Search results for: statistical techniques

Authors: Bindhu Lal, Karnika Kochal

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Clayey soil contains clay minerals with traces of metal oxides and organic matter, which exhibits properties like low drainage, high plasticity, and shrinkage. To overcome these issues, various soil reinforcement techniques are used to elevate the stiffness, water tightness, and bearing capacity of the soil. Such techniques include cementation, bituminization, freezing, fiber inclusion, geo-synthetics, nailing, etc. Reinforcement of soil with fibers has been a cost-effective solution to soil improvement problems. An experimental study was undertaken involving the inclusion of cotton waste fibers in clayey soil as reinforcement with different fiber contents (1%, 1.5%, 2%, and 2.5% by weight) and analyzing its effects on the unconfined compressive strength of the soil. Two categories of soil were taken, comprising of natural clay and clay mixed with 5% sodium bentonite by weight. The soil specimens were subjected to proctor compaction and unconfined compression tests. The validated outcome shows that fiber inclusion has a strikingly positive impact on the compressive strength and axial strain at failure of the soil. Based on the commendatory results procured, compressive strength was found to be directly proportional to the fiber content, with the effect being more pronounced at lower water content.

Keywords: bentonite clay, clay, cotton fibers, unconfined compressive strength

Procedia PDF Downloads 158

Authors: M. Miljkovic, A. Urakpo, M. Simic-Koumoutsaris

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Benefit-risk assessment (BRA) is a valuable tool that takes place in multiple stages during a medicine's lifecycle, and this assessment can be conducted in a variety of ways. The aim was to summarize current BRA methods used during approval decisions and in post-approval settings and to see possible future directions. Relevant reviews, recommendations, and guidelines published in medical literature and through regulatory agencies over the past five years have been examined. BRA implies the review of two dimensions: the dimension of benefits (determined mainly by the therapeutic efficacy) and the dimension of risks (comprises the safety profile of a drug). Regulators, industry, and academia have developed various approaches, ranging from descriptive textual (qualitative) to decision-analytic (quantitative) models, to facilitate the BRA of medicines during the product lifecycle (from Phase I trials, to authorization procedure, post-marketing surveillance and health technology assessment for inclusion in public formularies). These approaches can be classified into the following categories: stepwise structured approaches (frameworks); measures for benefits and risks that are usually endpoint specific (metrics), simulation techniques and meta-analysis (estimation techniques), and utility survey techniques to elicit stakeholders’ preferences (utilities). All these approaches share the following two common goals: to assist this analysis and to improve the communication of decisions, but each is subject to its own specific strengths and limitations. Before using any method, its utility, complexity, the extent to which it is established, and the ease of results interpretation should be considered. Despite widespread and long-time use, BRA is subject to debate, suffers from a number of limitations, and currently is still under development. The use of formal, systematic structured approaches to BRA for regulatory decision-making and quantitative methods to support BRA during the product lifecycle is a standard practice in medicine that is subject to continuous improvement and modernization, not only in methodology but also in cooperation between organizations.

Keywords: benefit-risk assessment, benefit-risk profile, product lifecycle, quantitative methods, structured approaches

Procedia PDF Downloads 134

Authors: Edgar Santoyo, Daniel Perez-Zarate, Agustin Acevedo, Lorena Diaz-Gonzalez, Mirna Guevara

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Four new gas geothermometers have been derived from a multivariate geo chemometric analysis of a geothermal fluid chemistry database, two of which use the natural logarithm of CO₂ and H2S concentrations (mmol/mol), respectively, and the other two use the natural logarithm of the H₂S/H₂ and CO₂/H₂ ratios. As a strict compilation criterion, the database was created with gas-phase composition of fluids and bottomhole temperatures (BHTM) measured in producing wells. The calibration of the geothermometers was based on the geochemical relationship existing between the gas-phase composition of well discharges and the equilibrium temperatures measured at bottomhole conditions. Multivariate statistical analysis together with the use of artificial neural networks (ANN) was successfully applied for correlating the gas-phase compositions and the BHTM. The predicted or simulated bottomhole temperatures (BHTANN), defined as output neurons or simulation targets, were statistically compared with measured temperatures (BHTM). The coefficients of the new geothermometers were obtained from an optimized self-adjusting training algorithm applied to approximately 2,080 ANN architectures with 15,000 simulation iterations each one. The self-adjusting training algorithm used the well-known Levenberg-Marquardt model, which was used to calculate: (i) the number of neurons of the hidden layer; (ii) the training factor and the training patterns of the ANN; (iii) the linear correlation coefficient, R; (iv) the synaptic weighting coefficients; and (v) the statistical parameter, Root Mean Squared Error (RMSE) to evaluate the prediction performance between the BHTM and the simulated BHTANN. The prediction performance of the new gas geothermometers together with those predictions inferred from sixteen well-known gas geothermometers (previously developed) was statistically evaluated by using an external database for avoiding a bias problem. Statistical evaluation was performed through the analysis of the lowest RMSE values computed among the predictions of all the gas geothermometers. The new gas geothermometers developed in this work have been successfully used for predicting subsurface temperatures in high-temperature geothermal systems of Mexico (e.g., Los Azufres, Mich., Los Humeros, Pue., and Cerro Prieto, B.C.) as well as in a blind geothermal system (known as Acoculco, Puebla). The last results of the gas geothermometers (inferred from gas-phase compositions of soil-gas bubble emissions) compare well with the temperature measured in two wells of the blind geothermal system of Acoculco, Puebla (México). Details of this new development are outlined in the present research work. Acknowledgements: The authors acknowledge the funding received from CeMIE-Geo P09 project (SENER-CONACyT).

Keywords: artificial intelligence, gas geochemistry, geochemometrics, geothermal energy

Procedia PDF Downloads 329

Authors: Pegah Farokhzad

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Family has a crucial role in maintaining the physical, social and mental health of the children. Most of the mental and anxiety problems of children reflects the complex interpersonal situations among family members, especially parents. In other words, anxiety problems of the children is correlated with deficit relationships of family members and improper child rearing styles. The parental child rearing styles leads to positive and negative consequences which affect the children’s mental health. Therefore, the present research was aimed to compare the parental child rearing styles and anxiety of children with stuttering and normal population. It was also aimed to study the relationship between parental child rearing styles and anxiety of children. The research sample included 54 boys with stuttering and 54 normal boys who were selected from the children (boys) of Tehran, Iran in the age range of 5 to 8 years in 2013. In order to collect data, Baumrind Child rearing Styles Inventory and Spence Parental Anxiety Inventory were used. Appropriate descriptive statistical methods and multivariate variance analysis and t test for independent groups were used to test the study hypotheses. Statistical data analyses demonstrated that there was a significant difference between stuttering boys and normal boys in anxiety (t = 7.601, p< 0.01); But there was no significant difference between stuttering boys and normal boys in parental child rearing styles (F = 0.129). There was also not found significant relationship between parental child rearing styles and children anxiety (F = 0.135, p< 0.05). It can be concluded that the influential factors of children’s society are parents, school, teachers, peers and media. So, parental child rearing styles are not the only influential factors on anxiety of children, and other factors including genetic, environment and child experiences are effective in anxiety as well. Details are discussed.

Keywords: child rearing styles, anxiety, stuttering, Iran

Procedia PDF Downloads 479

Authors: Lenka Hurtalová, Eva Tillová, Mária Chalupová, Juraj Belan, Milan Uhríčik

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The secondary alloy A226 is used for many automotive casting produced by mould casting and high pressure die-casting. This alloy has excellent castability, good mechanical properties and cost-effectiveness. Production of primary aluminium alloys belong to heavy source fouling of life environs. The European Union calls for the emission reduction and reduction in energy consumption, therefore, increase production of recycled (secondary) aluminium cast alloys. The contribution is deal with influence of recycling on the quality of the casting made from A226 in automotive industry. The properties of the casting made from secondary aluminium alloys were compared with the required properties of primary aluminium alloys. The effect of recycling on microstructure was observed using combination different analytical techniques (light microscopy upon black-white etching, scanning electron microscopy-SEM upon deep etching and energy dispersive X-ray analysis-EDX). These techniques were used for the identification of the various structure parameters, which was used to compare secondary alloy microstructure with primary alloy microstructure.

Keywords: A226 secondary aluminium alloy, deep etching, mechanical properties, recycling foundry aluminium alloy

Procedia PDF Downloads 520

Authors: Upal Barua

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Crash risk at intersections is a critical safety issue. This paper examines the effectiveness of removing an existing off-set at an intersection by realignment, in reducing crashes. Empirical Bayes method was applied to conduct a before-and-after study to assess the effect of this safety improvement. The Transportation Safety Improvement Program in Austin Transportation Department completed several safety improvement projects at high crash intersections with a view to reducing crashes. One of the common safety improvement techniques applied was the realignment of intersection approaches removing an existing off-set. This paper illustrates how this safety improvement technique is applied at a high crash intersection from inception to completion. This paper also highlights the significant crash reductions achieved from this safety improvement technique applying Empirical Bayes method in a before-and-after study. The result showed that realignment of intersection approaches removing an existing off-set can reduce crashes by 53%. This paper also features the state of the art techniques applied in planning, engineering, designing and construction of this safety improvement, key factors driving the success, and lessons learned in the process.

Keywords: crash risk, intersection, off-set, safety improvement technique, before-and-after study, empirical Bayes method

Procedia PDF Downloads 225

Authors: Hany M. Abd El-Lateef

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Two novel Schiff bases, 5-bromo-2-[(E)-(pyridin-3-ylimino) methyl] phenol (HBSAP) and 5-bromo-2-[(E)-(quinolin-8-ylimino) methyl] phenol (HBSAQ) have been synthesized. They have been characterized by elemental analysis and spectroscopic techniques (UV–Vis, IR and NMR). Moreover, the molecular structure of HBSAP and HBSAQ compounds are determined by single crystal X-ray diffraction technique. The inhibition activity of HBSAP and HBSAQ for carbon steel in 3.5 %NaCl+0.1 M HCl for both short and long immersion time, at different temperatures (20-50 ºC), was investigated using electrochemistry and surface characterization. The potentiodynamic polarization shows that the inhibitors molecule is more adsorbed on the cathodic sites. Its efficiency increases with increasing inhibitor concentrations (92.8 % at the optimal concentration of 10-3 M for HBSAQ). Adsorption of the inhibitors on the carbon steel surface was found to obey Langmuir’s adsorption isotherm with physical/chemical nature of the adsorption, as it is shown also by scanning electron microscopy. Further, the electronic structural calculations using quantum chemical methods were found to be in a good agreement with the results of the experimental studies.

Keywords: carbon steel, Schiff bases, corrosion inhibition, SEM, electrochemical techniques

Procedia PDF Downloads 370

Authors: Ling Xu, Giuseppe Loprencipe, Antonio D'Andrea

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Asphalt pavement with recycled and sustainable materials has become the most commonly adopted strategy for road construction, including reclaimed asphalt pavement (RAP) and crumb rubber (CR) from waste tires. However, the adhesion and cohesion characteristics of rubberized reclaimed asphalt pavement were still ambiguous, resulting in deteriorated adhesion behavior and life performance. This research investigated the effect of bonding characteristics on rutting resistance and moisture susceptibility of rubberized reclaimed asphalt pavement in terms of two RAP sources with different oxidation levels and two tire rubber with different particle sizes. Firstly, the binder bond strength (BBS) test and bonding failure distinguishment were conducted to analyze the surface behaviors of binder-aggregate interaction. Then, the compatibility and penetration grade of rubberized RAP binder were evaluated by rotational viscosity test and penetration test, respectively. Hamburg wheel track (HWT) test with high-temperature viscoelastic deformation analysis was adopted, which illustrated the rutting resistance. Additionally, a water boiling test was employed to evaluate the moisture susceptibility of the mixture and the texture features were characterized with the statistical parameters of image colors. Finally, the colloid structure model of rubberized RAP binder with surface interaction was proposed, and statistical analysis was established to release the correlation among various indexes. This study concluded that the gel-phase colloid structure and molecular diffusion of the free light fraction would affect the surface interpretation with aggregate, determining the bonding characteristic of rubberized RAP asphalt.

Keywords: bonding characteristics, reclaimed asphalt pavement, rubberized asphalt, sustainable material

Procedia PDF Downloads 44

Authors: Seyed Mehdi Ghezi, Hesam Hasanpoor

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This scientific article explores enhancement methods for ensemble learning with the aim of improving the performance of classifiers in the analysis and classification of brain signals. The research approach in this field consists of two main parts, each with its own strengths and weaknesses. The choice of approach depends on the specific research question and available resources. By combining these approaches and leveraging their respective strengths, researchers can enhance the accuracy and reliability of classification results, consequently advancing our understanding of the brain and its functions. The first approach focuses on utilizing machine learning methods to identify the best features among the vast array of features present in brain signals. The selection of features varies depending on the research objective, and different techniques have been employed for this purpose. For instance, the genetic algorithm has been used in some studies to identify the best features, while optimization methods have been utilized in others to identify the most influential features. Additionally, machine learning techniques have been applied to determine the influential electrodes in classification. Ensemble learning plays a crucial role in identifying the best features that contribute to learning, thereby improving the overall results. The second approach concentrates on designing and implementing methods for selecting the best classifier or utilizing meta-classifiers to enhance the final results in ensemble learning. In a different section of the research, a single classifier is used instead of multiple classifiers, employing different sets of features to improve the results. The article provides an in-depth examination of each technique, highlighting their advantages and limitations. By integrating these techniques, researchers can enhance the performance of classifiers in the analysis and classification of brain signals. This advancement in ensemble learning methodologies contributes to a better understanding of the brain and its functions, ultimately leading to improved accuracy and reliability in brain signal analysis and classification.

Keywords: ensemble learning, brain signals, classification, feature selection, machine learning, genetic algorithm, optimization methods, influential features, influential electrodes, meta-classifiers

Procedia PDF Downloads 59

Authors: Kausar Ali

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There are several ground treatment techniques which may meet the twin objectives of increasing the bearing capacity with simultaneous reduction of settlements, but the use of stone columns is one of the most suited techniques for flexible structures such as embankments, oil storage tanks etc. that can tolerate some settlement and used worldwide. However, when the stone columns in very soft soils are loaded; stone columns undergo excessive settlement due to low lateral confinement provided by the soft soil, leading to the failure of the structure. The poor performance of stone columns under these conditions can be improved by encasing the columns with a suitable geosynthetic. In this study, the effect of reinforcement on bearing capacity of composite soil has been investigated by conducting laboratory model tests on floating and end bearing long stone columns with l/d ratio of 12. The columns were reinforced by providing geosynthetic encasement over varying column length (upper 25%, 50%, 75%, and 100% column length). In this study, a group of columns has been used instead of single column, because in the field, columns used for the purpose always remain in groups. The tests indicate that the encasement over the full column length gives higher failure stress as compared to the encasement over the partial column length for both floating and end bearing long columns. The performance of end-bearing columns was found much better than the floating columns.

Keywords: geosynthetic, ground improvement, soft clay, stone column

Procedia PDF Downloads 410

Authors: Olutola Funmilayo Adekeye

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Tradition African practice of architecture can be said to be deeply rooted in Green Architecture in concept, design and execution. A study into the ancient building techniques in Ilorin Emirate depicts prominent (eco-centric approach of) Green Architecture principles. In the Pre-colonial era before the introduction of modern architecture and Western building materials, the Nigeria traditional communities built their houses to meet their cultural, religious and social needs using mainly indigenous building materials such as mud (Amo), cowdung (Boto), straws (koriko), palm fronts (Imo-Ope) to mention a few. This research attempts to identify the various techniques of applying the traditional African principles of Green Architecture to Ilorin traditional buildings. It will examine and assess some case studies to understand the extent to which Green architecture principles have been applied to traditional building designs that are still preserved today in Ilorin, Nigeria. Furthermore, this study intends to answer many questions, which can be summarized into two basic questions which are: (1) What aspects of what today are recognized as important green architecture principles have been applied to Ilorin traditional buildings? (2) To what extent have the principles of green architecture applied to Ilorin traditional buildings been ways of demonstrating a cultural attachment to the earth as an expression of the African sense of human being as one with nature?

Keywords: green architecture, Ilorin, traditional buildings, design principles, ecocentric, application

Procedia PDF Downloads 524

Authors: Elisabetta Cianfanelli, Margherita Tufarelli, Paolo Pupparo

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The dialectical mediation between desires and objects or between mass production and consumption continues to evolve over time. This relationship is influenced both by variable geometries of contexts that are distant from the mere design of product form and by aspects rooted in the very definition of industrial design. In particular, the overcoming of macro-areas of innovation in the technological, social, cultural, formal, and morphological spheres, supported by recent theories in critical and speculative design, seems to be moving further and further away from the design of the formal dimension of advanced products. The articulated fabric of theories and practices that feed the definition of “hyperobjects”, and no longer objects describes a common tension in all areas of design and production of industrial products. The latter are increasingly detached from the design of the form and meaning of the same in mass productions, thus losing the quality of products capable of social transformation. For years we have been living in a transformative moment as regards the design process in the definition of the industrial product. We are faced with a dichotomy in which there is, on the one hand, a reactionary aversion to the new techniques of industrial production and, on the other hand, a sterile adoption of the techniques of mass production that we can now consider traditional. This ambiguity becomes even more evident when we talk about industrial products, and we realize that we are moving further and further away from the concepts of "form" as a synthesis of a design thought aimed at the aesthetic-emotional component as well as the functional one. The design of forms and their contents, as statutes of social acts, allows us to investigate the tension on mass production that crosses seasons, trends, technicalities, and sterile determinisms. The design culture has always determined the formal qualities of objects as a sum of aesthetic characteristics functional and structural relationships that define a product as a coherent unit. The contribution proposes a reflection and a series of practical experiences of research on the form of advanced products. This form is understood as a kaleidoscope of relationships through the search for an identity, the desire for democratization, and between these two, the exploration of the aesthetic factor. The study of form also corresponds to the study of production processes, technological innovations, the definition of standards, distribution, advertising, the vicissitudes of taste and lifestyles. Specifically, we will investigate how the genesis of new forms for new meanings introduces a change in the relative innovative production techniques. It becomes, therefore, fundamental to investigate, through the reflections and the case studies exposed inside the contribution, also the new techniques of production and elaboration of the forms of the products, as new immanent and determining element inside the planning process.

Keywords: industrial design, product advanced design, mass productions, new meanings

Procedia PDF Downloads 108

Authors: Zhongmin Wang, Wudong Fan, Hengshan Zhang, Yimin Zhou

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In data-driven prognostic methods, the prediction accuracy of the estimation for remaining useful life of bearings mainly depends on the performance of health indicators, which are usually fused some statistical features extracted from vibrating signals. However, the existing health indicators have the following two drawbacks: (1) The differnet ranges of the statistical features have the different contributions to construct the health indicators, the expert knowledge is required to extract the features. (2) When convolutional neural networks are utilized to tackle time-frequency features of signals, the time-series of signals are not considered. To overcome these drawbacks, in this study, the method combining convolutional neural network with gated recurrent unit is proposed to extract the time-frequency image features. The extracted features are utilized to construct health indicator and predict remaining useful life of bearings. First, original signals are converted into time-frequency images by using continuous wavelet transform so as to form the original feature sets. Second, with convolutional and pooling layers of convolutional neural networks, the most sensitive features of time-frequency images are selected from the original feature sets. Finally, these selected features are fed into the gated recurrent unit to construct the health indicator. The results state that the proposed method shows the enhance performance than the related studies which have used the same bearing dataset provided by PRONOSTIA.

Keywords: continuous wavelet transform, convolution neural net-work, gated recurrent unit, health indicators, remaining useful life

Procedia PDF Downloads 114

Authors: Hiroyuki Aoki

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The physical properties of polymer materials are dependent on the conformation and molecular motion of a polymer chain. Therefore, the structure and dynamic behavior of the single polymer chain have been the most important concerns in the field of polymer physics. However, it has been impossible to directly observe the conformation of the single polymer chain in a bulk medium. In the current work, the novel techniques to study the conformation and dynamics of a single polymer chain are proposed. Since a fluorescence method is extremely sensitive, the fluorescence microscopy enables the direct detection of a single molecule. However, the structure of the polymer chain as large as 100 nm cannot be resolved by conventional fluorescence methods because of the diffraction limit of light. In order to observe the single chains, we developed the labeling method of polymer materials with a photo-switchable dye and the super-resolution microscopy. The real-space conformational analysis of single polymer chains with the spatial resolution of 15-20 nm was achieved. The super-resolution microscopy enables us to obtain the three-dimensional coordinates; therefore, we succeeded the conformational analysis in three dimensions. The direct observation by the nanometric optical microscopy would reveal the detailed information on the molecular processes in the various polymer systems.

Keywords: polymer materials, single molecule, super-resolution techniques, conformation

Procedia PDF Downloads 286

Authors: Neetu Manocha

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Image segmentation is a technique where a picture is parted into distinct parts having similar features which have a place with similar items. Various segmentation strategies have been proposed as of late by prominent analysts. But, after ultimate thorough research, the novelists have analyzed that generally, the old methods do not decrease the segmentation error rate. Then author finds the technique HIST-SI to decrease the segmentation error rates. In this technique, cluster-based and threshold-based segmentation techniques are merged together. After then, to improve the result of HIST-SI, the authors added the method of filtering and linking in this technique named Imp_HIST-SI to decrease the segmentation error rates. The goal of this research is to find a new technique to decrease the segmentation error rates and produce much better results than the HIST-SI technique. For testing the proposed technique, a dataset of Bhuvan – a National Geoportal developed and hosted by ISRO (Indian Space Research Organisation) is used. Experiments are conducted using Scikit-image & OpenCV tools of Python, and performance is evaluated and compared over various existing image segmentation techniques for several matrices, i.e., Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR).

Keywords: satellite image, image segmentation, edge detection, error rate, MSE, PSNR, HIST-SI, linking, filtering, imp_HIST-SI

Procedia PDF Downloads 122

Authors: Shi-Yu Xu

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The top three risk factors that have contributed to building collapses during past earthquake events in Taiwan are: "irregular floor plans or elevations," "insufficient columns in single-bay buildings," and the "weak-story problem." Fortunately, these unsound structural characteristics can be directly identified from the floor plans. However, due to the vast number of old buildings, conducting manual inspections to identify these compromised structural features in all existing structures would be time-consuming and prone to human errors. This study aims to develop an algorithm that utilizes artificial intelligence techniques to automatically pinpoint the structural components within a building's floor plans. The obtained spatial information will be utilized to construct a digital structural model of the building. This information, particularly regarding the distribution of columns in the floor plan, can then be used to conduct preliminary seismic assessments of the building. The study employs various image processing and pattern recognition techniques to enhance detection efficiency and accuracy. The study enables a large-scale evaluation of structural vulnerability for numerous old buildings, providing ample time to arrange for structural retrofitting in those buildings that are at risk of significant damage or collapse during earthquakes.

Keywords: structural vulnerability detection, object recognition, seismic capacity assessment, old buildings, artificial intelligence

Procedia PDF Downloads 67

Authors: Kwan Hee Han

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External environment of enterprise is rapidly changing majorly by global competition, cost reduction pressures, and new technology. In these situations, production scheduling function plays a critical role to meet customer requirements and to attain the goal of operational efficiency. It deals with short-term decision making in the production process of the whole supply chain. The major task of production scheduling is to seek a balance between customer orders and limited resources. In manufacturing companies, this task is so difficult because it should efficiently utilize resource capacity under the careful consideration of many interacting constraints. At present, many computerized software solutions have been utilized in many enterprises to generate a realistic production schedule to overcome the complexity of schedule generation. However, most production scheduling systems do not provide sufficient information about the validity of the generated schedule except limited statistics. Process mining only recently emerged as a sub-discipline of both data mining and business process management. Process mining techniques enable the useful analysis of a wide variety of processes such as process discovery, conformance checking, and bottleneck analysis. In this study, the performance of generated production schedule is evaluated by mining event log data of production scheduling software system by using the process mining techniques since every software system generates event logs for the further use such as security investigation, auditing and error bugging. An application of process mining approach is proposed for the validation of the goodness of production schedule generated by scheduling software systems in this study. By using process mining techniques, major evaluation criteria such as utilization of workstation, existence of bottleneck workstations, critical process route patterns, and work load balance of each machine over time are measured, and finally, the goodness of production schedule is evaluated. By using the proposed process mining approach for evaluating the performance of generated production schedule, the quality of production schedule of manufacturing enterprises can be improved.

Keywords: data mining, event log, process mining, production scheduling

Procedia PDF Downloads 264

Authors: Philipp Ruf, Massiwa Chabbi, Christoph Reich, Djaffar Ould-Abdeslam

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In recent years, convolutional neural networks (CNN) have demonstrated high performance in image analysis, but oftentimes, there is only structured data available regarding a specific problem. By interpreting structured data as images, CNNs can effectively learn and extract valuable insights from tabular data, leading to improved predictive accuracy and uncovering hidden patterns that may not be apparent in traditional structured data analysis. In applying a single neural network for analyzing multimodal data, e.g., both structured and unstructured information, significant advantages in terms of time complexity and energy efficiency can be achieved. Converting structured data into images and merging them with existing visual material offers a promising solution for applying CNN in multimodal datasets, as they often occur in a medical context. By employing suitable preprocessing techniques, structured data is transformed into image representations, where the respective features are expressed as different formations of colors and shapes. In an additional step, these representations are fused with existing images to incorporate both types of information. This final image is finally analyzed using a CNN.

Keywords: CNN, image processing, tabular data, mixed dataset, data transformation, multimodal fusion

Procedia PDF Downloads 100

Authors: Hira Lal Gope, Hidekazu Fukai

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The aim of this study is to develop a system which can identify and sort peaberries automatically at low cost for coffee producers in developing countries. In this paper, the focus is on the classification of peaberries and normal coffee beans using image processing and machine learning techniques. The peaberry is not bad and not a normal bean. The peaberry is born in an only single seed, relatively round seed from a coffee cherry instead of the usual flat-sided pair of beans. It has another value and flavor. To make the taste of the coffee better, it is necessary to separate the peaberry and normal bean before green coffee beans roasting. Otherwise, the taste of total beans will be mixed, and it will be bad. In roaster procedure time, all the beans shape, size, and weight must be unique; otherwise, the larger bean will take more time for roasting inside. The peaberry has a different size and different shape even though they have the same weight as normal beans. The peaberry roasts slower than other normal beans. Therefore, neither technique provides a good option to select the peaberries. Defect beans, e.g., sour, broken, black, and fade bean, are easy to check and pick up manually by hand. On the other hand, the peaberry pick up is very difficult even for trained specialists because the shape and color of the peaberry are similar to normal beans. In this study, we use image processing and machine learning techniques to discriminate the normal and peaberry bean as a part of the sorting system. As the first step, we applied Deep Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) as machine learning techniques to discriminate the peaberry and normal bean. As a result, better performance was obtained with CNN than with SVM for the discrimination of the peaberry. The trained artificial neural network with high performance CPU and GPU in this work will be simply installed into the inexpensive and low in calculation Raspberry Pi system. We assume that this system will be used in under developed countries. The study evaluates and compares the feasibility of the methods in terms of accuracy of classification and processing speed.

Keywords: convolutional neural networks, coffee bean, peaberry, sorting, support vector machine

Procedia PDF Downloads 129

Authors: Debarati Bhaumik, Diptish Dey

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Multilevel models, increasingly deployed in industries such as insurance, food production, and entertainment within functions such as marketing and supply chain management, need to be transparent and ethical. Applications usually result in binary classification within groups or hierarchies based on a set of input features. Using open-source datasets, we demonstrate that popular explainability methods, such as SHAP and LIME, consistently underperform inaccuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution to the outcome). Besides accuracy, the computational intractability of SHAP for binomial classification is a cause of concern. For transparent and ethical applications of these hierarchical statistical models, sound audit frameworks need to be developed. In this paper, we propose an audit framework for technical assessment of multilevel regression models focusing on three aspects: (i) model assumptions & statistical properties, (ii) model transparency using different explainability methods, and (iii) discrimination assessment. To this end, we undertake a quantitative approach and compare intrinsic model methods with SHAP and LIME. The framework comprises a shortlist of KPIs, such as PoCE (Percentage of Correct Explanations) and MDG (Mean Discriminatory Gap) per feature, for each of these three aspects. A traffic light risk assessment method is furthermore coupled to these KPIs. The audit framework will assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit businesses deploying multilevel models to be future-proof and aligned with the European Commission’s proposed Regulation on Artificial Intelligence.

Keywords: audit, multilevel model, model transparency, model explainability, discrimination, ethics

Procedia PDF Downloads 76

Authors: N. Ould cherchali, M. S. Boucherit, L. Barazane, A. Morsli

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Photovoltaic power is widely used to supply isolated or unpopulated areas (lighting, pumping, etc.). Great advantage is that this source is inexhaustible, it offers great safety in use and it is clean. But the dynamic models used to describe a photovoltaic system are complicated and nonlinear and due to nonlinear I-V and P–V characteristics of photovoltaic generators, a maximum power point tracking technique (MPPT) is required to maximize the output power. In this paper, two online techniques of maximum power point tracking using robust controller for photovoltaic systems are proposed, the first technique use fuzzy logic controller (FLC) and the second use sliding mode controller (SMC) for photovoltaic systems. The two maximum power point tracking controllers receive the partial derivative of power as inputs, and the output is the duty cycle corresponding to maximum power. A Photovoltaic generator with Boost converter is developed using MATLAB/Simulink to verify the preferences of the proposed techniques. SMC technique provides a good tracking speed in fast changing irradiation and when the irradiation changes slowly or is constant the panel power of FLC technique presents a much smoother signal with less fluctuations.

Keywords: fuzzy logic controller, maximum power point, photovoltaic system, tracker, sliding mode controller

Procedia PDF Downloads 526

Authors: Luiz Daniel Garay Trindade, Antonio De Freitas Valle Neto, Fabio Paulo Basso, Elder De Macedo Rodrigues, Maicon Bernardino, Daniel Welfer, Daniel Muller

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Agriculture plays an important role in society since it is one of the main sources of food in the world. To help the production and yield of crops, precision agriculture makes use of technologies aiming at improving productivity and quality of agricultural commodities. One of the problems hampering quality of agricultural production is the disease affecting crops. Failure in detecting diseases in a short period of time can result in small or big damages to production, causing financial losses to farmers. In order to provide a map of the contributions destined to the early detection of plant diseases and a comparison of the accuracy of the selected studies, a systematic literature review of the literature was performed, showing techniques for digital image processing and neural networks. We found 35 interesting tool support alternatives to detect disease in 19 plants. Our comparison of these studies resulted in an overall average accuracy of 87.45%, with two studies very closer to obtain 100%.

Keywords: pattern recognition, image processing, deep learning, precision agriculture, smart farming, agricultural automation

Procedia PDF Downloads 357

Authors: Tarik Ouslimane, Abdenour Lami, Salaheddine Aoudj, Mouna Hecini, Ouahiba Bouchelaghem, Nadjib Drouiche

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Slurry waste is a byproduct generated from the slicing process of multi-crystalline silicon ingots. This waste can be used as a secondary resource to recover high purity silicon which has a great economic value. From the management perspective, the ever increasing generation of kerf slurry waste loss leads to significant challenges for the photovoltaic industry due to the current low use of slurry waste for silicon recovery. Slurry waste, in most cases, contains silicon, silicon carbide, metal fragments and mineral-oil-based or glycol-based slurry vehicle. As a result, of the global scarcity of high purity silicon supply, the high purity silicon content in slurry has increasingly attracted interest for research. This paper presents a critical overview of the current techniques employed for high purity silicon recovery from kerf slurry waste. Hydrometallurgy is continuously a matter of study and research. However, in this review paper, several new techniques about the process of high purity silicon recovery from slurry waste are introduced. The purpose of the information presented is to improve the development of a clean and effective recovery process of high purity silicon from slurry waste.

Keywords: Kerf-loss, slurry waste, silicon carbide, silicon recovery, photovoltaic, high purity silicon, polyethylen glycol

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Authors: Telesphore Tiendrebeogo, Oumarou Sié

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Cloud computing technologies have attracted considerable interest in recent years. Thus, these latters have become more important for many existing database applications. It provides a new mode of use and of offer of IT resources in general. Such resources can be used “on demand” by anybody who has access to the internet. Particularly, the Cloud platform provides an ease to use interface between providers and users, allow providers to develop and provide software and databases for users over locations. Currently, there are many Cloud platform providers support large scale database services. However, most of these only support simple keyword-based queries and can’t response complex query efficiently due to lack of efficient in multi-attribute index techniques. Existing Cloud platform providers seek to improve performance of indexing techniques for complex queries. In this paper, we define a new cloud computing architecture based on a Distributed Hash Table (DHT) and design a prototype system. Next, we perform and evaluate our cloud computing indexing structure based on a hyperbolic tree using virtual coordinates taken in the hyperbolic plane. We show through our experimental results that we compare with others clouds systems to show our solution ensures consistence and scalability for Cloud platform.

Keywords: virtual coordinates, cloud, hyperbolic plane, storage, scalability, consistency

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Authors: Imine Zakaria, Meftah Sidi Mohamed El Amine

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One of the most vital aerodynamic organs of a flying machine is the wing, which allows it to fly in the air efficiently. The flow around the wing is very sensitive to changes in the angle of attack. Beyond a value, there is a phenomenon of the boundary layer separation on the upper surface, which causes instability and total degradation of aerodynamic performance called a stall. However, controlling flow around an airfoil has become a researcher concern in the aeronautics field. There are two techniques for controlling flow around a wing to improve its aerodynamic performance: passive and active controls. Blowing and suction are among the active techniques that control the boundary layer separation around an airfoil. Their objective is to give energy to the air particles in the boundary layer separation zones and to create vortex structures that will homogenize the velocity near the wall and allow control. Blowing and suction have long been used as flow control actuators around obstacles. In 1904 Prandtl applied a permanent blowing to a cylinder to delay the boundary layer separation. In the present study, several numerical investigations have been developed to predict a turbulent flow around an aerodynamic profile. CFD code was used for several angles of attack in order to validate the present work with that of the literature in the case of a clean profile. The variation of the lift coefficient CL with the momentum coefficient

Keywords: CFD, control flow, lift, slot

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Authors: Fei Gao, Rodolfo C. Raga Jr.

Abstract:

This research proposal will ascertain the major risk factors for diabetes and to design a predictive model for risk assessment. The project aims to improve diabetes early detection and management by utilizing data science techniques, which may improve patient outcomes and healthcare efficiency. The phase relation values of each attribute were used to analyze and choose the attributes that might influence the examiner's survival probability using Diabetes Health Indicators Dataset from Kaggle’s data as the research data. We compare and evaluate eight machine learning algorithms. Our investigation begins with comprehensive data preprocessing, including feature engineering and dimensionality reduction, aimed at enhancing data quality. The dataset, comprising health indicators and medical data, serves as a foundation for training and testing these algorithms. A rigorous cross-validation process is applied, and we assess their performance using five key metrics like accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). After analyzing the data characteristics, investigate their impact on the likelihood of diabetes and develop corresponding risk indicators.

Keywords: diabetes, risk factors, predictive model, risk assessment, data science techniques, early detection, data analysis, Kaggle

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Authors: Mohamed Dhouibi, Bogdan Stirbu, Chabotier André, Marc Pirlot

Abstract:

Internal barrel wear is a major factor affecting the performance of small caliber guns in their different life phases. Wear analysis is, therefore, a very important process for understanding how wear occurs, where it takes place, and how it spreads with the aim on improving the accuracy and effectiveness of small caliber weapons. This paper discusses the measurement and analysis of combustion chamber wear for a small-caliber gun using a Coordinate Measuring Machine (CMM). Initially, two different NATO small caliber guns: 5.56x45mm and 7.62x51mm, are considered. A Micura Zeiss Coordinate Measuring Machine (CMM) equipped with the VAST XTR gold high-end sensor is used to measure the inner profile of the two guns every 300-shot cycle. The CMM parameters, such us (i) the measuring force, (ii) the measured points, (iii) the time of masking, and (iv) the scanning velocity, are investigated. In order to ensure minimum measurement error, a statistical analysis is adopted to select the reliable CMM parameters combination. Next, two measurement strategies are developed to capture the shape and the volume of each gun chamber. Thus, a task-specific measurement uncertainty (TSMU) analysis is carried out for each measurement plan. Different approaches of TSMU evaluation have been proposed in the literature. This paper discusses two different techniques. The first is the substitution method described in ISO 15530 part 3. This approach is based on the use of calibrated workpieces with similar shape and size as the measured part. The second is the Monte Carlo simulation method presented in ISO 15530 part 4. Uncertainty evaluation software (UES), also known as the Virtual Coordinate Measuring Machine (VCMM), is utilized in this technique to perform a point-by-point simulation of the measurements. To conclude, a comparison between both approaches is performed. Finally, the results of the measurements are verified through calibrated gauges of several dimensions specially designed for the two barrels. On this basis, an experimental database is developed for further analysis aiming to quantify the relationship between the volume of wear and the muzzle velocity of small caliber guns.

Keywords: coordinate measuring machine, measurement uncertainty, erosion and wear volume, small caliber guns

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Authors: Mirna Guevara, Edgar Santoyo, Daniel Perez-Zarate, Erika Almirudis

Abstract:

The Acoculco caldera located in Puebla (Mexico) has been preliminary identified as a blind hot-dry rock geothermal system. Two drilled wells suggest the existence of high temperatures >300°C and non-conventional tools are been applied to study this system. A comprehensive survey of soil-gas (CO₂) flux measurements (1,500 sites) was carried out during the dry seasons over almost two years (2015 and 2016). Isotopic analyses of δ¹³CCO₂ were performed to discriminate the origin source of the CO2 fluxes. The soil CO2 flux measurements were made in situ by the accumulation chamber method, whereas gas samples for δ13CCO2 were selectively collected from the accumulation chamber with evacuated gas vials via a septum. Two anomalous geothermal zones were identified as a result of these campaigns: Los Azufres (19°55'29.4'' N; 98°08'39.9'' W; 2,839 masl) and Alcaparrosa (19°55'20.6'' N; 98°08'38.3'' W; 2,845 masl). To elucidate the origin of the C in soil CO₂ fluxes, the isotopic signature of δ¹³C was used. Graphical Statistical Analysis (GSA) and a three end-member mixing diagram were used to corroborate the presence of distinctive statistical samples, and trends for the diffuse gas fluxes. Spatial and temporal distributions of the CO₂ fluxes were studied. High CO₂ emission rates up to 38,217 g/m2/d and 33,706 g/m2/d were measured for the Los Azufres and Alcaparrosa respectively; whereas the δ¹³C signatures showed values ranging from -3.4 to -5.5 o/oo for both zones, confirming their magmatic origin. This study has provided a valuable framework to set the direction of further exploration campaigns in the Acoculco caldera. Acknowledgements: The authors acknowledge the funding received from CeMIE-Geo P09 project (SENER-CONACyT).

Keywords: accumulation chamber method, carbon dioxide, diffusive degassing, geothermal exploration

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Authors: Anne Jane M. Hallasgo

Abstract:

This study assessed the level of job satisfaction and organizational commitment among job order personnel at the Overseas Workers Welfare Administration (OWWA) Regional Welfare Office Caraga. The primary objective of the study was to determine a correlation between the employees’ level of organizational commitment, job satisfaction, and their work performance. A carefully selected sample of twenty-five job orders from the OWWA Regional Welfare Office Caraga participated in the study. These individuals were chosen to represent the organization’s job order workforce. For accuracy and dependability, various types of statistical methods and instruments were employed, including advanced statistical tests like the independent sample T-test, one-way analysis of variance (ANOVA), and Spearman's rank correlation coefficient, as well as descriptive statistics like mean, frequency, and percentage. The study found an acceptable level of job satisfaction regarding work performance. It revealed a significant relationship between affective commitment and job satisfaction concerning leadership and coworkers. A correlation was observed between normative commitment and work performance. The findings suggest that organizations emphasizing positive leadership, fostering supportive coworker relationships, aligning with employee values, and promoting a culture of commitment are likely to enhance both affective and normative commitment, thereby improving overall employee satisfaction. The study recommends designing and implementing a holistic employee well-being program that addresses physical, mental, and emotional health contributing to increased job satisfaction and organizational commitment, creating a healthier and engaged workforce. This research contributes to the understanding of the dynamics of organizational commitment and job satisfaction among job order employees in the public sector.

Keywords: affective commitment, continuous commitment, normative commitment, job satisfaction

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Authors: U. Waqas, M. F. Ahmed, A. Mehmood, M. A. Rashid

Abstract:

In geophysical exploration surveys, the quality of acquired data holds significant importance before executing the data processing and interpretation phases. In this study, 2D seismic reflection survey data of Fort Abbas area, Cholistan Desert, Pakistan was taken as test case in order to assess its quality on statistical bases by using normalized root mean square error (NRMSE), Cronbach’s alpha test (α) and null hypothesis tests (t-test and F-test). The analysis challenged the quality of the acquired data and highlighted the significant errors in the acquired database. It is proven that the study area is plain, tectonically least affected and rich in oil and gas reserves. However, subsurface 3D modeling and contouring by using acquired database revealed high degrees of structural complexities and intense folding. The NRMSE had highest percentage of residuals between the estimated and predicted cases. The outcomes of hypothesis testing also proved the biasness and erraticness of the acquired database. Low estimated value of alpha (α) in Cronbach’s alpha test confirmed poor reliability of acquired database. A very low quality of acquired database needs excessive static correction or in some cases, reacquisition of data is also suggested which is most of the time not feasible on economic grounds. The outcomes of this study could be used to assess the quality of large databases and to further utilize as a guideline to establish database quality assessment models to make much more informed decisions in hydrocarbon exploration field.

Keywords: Data quality, Null hypothesis, Seismic lines, Seismic reflection survey

Procedia PDF Downloads 139