshore structures, Gaussian processes, Physics informed machine learning, Kernel design

Procedia PDF Downloads 186

Authors: Woo-Tai Jung, Sung-Yong Choi, Young-Hwan Park

Abstract:

The composite pavement system considered in this paper is composed of a functional surface layer, a fiber reinforced asphalt middle layer and a fiber reinforced lean concrete base layer. The mix design of the fiber reinforced lean concrete corresponds to the mix composition of conventional lean concrete but reinforced by fibers. The quasi-absence of research on the durability or long-term performances (fatigue, creep, etc.) of such mix design stresses the necessity to evaluate experimentally the long-term characteristics of this layer composition. This study tests the creep characteristics as one of the long-term characteristics of the fiber reinforced lean concrete layer for composite pavement using a new creep device. The test results reveal that the lean concrete mixed with fiber reinforcement and fly ash develops smaller creep than the conventional lean concrete. The results of the application of the CEB-FIP prediction equation indicate that a modified creep prediction equation should be developed to fit with the new mix design of the layer.

Keywords: creep, lean concrete, pavement, fiber reinforced concrete, base

Procedia PDF Downloads 518

Authors: Shan-Hong Ying, Ying-Fang Wang

Abstract:

A surrogate approach was deployed for assessing exposures of multiple chemicals at the selected working area of coating processes and applied to assess the exposure concentration of similar exposed groups using the same chemicals but different formula ratios. For the selected area, 6 to 12 portable photoionization detector (PID) were placed uniformly in its workplace to measure its total VOCs concentrations (CT-VOCs) for 6 randomly selected workshifts. Simultaneously, one sampling strain was placed beside one of these portable PIDs, and the collected air sample was analyzed for individual concentration (CVOCi) of 5 VOCs (xylene, butanone, toluene, butyl acetate, and dimethylformamide). Predictive models were established by relating the CT-VOCs to CVOCi of each individual compound via simple regression analysis. The established predictive models were employed to predict each CVOCi based on the measured CT-VOC for each the similar working area using the same portable PID. Results show that predictive models obtained from simple linear regression analyses were found with an R2 = 0.83~0.99 indicating that CT-VOCs were adequate for predicting CVOCi. In order to verify the validity of the exposure prediction model, the sampling analysis of the above chemical substances was further carried out and the correlation between the measured value (Cm) and the predicted value (Cp) was analyzed. It was found that there is a good correction between the predicted value and measured value of each measured chemical substance (R2=0.83~0.98). Therefore, the surrogate approach could be assessed the exposure concentration of similar exposed groups using the same chemicals but different formula ratios. However, it is recommended to establish the prediction model between the chemical substances belonging to each coater and the direct-reading PID, which is more representative of reality exposure situation and more accurately to estimate the long-term exposure concentration of operators.

Keywords: exposure assessment, exposure prediction model, surrogate approach, TVOC

Procedia PDF Downloads 147

Authors: A. H. Elkholy, D. M. Almutairi

Abstract:

The successful and efficient execution of a forging process is dependent upon the correct analysis of loading and metal flow of blanks. This paper investigates the Upper Bound Technique (UBT) and its application in the analysis of open forging process when a possibility of blank bulging exists. The UBT is one of the energy rate minimization methods for the solution of metal forming process based on the upper bound theorem. In this regards, the kinematically admissible velocity field is obtained by minimizing the total forging energy rate. A computer program is developed in this research to implement the UBT. The significant advantages of this method is the speed of execution while maintaining a fairly high degree of accuracy and the wide prediction capability. The information from this analysis is useful for the design of forging processes and dies. Results for the prediction of forging loads and stresses, metal flow and surface profiles with the assured benefits in terms of press selection and blank preform design are outlined in some detail. The obtained predictions are ready for comparison with both laboratory and industrial results.

Keywords: forging, upper bound technique, metal forming, forging energy, forging die/platen

Procedia PDF Downloads 291

Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

Abstract:

Current real-estate value estimation, difficult for laymen, usually is performed by specialists. This paper presents an automated estimation process based on big data and machine-learning technology that calculates influences of building conditions on real-estate price measurement. The present study analyzed actual building sales sample data for Nonhyeon-dong, Gangnam-gu, Seoul, Korea, measuring the major influencing factors among the various building conditions. Further to that analysis, a prediction model was established and applied using RapidMiner Studio, a graphical user interface (GUI)-based tool for derivation of machine-learning prototypes. The prediction model is formulated by reference to previous examples. When new examples are applied, it analyses and predicts accordingly. The analysis process discerns the crucial factors effecting price increases by calculation of weighted values. The model was verified, and its accuracy determined, by comparing its predicted values with actual price increases.

Keywords: apartment complex, big data, life-cycle building value analysis, machine learning

Procedia PDF Downloads 370

Authors: Kaiying Lai, Suiping Wang, Luodi Yu, Yang Zhang, Pengmin Qin

Abstract:

Abnormalities in auditory processing are one of the most commonly reported sensory processing impairments in children with Autism Spectrum Disorder (ASD). Tonal language speaker with autism has enhanced neural sensitivity to pitch changes in pure tone. However, not all children with ASD exhibit the same performance in pitch processing due to different auditory sensitivity. The current study aimed to examine auditory change detection in ASD with different auditory sensitivity. K-means clustering method was adopted to classify ASD participants into two groups according to the auditory processing scores of the Sensory Profile, 11 autism with hypersensitivity (mean age = 11.36 ; SD = 1.46) and 18 with hypo-sensitivity (mean age = 10.64; SD = 1.89) participated in a passive auditory oddball paradigm designed for eliciting mismatch negativity (MMN) under the pure tone condition. Results revealed that compared to hypersensitive autism, the children with hypo-sensitivity showed smaller MMN responses to pure tone stimuli. These results suggest that ASD with auditory hypersensitivity and hypo-sensitivity performed differently in processing pure tone, so neural responses to pure tone hold promise for predicting the auditory sensitivity of ASD and targeted treatment in children with ASD.

Keywords: ASD, sensory profile, pitch processing, mismatch negativity, MMN

Procedia PDF Downloads 388

Authors: Hexiong Liu

Abstract:

Powder metallurgy is the optimal method for the consolidation and preparation of W(Mo) alloys, which exhibit excellent application prospects at high temperatures. The properties of W(Mo) alloys are closely related to the sintered density. However, controlling the sintered density and porosity of these alloys is still challenging. In the past, the regulation methods mainly focused on time-consuming and costly trial-and-error experiments. In this study, the sintering data for more than a dozen W(Mo) alloys constituted a small-scale dataset, including both solid and liquid phases of sintering. Furthermore, simple descriptors were used to predict the sintered density of W(Mo) alloys based on the descriptor selection strategy and machine learning method (ML), where the ML algorithm included the least absolute shrinkage and selection operator (Lasso) regression, k-nearest neighbor (k-NN), random forest (RF), and multi-layer perceptron (MLP). The results showed that the interpretable descriptors extracted by our proposed selection strategy and the MLP neural network achieved a high prediction accuracy (R>0.950). By further predicting the sintered density of W(Mo) alloys using different sintering processes, the error between the predicted and experimental values was less than 0.063, confirming the application potential of the model.

Keywords: sintered density, machine learning, interpretable descriptors, W(Mo) alloy

Procedia PDF Downloads 78

Authors: Fanous Mohammadi, Majid H. Tangestani, Mohammad H. Tayebi

Abstract:

This research aims to evaluate and compare Geographical Information Systems (GIS)-based fuzzy models for producing orogenic gold prospectivity maps in the Saqqez area, NW of Iran. Gold occurrences are hosted in sericite schist and mafic to felsic meta-volcanic rocks in this area and are associated with hydrothermal alterations that extend over ductile to brittle shear zones. The predictor maps, which represent the Pre-(Source/Trigger/Pathway), syn-(deposition/physical/chemical traps) and post-mineralization (preservation/distribution of indicator minerals) subsystems for gold mineralization, were generated using empirical understandings of the specifications of known orogenic gold deposits and gold mineral systems and were then pre-processed and integrated to produce mineral prospectivity maps. Five fuzzy logic operators, including AND, OR, Fuzzy Algebraic Product (FAP), Fuzzy Algebraic Sum (FAS), and GAMMA, were applied to the predictor maps in order to find the most efficient prediction model. Prediction-Area (P-A) plots and field observations were used to assess and evaluate the accuracy of prediction models. Mineral prospectivity maps generated by AND, OR, FAP, and FAS operators were inaccurate and, therefore, unable to pinpoint the exact location of discovered gold occurrences. The GAMMA operator, on the other hand, produced acceptable results and identified potentially economic target sites. The P-A plot revealed that 68 percent of known orogenic gold deposits are found in high and very high potential regions. The GAMMA operator was shown to be useful in predicting and defining cost-effective target sites for orogenic gold deposits, as well as optimizing mineral deposit exploitation.

Keywords: mineral prospectivity mapping, fuzzy logic, GIS, orogenic gold deposit, Saqqez, Iran

Procedia PDF Downloads 119

Authors: Hamdi Amroun, Yacine Benziani, Mehdi Ammi

Abstract:

In this paper, we want to determine whether the accurate prediction of nicotine concentration can be obtained by using a network of smart objects and an e-cigarette. The approach consists of, first, the recognition of factors influencing smoking cessation such as physical activity recognition and participant’s behaviors (using both smartphone and smartwatch), then the prediction of the configuration of the e-cigarette (in terms of nicotine concentration, power, and resistance of e-cigarette). The study uses a network of commonly connected objects; a smartwatch, a smartphone, and an e-cigarette transported by the participants during an uncontrolled experiment. The data obtained from sensors carried in the three devices were trained by a Long short-term memory algorithm (LSTM). Results show that our LSTM-based model allows predicting the configuration of the e-cigarette in terms of nicotine concentration, power, and resistance with a root mean square error percentage of 12.9%, 9.15%, and 11.84%, respectively. This study can help to better control consumption of nicotine and offer an intelligent configuration of the e-cigarette to users.

Keywords: Iot, activity recognition, automatic classification, unconstrained environment

Procedia PDF Downloads 219

Authors: Sukhvinder Kaur, Jayanta Bora, Ashok Agarwal, Sangeeta Kaul

Abstract:

Background: HIV and STI transmission can be prevented if condoms are used properly, but condom tear may lead to infections even if are used consistently. Studies reveal high rates of condom breakage among Female Sex Workers (FSWs). USAID PHFI-PIPPSE is piloting a prevention model among high risk groups at Thane district of Maharashtra, India by implementing prevention and advocacy efforts for such risk behaviors. The current analysis highlights the correlates of condom breakage among FSWs from Thane. Method: A Behavioral Tracking Survey was conducted in 2014-15 among 503 FSWs through probability-based two stage random sampling from 3,660 FSWs at 100 hotspots, to understand levels of high risk behaviors, awareness and exposure to prevention programs. Bi-variate and multivariate-logistic regression methods used to assess the association of condom breakage while having sex with age, STI occurrence, anal sex with clients and alcohol consumption. Only self-reported STIs (Genital sore/ulcer, yellowish/ greenish discharge from vagina with/without foul smell, lower abdominal pain without diarrhea/dysentery or menses) were considered. Major Findings: Results depicted FSWs who reported condom breakage while having sex with any type of partner (paying clients, non-paying partners and other than main partner husband/boyfriend) had significantly high number of STIs (42.3% vs 16.9 %, P, 0.000) and had started sexual relationship in <16 years of age (31.0% vs 16.4 %, P, 0.000). Multivariate analysis after controlling the age at sex, knowledge about HIV and literacy, highlighted significantly higher odds of condom breakage among FSWs who have reported currently suffering with STI [AOR 2.91, 95% CI 1.75 - 4.83; P, 0.000]; who had anal sex with their paying client [AOR 2.59, 95% CI 1.59 - 4.19; P, 0.000]; and who consumed alcohol in the last 12 months [AOR 1.89, 95% CI 1.01 - 3.53; P, 0.047]. Conclusion: Risky behavior like anal sex with paying clients and impact of alcohol while having sex are main factors for condom breakage among young sex workers; and condom breakage leads to STIs. Hence, program interventions should address measures for prevention of condom breakage for HIV/STI prevention.

Keywords: female sex workers, condom breakage, anal sex, young sex workers

Procedia PDF Downloads 257

Authors: Dineshsingh Thakur, S. Nagesh, J. Basha

Abstract:

A lightweight High Speed Flexible Coupling (HSFC) is used to connect the Engine Gear Box (EGB) with an Accessory Gear Box (AGB) of the combat aircraft. The HSFC transmits the power at high speeds ranging from 10000 to 18000 rpm from the EGB to AGB. The HSFC is also accommodates larger misalignments resulting from thermal expansion of the aircraft engine and mounting arrangement. The HSFC has the series of metallic contoured annular thin cross-sectioned flexible plates to accommodate the misalignments. The flexible plates are accommodating the misalignment by the elastic material flexure. As the HSFC operates at higher speed, the flexural and axial resonance frequencies are to be kept away from the operating speed and proper prediction is required to prevent failure in the transmission line of a single engine fighter aircraft. To study the influence of flexible plate’s contour on the lateral critical speed (LCS) of HSFC, a mathematical model of HSFC as a elven rotor system is developed. The flexible plate being the bending member of the system, its bending stiffness which results from the contoured governs the LCS. Using transfer matrix method, Influence of various flexible plate contours on critical speed is analyzed. In the above analysis, the support bearing flexibility on critical speed prediction is also considered. Based on the study, a model is built with the optimum contour of flexible plate, for validation by experimental modal analysis. A good correlation between the theoretical prediction and model behavior is observed. From the study, it is found that the flexible plate’s contour is playing vital role in modification of system’s dynamic behavior and the present model can be extended for the development of similar type of flexible couplings for its computational simplicity and reliability.

Keywords: flexible rotor, critical speed, experimental modal analysis, high speed flexible coupling (HSFC), misalignment

Procedia PDF Downloads 210