Search results for: defect prediction

Authors: Vinod Kumar Jaysaval, Prateek Agarwal

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Performance prediction of airborne radar is a challenging and cumbersome task in clutter scenario for different types of targets. A generalized model requires to predict the performance of Radar for air targets as well as ground moving targets. In this paper, we propose a generalized model to bring out the performance of airborne radar for different Pulsed Repetition Frequency (PRF) as well as different type of targets. The model provides a platform to bring out different subsystem parameters for different applications and performance requirements under different types of clutter terrain.

Keywords: airborne radar, blind zone, clutter, probability of detection

Procedia PDF Downloads 464

Authors: Janis Zarins, Kristaps Blums, Oskars Radzins, Renars Deksnis, Atis Svare, Santa Salaka

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Wide tumor resection remains the first choice method for tumors of the oral cavity. Nevertheless, remained tissue defect impacts patients functional and aesthetical outcome, which could be improved using microvascular tissue transfers. Mandibular reconstruction is challenging due to the complexity of composite tissue defects and occlusal relationships for normal eating, chewing, and pain free jaw motions. Individual 3-D virtual planning would provide better symmetry and functional outcome. The main goal of preoperative planning is to develop a customized surgical approach with patient specific cutting guides of the mandible, osteotomy guides of the fibula, pre-bended osteosynthesis plates to perform more precise reconstruction, to decrease the surgery time and reach the best outcome. Our study is based on the analysis of 32 patients operated on between 2019 to 2021. All patients underwent mandible reconstruction with vascularized fibula flaps. Patients characteristics, surgery profile, survival, functional outcome, and quality of life was evaluated. Preoperative planning provided a significant decrease of surgery time and the best arrangement of bone closely similar as before the surgery. In cases of bone asymmetry, deformity and malposition, a new mandible was created using 3D planning to restore the appearance of lower jaw anatomy and functionality.

Keywords: mandibular, 3D planning, cutting guides, fibula flap, reconstruction

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Authors: Soumendu Monia, Vaibhav Jain, Hrishikesh Jugade, Manashi Adhikary, Goutam Mukhopadhyay

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'Rolled in scale' (RIS) and 'Rolled in Scum' (RISc) are two superficial surface defects on cold rolled and annealed steel sheets which affect the aesthetics of surface and thereby that of the end-product. Both the defects are believed to be originating from distinctly different sources having different mechanisms of formation. However, due to their similar physical appearance, RIS and RISc are generally confused with each other and hence attaining the exact root cause for elimination of the defect becomes difficult. RIS appears irregular in shape, sometimes scattered, and always oriented in rolling direction. RISc is generally oval shaped, having identifiable pointed edges and mostly oriented in rolling direction. Visually, RIS appears to be greyish in colour whereas RISc is whitish in colour. Both the defects have quite random occurrence and do not leave any imprints on the reverse-side of the sheet. In the current study, an attempt has been made to differentiate these two similar looking surface defects using various metallographic and characterization techniques. Systematic experiments have been carried out to identify possible mechanisms of formation of these defects. Detailed characterization revealed basic differences between RIS and RISc with respect to their surface morphology. To summarize, RIS was observed as a residue of an otherwise under-pickled scale patch on surface, after it has been subjected to cold rolling and annealing in a batch/continuous furnace. Whereas RISc was found to be a localized rubbing of the surface, at the time of cold rolling itself, resulting in a rough surface texture.

Keywords: annealing, rolled in scale, rolled in scum, skin panel

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Authors: S. Shankar Bharathi

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Inspection of surface defects on metallic components has always been challenging due to its specular property. Occurrences of defects such as scratches, rust, pitting are very common in metallic surfaces during the manufacturing process. These defects if unchecked can hamper the performance and reduce the life time of such component. Many of the conventional image processing algorithms in detecting the surface defects generally involve segmentation techniques, based on thresholding, edge detection, watershed segmentation and textural segmentation. They later employ other suitable algorithms based on morphology, region growing, shape analysis, neural networks for classification purpose. In this paper the work has been focused only towards detecting scratches. Global and other thresholding techniques were used to extract the defects, but it proved to be inaccurate in extracting the defects alone. However, this paper does not focus on comparison of different segmentation techniques, but rather describes a novel approach towards segmentation combined with hausdorff dilation distance. The proposed algorithm is based on the distribution of the intensity levels, that is, whether a certain gray level is concentrated or evenly distributed. The algorithm is based on extraction of such concentrated pixels. Defective images showed higher level of concentration of some gray level, whereas in non-defective image, there seemed to be no concentration, but were evenly distributed. This formed the basis in detecting the defects in the proposed algorithm. Hausdorff dilation distance based on mathematical morphology was used to strengthen the segmentation of the defects.

Keywords: metallic surface, scratches, segmentation, hausdorff dilation distance, machine vision

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Authors: Abdul Manan

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The necessity for efficient and cost-effective energy storage devices to intelligently store the inconsistent energy output from modern renewable energy sources is peaked today. The scientific community is struggling to identify the appropriate material system for energy storage applications. Countless contributions by researchers worldwide have now helped us identify the possible snags and limitations associated with each material/method. Energy storage has attracted great attention for its use in portable electronic devices military field. Different devices, such as dielectric capacitors, supercapacitors, and batteries, are used for energy storage. Of these, dielectric capacitors have high energy output, a long life cycle, fast charging and discharging capabilities, work at high temperatures, and excellent fatigue resistance. The energy storage characteristics have been studied to be highly affected by various factors, such as grain size, optimized compositions, grain orientation, energy band gap, processing techniques, defect engineering, core-shell formation, interface engineering, electronegativity difference, the addition of additives, density, secondary phases, the difference of Pmax-Pr, sample thickness, area of the electrode, testing frequency, and AC/DC conditions. The data regarding these parameters/factors are scattered in the literature, and the aim of this study is to gather the data into a single paper that will be beneficial for new researchers in the field of interest. Furthermore, control over and optimizing these parameters will lead to enhancing the energy storage properties.

Keywords: strategies, ceramics, energy storage, capacitors

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Authors: Marcus Klein, Martina GrießBach, Richard Kupke

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The current advances in the research and manufacturing of large area graphene layers are promising towards the introduction of this exciting material in the display industry and other applications that benefit from excellent electrical and optical characteristics. New production technologies in the fabrication of flexible displays, touch screens or printed electronics apply graphene layers on non-metal substrates and bring new challenges to the required metrology. Traditional measurement concepts of layer thickness, sheet resistance, and layer uniformity, are difficult to apply to graphene production processes and are often harmful to the product layer. New non-contact sensor concepts are required to adapt to the challenges and even the foreseeable inline production of large area graphene. Dedicated non-contact measurement sensors are a pioneering method to leverage these issues in a large variety of applications, while significantly lowering the costs of development and process setup. Transferred and printed graphene layers can be characterized with high accuracy in a huge measurement range using a very high resolution. Large area graphene mappings are applied for process optimization and for efficient quality control for transfer, doping, annealing and stacking processes. Examples of doped, defected and excellent Graphene are presented as quality images and implications for manufacturers are explained.

Keywords: graphene, doping and defect testing, non-contact sheet resistance measurement, inline metrology

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Authors: Yu-Shan Wu, Po-Liang Lai, I-Ming Chu

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Poly(methylmethacrylate)(PMMA) is the most frequently used bone void filler for vertebral augmentation in osteoporotic fracture. PMMA bone cement not only exhibits strong mechanical properties but also can fabricate according to the shape of bone defect. However, the adhesion between the PMMA-based cement and the adjacent bone is usually weak and as PMMA bone cement is inherently bioinert. The combination of bioceramics and polymers as composites may increase cell adhesion and improve biocompatibility. The nano-hydroxyapatite(HAP) not only plays a significant role in maintaining the properties of the natural bone but also offers a favorable environment for osteoconduction, protein adhesion, and osteoblast proliferation. However, defects and cracks can form at the polymer/ceramics interface, resulting in uneven distribution of stress and subsequent inferior mechanical strength. Surface-modified HAP nano-crystals were prepared by chemically grafting poly(ε-caprolactone)(PCL) on surface-modified nano-HAP surface to increase the affinity of polymer/ceramic phases .Thus, incorporation of surface-modified nano-hydroxyapatite (EC-HAP) may not only improve the interfacial adhesion between cement and bone and between nanoparticles and cement, but also increase biocompatibility. In this research, PMMA mixing with 0, 5, 10, 15, 20, 25 and 30 wt% EC-HAP were examined. MC3T3-E1 cells were used for the biological evaluation of the response to the cements in vitro. Morphology was observed using scanning electron microscopy (SEM). Mechanical properties of HAP/PMMA and EC-HAP/PMMA cement were investigated by compression test. Surface wettability of the cements was measured by contact angles.

Keywords: bone cement, biocompatibility, nano-hydroxyapatite, polycaprolactone, PMMA, surface grafting

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Authors: Ali Kazemi

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Those within the volatile and interconnected international economic markets, appropriately predicting market trends, hold substantial fees for traders and financial establishments. Traditional device mastering strategies have made full-size strides in forecasting marketplace movements; however, monetary data's complicated and networked nature calls for extra sophisticated processes. This observation offers a groundbreaking method for monetary marketplace prediction that leverages the synergistic capability of Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks. Our suggested algorithm is meticulously designed to forecast the traits of inventory market indices and cryptocurrency costs, utilizing a comprehensive dataset spanning from January 1, 2015, to December 31, 2023. This era, marked by sizable volatility and transformation in financial markets, affords a solid basis for schooling and checking out our predictive version. Our algorithm integrates diverse facts to construct a dynamic economic graph that correctly reflects market intricacies. We meticulously collect opening, closing, and high and low costs daily for key inventory marketplace indices (e.g., S&P 500, NASDAQ) and widespread cryptocurrencies (e.g., Bitcoin, Ethereum), ensuring a holistic view of marketplace traits. Daily trading volumes are also incorporated to seize marketplace pastime and liquidity, providing critical insights into the market's shopping for and selling dynamics. Furthermore, recognizing the profound influence of the monetary surroundings on financial markets, we integrate critical macroeconomic signs with hobby fees, inflation rates, GDP increase, and unemployment costs into our model. Our GCN algorithm is adept at learning the relational patterns amongst specific financial devices represented as nodes in a comprehensive market graph. Edges in this graph encapsulate the relationships based totally on co-movement styles and sentiment correlations, enabling our version to grasp the complicated community of influences governing marketplace moves. Complementing this, our LSTM algorithm is trained on sequences of the spatial-temporal illustration discovered through the GCN, enriched with historic fee and extent records. This lets the LSTM seize and expect temporal marketplace developments accurately. Inside the complete assessment of our GCN-LSTM algorithm across the inventory marketplace and cryptocurrency datasets, the version confirmed advanced predictive accuracy and profitability compared to conventional and opportunity machine learning to know benchmarks. Specifically, the model performed a Mean Absolute Error (MAE) of 0.85%, indicating high precision in predicting day-by-day charge movements. The RMSE was recorded at 1.2%, underscoring the model's effectiveness in minimizing tremendous prediction mistakes, which is vital in volatile markets. Furthermore, when assessing the model's predictive performance on directional market movements, it achieved an accuracy rate of 78%, significantly outperforming the benchmark models, averaging an accuracy of 65%. This high degree of accuracy is instrumental for techniques that predict the course of price moves. This study showcases the efficacy of mixing graph-based totally and sequential deep learning knowledge in economic marketplace prediction and highlights the fee of a comprehensive, records-pushed evaluation framework. Our findings promise to revolutionize investment techniques and hazard management practices, offering investors and economic analysts a powerful device to navigate the complexities of cutting-edge economic markets.

Keywords: financial market prediction, graph convolutional networks (GCNs), long short-term memory (LSTM), cryptocurrency forecasting

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Authors: Esra Polat

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Partial Least Squares Discriminant Analysis (PLSDA) is a statistical method for classification and consists a classical Partial Least Squares Regression (PLSR) in which the dependent variable is a categorical one expressing the class membership of each observation. PLSDA can be applied in many cases when classical discriminant analysis cannot be applied. For example, when the number of observations is low and when the number of independent variables is high. When there are missing values, PLSDA can be applied on the data that is available. Finally, it is adapted when multicollinearity between independent variables is high. The aim of this study is to determine the economic and/or demographic indicators, which are effective in grouping the 28 European Union (EU) member countries and 7 candidate countries (including potential candidates Bosnia and Herzegovina (BiH) and Kosova) by using the data set obtained from database of the World Bank for 2014. Leaving the political issues aside, the analysis is only concerned with the economic and demographic variables that have the potential influence on country’s eligibility for EU entrance. Hence, in this study, both the performance of PLSDA method in classifying the countries correctly to their pre-defined groups (candidate or member) and the differences between the EU countries and candidate countries in terms of these indicators are analyzed. As a result of the PLSDA, the value of percentage correctness of 100 % indicates that overall of the 35 countries is classified correctly. Moreover, the most important variables that determine the statuses of member and candidate countries in terms of economic indicators are identified as 'external balance on goods and services (% GDP)', 'gross domestic savings (% GDP)' and 'gross national expenditure (% GDP)' that means for the 2014 economical structure of countries is the most important determinant of EU membership. Subsequently, the model validated to prove the predictive ability by using the data set for 2015. For prediction sample, %97,14 of the countries are correctly classified. An interesting result is obtained for only BiH, which is still a potential candidate for EU, predicted as a member of EU by using the indicators data set for 2015 as a prediction sample. Although BiH has made a significant transformation from a war-torn country to a semi-functional state, ethnic tensions, nationalistic rhetoric and political disagreements are still evident, which inhibit Bosnian progress towards the EU.

Keywords: classification, demographic indicators, economic indicators, European Union, partial least squares discriminant analysis

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Authors: Faiz N. K. Yusufi, Aquil Ahmed, Jamal Ahmad

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Predicting the risk of diabetic retinopathy (DR) in Indian type 2 diabetes patients is immensely necessary. India, being the second largest country after China in terms of a number of diabetic patients, to the best of our knowledge not a single risk score for complications has ever been investigated. Diabetic retinopathy is a serious complication and is the topmost reason for visual impairment across countries. Any type or form of DR has been taken as the event of interest, be it mild, back, grade I, II, III, and IV DR. A sample was determined and randomly collected from the Rajiv Gandhi Centre for Diabetes and Endocrinology, J.N.M.C., A.M.U., Aligarh, India. Collected variables include patients data such as sex, age, height, weight, body mass index (BMI), blood sugar fasting (BSF), post prandial sugar (PP), glycosylated haemoglobin (HbA1c), diastolic blood pressure (DBP), systolic blood pressure (SBP), smoking, alcohol habits, total cholesterol (TC), triglycerides (TG), high density lipoprotein (HDL), low density lipoprotein (LDL), very low density lipoprotein (VLDL), physical activity, duration of diabetes, diet control, history of antihypertensive drug treatment, family history of diabetes, waist circumference, hip circumference, medications, central obesity and history of DR. Cox proportional hazard regression is used to design risk scores for the prediction of retinopathy. Model calibration and discrimination are assessed from Hosmer Lemeshow and area under receiver operating characteristic curve (ROC). Overfitting and underfitting of the model are checked by applying regularization techniques and best method is selected between ridge, lasso and elastic net regression. Optimal cut off point is chosen by Youden’s index. Five-year probability of DR is predicted by both survival function, and Markov chain two state model and the better technique is concluded. The risk scores developed can be applied by doctors and patients themselves for self evaluation. Furthermore, the five-year probabilities can be applied as well to forecast and maintain the condition of patients. This provides immense benefit in real application of DR prediction in T2DM.

Keywords: Cox proportional hazard regression, diabetic retinopathy, ROC curve, type 2 diabetes mellitus

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Authors: Habtamu Ayenew Asegie

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Wealth, as opposed to income or consumption, implies a more stable and permanent status. Due to natural and human-made difficulties, households' economies will be diminished, and their well-being will fall into trouble. Hence, governments and humanitarian agencies offer considerable resources for poverty and malnutrition reduction efforts. One key factor in the effectiveness of such efforts is the accuracy with which low-income or poor populations can be identified. As a result, this study aims to predict a household’s wealth status using ensemble Machine learning (ML) algorithms. In this study, design science research methodology (DSRM) is employed, and four ML algorithms, Random Forest (RF), Adaptive Boosting (AdaBoost), Light Gradient Boosted Machine (LightGBM), and Extreme Gradient Boosting (XGBoost), have been used to train models. The Ethiopian Demographic and Health Survey (EDHS) dataset is accessed for this purpose from the Central Statistical Agency (CSA)'s database. Various data pre-processing techniques were employed, and the model training has been conducted using the scikit learn Python library functions. Model evaluation is executed using various metrics like Accuracy, Precision, Recall, F1-score, area under curve-the receiver operating characteristics (AUC-ROC), and subjective evaluations of domain experts. An optimal subset of hyper-parameters for the algorithms was selected through the grid search function for the best prediction. The RF model has performed better than the rest of the algorithms by achieving an accuracy of 96.06% and is better suited as a solution model for our purpose. Following RF, LightGBM, XGBoost, and AdaBoost algorithms have an accuracy of 91.53%, 88.44%, and 58.55%, respectively. The findings suggest that some of the features like ‘Age of household head’, ‘Total children ever born’ in a family, ‘Main roof material’ of their house, ‘Region’ they lived in, whether a household uses ‘Electricity’ or not, and ‘Type of toilet facility’ of a household are determinant factors to be a focal point for economic policymakers. The determinant risk factors, extracted rules, and designed artifact achieved 82.28% of the domain expert’s evaluation. Overall, the study shows ML techniques are effective in predicting the wealth status of households.

Keywords: ensemble machine learning, households wealth status, predictive model, wealth status prediction

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Authors: Kaewkarn Phuangsombat, Arthit Phuangsombat, Anupun Terdwongworakul

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Hard seeds will not grow and can cause mold in sprouting process. Thus, the hard seeds need to be separated from the normal seeds. Near infrared hyperspectral imaging in a range of 900 to 1700 nm was implemented to develop a model by partial least squares discriminant analysis to discriminate the hard seeds from the normal seeds. The orientation of the seeds was also studied to compare the performance of the models. The model based on hilum-up orientation achieved the best result giving the coefficient of determination of 0.98, and root mean square error of prediction of 0.07 with classification accuracy was equal to 100%.

Keywords: mung bean, near infrared, germinatability, hard seed

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Authors: Sonia Ben Hamza, Sabra Habli, Nejla Mahjoub Said, Hervé Bournot, Georges Le Palec

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In this work, we determine the turbulent dynamic structure of pollutant dispersion in two-phase free surface flow. The numerical simulation was performed using ANSYS Fluent. The flow study is three-dimensional, unsteady and isothermal. The study area has been endowed with a rectangular obstacle to analyze its influence on the hydrodynamic variables and progression of the pollutant. The numerical results show that the hydrodynamic model provides prediction of the dispersion of a pollutant in an open channel flow and reproduces the recirculation and trapping the pollutant downstream near the obstacle.

Keywords: CFD, free surface, polluant dispersion, turbulent flows

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Authors: Satya Pradhan, Venky Nanniyur

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As the software industry transitions from a license-based model to a subscription-based Software-as-a-Service (SaaS) model, many software development groups are using a hybrid development model that incorporates Agile and Waterfall methodologies in different parts of the organization. The traditional metrics used for measuring software quality in Waterfall or Agile paradigms do not apply to this new hybrid methodology. In addition, to respond to higher quality demands from customers and to gain a competitive advantage in the market, many companies are starting to prioritize quality as a strategic differentiator. As a result, quality metrics are included in the decision-making activities all the way up to the executive level, including board of director reviews. This paper presents key challenges associated with measuring software quality in organizations using the hybrid development model. We introduce a framework called Prevention-Inspection-Evaluation-Removal (PIER) to provide a comprehensive metric definition for hybrid organizations. The framework includes quality measurements, quality enforcement, and quality decision points at different organizational levels and project milestones. The metrics framework defined in this paper is being used for all Cisco systems products used in customer premises. We present several field metrics for one product portfolio (enterprise networking) to show the effectiveness of the proposed measurement system. As the results show, this metrics framework has significantly improved in-process defect management as well as field quality.

Keywords: quality management system, quality metrics framework, quality metrics, agile, waterfall, hybrid development system

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Authors: Ahmet Karakuş, Akif Can Kilic, Emre Alptekin

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A growing number of studies have been conducted to determine how well-being may be predicted using well-designed models. It is necessary to investigate the backgrounds of features in order to construct a viable Subjective Well-Being (SWB) model. We have picked the suitable variables from the literature on SWB that are acceptable for real-world data instructions. The goal of this work is to evaluate the model by feeding it with SWB characteristics and then categorizing the stress levels using machine learning methods to see how well it performs on a real dataset. Despite the fact that it is a multiclass classification issue, we have achieved significant metric scores, which may be taken into account for a specific task.

Keywords: machine learning, multiclassification problem, subjective well-being, perceived stress scale

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Authors: Joseph Fitoussi, Mohammadali Shirinbayan, Abbas Tcharkhtchi

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Due to material flow during processing, short fiber reinforced composites structures obtained by injection or compression molding generally present strong spatial microstructure variation. On the other hand, quasi-static, dynamic, and fatigue behavior of these materials are highly dependent on microstructure parameters such as fiber orientation distribution. Indeed, because of complex damage mechanisms, SFRC structures design is a key challenge for safety and reliability. In this paper, we propose a micromechanical model allowing prediction of damage behavior of real structures as a function of microstructure spatial distribution. To this aim, a statistical damage criterion including strain rate and fatigue effect at the local scale is introduced into a Mori and Tanaka model. A critical local damage state is identified, allowing fatigue life prediction. Moreover, the multi-scale model is coupled with an experimental intrinsic link between damage under monotonic loading and fatigue life in order to build an abacus giving Tsai-Wu failure criterion parameters as a function of microstructure and targeted fatigue life. On the other hand, the micromechanical damage model gives access to the evolution of the anisotropic stiffness tensor of SFRC submitted to complex thermomechanical loading, including quasi-static, dynamic, and cyclic loading with temperature and amplitude variations. Then, the latter is used to fill out microstructure dependent material cards in finite element analysis for design optimization in the case of complex loading history. The proposed methodology is illustrated in the case of a real automotive component made of sheet molding compound (PSA 3008 tailgate). The obtained results emphasize how the proposed micromechanical methodology opens a new path for the automotive industry to lighten vehicle bodies and thereby save energy and reduce gas emission.

Keywords: short fiber reinforced composite, structural design, damage, micromechanical modelling, fatigue, strain rate effect

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Authors: R. B. Mishra

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Hidden Markov Model (HMM) has been used in prediction and determination of states that generate different neural activations as well as mental working conditions. This paper addresses two applications of HMM; one to determine the optimal sequence of states for two neural states: Active (AC) and Inactive (IA) for the three emission (observations) which are for No Working (NW), Waiting (WT) and Working (W) conditions of human beings. Another is for the determination of optimal sequence of intentionality i.e. Believe (B), Desire (D), and Intention (I) as the states and three observational sequences: NW, WT and W. The computational results are encouraging and useful.

Keywords: hiden markov model, believe desire intention, neural activation, simulation

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Authors: Sai Amulya Gollapalli, Dilip A. Patel, Parth Patel K., Lukman E. Mansuri

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Accumulation of moisture is one of the most worrisome aspects of a building. Architects and engineers tend to ignore its vitality during the designing and construction stage. Major fatalities in buildings can be caused by it. People avoid spending a lot of money on waterproofing. If the same mistake is repeated, no deep thinking is done. The quality of workmanship and construction is depleting due to negligence. It is important to do an analysis of the water maintenance issues happening in the current buildings and give a database for all the factors that are causing the defect. In this research, surveys are done with two waterproofing consultants, two client engineers, and two project managers. The survey was based on a matrix that was based on the causes of water maintenance issues. There were around 100 causes that were identified. The causes were categorized into six, namely, manpower, finance, method, management, environment, and material. In the matrices, the causes on the x-direction matched with the causes on the y-direction. 3 Likert scale was used to make a pairwise comparison between causes on each cell. Matrices were evaluated for the main categories and for each category separately. A final ranking was done by the weights achieved, and ‘cracks arriving from various construction joints’ was the highest with 0.57 relative significance, and ‘usage of the material’ was the lowest with 0.03 relative significance. Twelve defects due to water leakage were identified, and interviewees were asked to make a pairwise comparison of them, too, to understand the priorities. When the list of causes is achieved, the prioritization as per the stratification analysis is done. This will be beneficial to the consultants and contractors as they will get a primary idea of which causes to focus on.

Keywords: water leakage, survey, causes, matrices, prioritization

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Authors: B. Afsharipoor, E. Nazemi

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Artificial neural networks (ANNs) are powerful tool for prediction which can be trained based on a set of examples and thus, it would be useful for nonlinear image processing. The present paper reviews several paper regarding applications of ANN in image processing to shed the light on advantage and disadvantage of ANNs in this field. Different steps in the image processing chain including pre-processing, enhancement, segmentation, object recognition, image understanding and optimization by using ANN are summarized. Furthermore, results on using multi artificial neural networks are presented.

Keywords: neural networks, image processing, segmentation, object recognition, image understanding, optimization, MANN

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Authors: Ju Peng Poh, Jun Yu Charles Lee, Jonathan Chew Hoe Khoo

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The emergence of digital twin technology, a digital replica of physical world, has improved the real-time access to data from sensors about the performance of buildings. This digital transformation has opened up many opportunities to improve the management of the building by using the data collected to help monitor consumption patterns and energy leakages. One example is the integration of predictive models for anomaly detection. In this paper, we use the GAM (Generalised Additive Model) for the anomaly detection of Air Handling Units (AHU) power consumption pattern. There is ample research work on the use of GAM for the prediction of power consumption at the office building and nation-wide level. However, there is limited illustration of its anomaly detection capabilities, prescriptive analytics case study, and its integration with the latest development of digital twin technology. In this paper, we applied the general GAM modelling framework on the historical data of the AHU power consumption and cooling load of the building between Jan 2018 to Aug 2019 from an education campus in Singapore to train prediction models that, in turn, yield predicted values and ranges. The historical data are seamlessly extracted from the digital twin for modelling purposes. We enhanced the utility of the GAM model by using it to power a real-time anomaly detection system based on the forward predicted ranges. The magnitude of deviation from the upper and lower bounds of the uncertainty intervals is used to inform and identify anomalous data points, all based on historical data, without explicit intervention from domain experts. Notwithstanding, the domain expert fits in through an optional feedback loop through which iterative data cleansing is performed. After an anomalously high or low level of power consumption detected, a set of rule-based conditions are evaluated in real-time to help determine the next course of action for the facilities manager. The performance of GAM is then compared with other approaches to evaluate its effectiveness. Lastly, we discuss the successfully deployment of this approach for the detection of anomalous power consumption pattern and illustrated with real-world use cases.

Keywords: anomaly detection, digital twin, generalised additive model, GAM, power consumption, supervised learning

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Authors: Reena D. Souza, Tripti Vats, Prem F. Siril

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Esterification of free fatty acid (oleic acid) and transesterification of waste cooking oil (WCO) with ethanol over graphene oxide (GO), GO-Fe2O3, sulfonated GO (GO-SO3H), and Fe2O3/GO-SO3H catalysts were examined in the present study. Iron oxide supported graphene-based acid catalyst (Fe2O3/GO-SO3H) exhibited highest catalytic activity. GO was prepared by modified Hummer’s process. The GO-Fe2O3 nanocomposites were prepared by the addition of NaOH to a solution containing GO and FeCl3. Sulfonation was done using concentrated sulfuric acid. Transmissionelectron microscopy (TEM) and atomic force microscopy (AFM) imaging revealed the presence of Fe2O3 particles having size in the range of 50-200 nm. Crystal structure was analyzed by XRD and defect states of graphene were characterized using Raman spectroscopy. The effects of the reaction variables such as catalyst loading, ethanol to acid ratio, reaction time and temperature on the conversion of fatty acids were studied. The optimum conditions for the esterification process were molar ratio of alcohol to oleic acid at 12:1 with 5 wt% of Fe2O3/GO-SO3H at 1000C with a reaction time of 4h yielding 99% of ethyl oleate. This is because metal oxide supported solid acid catalysts have advantages of having both strong Brønsted as well as Lewis acid properties. The biodiesel obtained by transesterification of WCO was characterized by 1H NMR and Gas Chromatography techniques. XRD patterns of the recycled catalyst evidenced that the catalyst structure was unchanged up to the 5th cycle, which indicated the long life of the catalyst.

Keywords: Fe₂O₃/GO-SO₃H, Graphene Oxide, GO-Fe₂O₃, GO-SO₃H, WCO

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Authors: Sadeq Al Yaari, Ayman Al Yaari, Adham Al Yaari, Montaha Al Yaari, Aayah Al Yaari, Sajedah Al Yaari

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Objective: This research aims at investigating the research ethics in the field of science. Method: It is an exploratory research wherein the researchers attempted to cover the phenomenon at hand from all specialists’ viewpoints. Results Discussion is based upon the findings resulted from the analysis the researcher undertook. Concerning the results’ prediction, the researcher needs first to seek highly qualified people in the field of research as well as in the field of statistics who share the philosophy of the research. Then s/he should make sure that s/he is adequately trained in the specific techniques, methods and statically programs that are used at the study. S/he should also believe in continually analysis for the data in the most current methods.

Keywords: research ethics, legal, rights, psychoneurolinguistics

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Authors: Leila Keshavarz Afshar, Hedieh Sajedi

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Estimation of biological brain age from MR images is a topic that has been much addressed in recent years due to the importance it attaches to early diagnosis of diseases such as Alzheimer's. In this paper, we use a 3D Convolutional Neural Network (CNN) to provide a method for estimating the biological age of the brain. The 3D-CNN model is trained by MRI data that has been normalized. In addition, to reduce computation while saving overall performance, some effectual slices are selected for age estimation. By this method, the biological age of individuals using selected normalized data was estimated with Mean Absolute Error (MAE) of 4.82 years.

Keywords: brain age estimation, biological age, 3D-CNN, deep learning, T1-weighted image, SPM, preprocessing, MRI, canny, gray matter

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Authors: Kadda Boumediene, Mohamed Bouzit

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The prediction of the flow around marine propellers and vessel hulls propeller interaction is one of the challenges of Computational fluid dynamics (CFD). The CFD has emerged as a potential tool in recent years and has promising applications. The objective of the current study is to predict the hydrodynamic performances of HSP marine propeller in open water and behind a vessel. The unsteady 3-D flow was modeled numerically along with respectively the K-ω standard and K-ω SST turbulence models for steady and unsteady cases. The hydrodynamic performances such us a torque and thrust coefficients and efficiency show good agreement with the experiment results.

Keywords: seiun maru propeller, steady, unstead, CFD, HSP

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Authors: Minjuan Sun

Abstract:

Ever since the late 1990s, China has experienced explosive growth in consumer lending, especially in short-term consumer loans, among which, the growth rate of non-bank lending has surpassed bank lending due to the development in financial technology. On the other hand, China does not have a universal credit scoring and registration system that can guide lenders during the processes of credit evaluation and risk control, for example, an individual’s bank credit records are not available for online lenders to see and vice versa. Given this context, the purpose of this paper is three-fold. First, we explore if and how alternative digital footprint data can be utilized to assess borrower’s creditworthiness. Then, we perform a comparative analysis of machine learning methods for the canonical problem of credit default prediction. Finally, we analyze, from an institutional point of view, the necessity of establishing a viable and nationally universal credit registration and scoring system utilizing online digital footprints, so that more people in China can have better access to the consumption loan market. Two different types of digital footprint data are utilized to match with bank’s loan default records. Each separately captures distinct dimensions of a person’s characteristics, such as his shopping patterns and certain aspects of his personality or inferred demographics revealed by social media features like profile image and nickname. We find both datasets can generate either acceptable or excellent prediction results, and different types of data tend to complement each other to get better performances. Typically, the traditional types of data banks normally use like income, occupation, and credit history, update over longer cycles, hence they can’t reflect more immediate changes, like the financial status changes caused by the business crisis; whereas digital footprints can update daily, weekly, or monthly, thus capable of providing a more comprehensive profile of the borrower’s credit capabilities and risks. From the empirical and quantitative examination, we believe digital footprints can become an alternative information source for creditworthiness assessment, because of their near-universal data coverage, and because they can by and large resolve the "thin-file" issue, due to the fact that digital footprints come in much larger volume and higher frequency.

Keywords: credit score, digital footprint, Fintech, machine learning

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Authors: Xing Wang, Lin Feng, Xuan Zou, Hongchen liu

Abstract:

Immediate Implantation after tooth extraction is considered to be the ideal way to retain the alveolar bone, but some scholars believe the aesthetic effect in the Early Implantation case are more reliable. In this study, 89 patients were added to this retrospective study up to 5 years. Assessment indicators was including the survival of the implant (peri-implant infection, implant loosening, shedding, crowns and occlusal), aesthetics (color and fullness gums, papilla height, probing depth, X-ray alveolar crest height, the patient's own aesthetic satisfaction, doctors aesthetics score), repair defects around the implant (peri-implant bone changes in height and thickness, whether the use of autologous bone graft, whether to use absorption/repair manual nonabsorbable material), treatment time, cost and the use of antibiotics.The results demonstrated that there is no significant difference in long-term success rate of immediate implantation, early implantation and delayed implantation (p> 0.05). But the results indicated immediate implantation group could get get better aesthetic results after two years (p< 0.05), but may increase the risk of complications and failures (p< 0.05). High-risk indicators include gingival recession, labial bone wall damage, thin gingival biotypes, planting position and occlusal restoration bad and so on. No matter which type of implanting methods was selected, the extraction methods and bone defect amplification techniques are observed as a significant factors on aesthetic effect (p< 0.05).

Keywords: immediate implantation, long-term effects, aesthetics area, dental implants

Procedia PDF Downloads 353

Authors: John-Nelson Ekumah, Selorm Yao-Say Solomon Adade, Mingming Zhong, Yufan Sun, Qiufang Liang, Muhammad Safiullah Virk, Xorlali Nunekpeku, Nana Adwoa Nkuma Johnson, Bridget Ama Kwadzokpui, Xiaofeng Ren

Abstract:

Enhancing starch gel strength and stability is crucial. However, traditional gel property assessment methods are destructive, time-consuming, and resource-intensive. Thus, understanding ethanol treatment effects on kudzu starch gel strength and developing a rapid, nondestructive gel strength assessment method is essential for optimizing the treatment process and ensuring product quality consistency. This study investigated the effects of different ethanol concentrations on the microstructure of kudzu starch gels using a comprehensive microstructural analysis. We also developed a nondestructive method for predicting gel strength and classifying treatment levels using near-infrared (NIR) spectroscopy, and advanced data analytics. Scanning electron microscopy revealed progressive network densification and pore collapse with increasing ethanol concentration, correlating with enhanced mechanical properties. NIR spectroscopy, combined with various variable selection methods (CARS, GA, and UVE) and modeling algorithms (PLS, SVM, and ELM), was employed to develop predictive models for gel strength. The UVE-SVM model demonstrated exceptional performance, with the highest R² values (Rc = 0.9786, Rp = 0.9688) and lowest error rates (RMSEC = 6.1340, RMSEP = 6.0283). Pattern recognition algorithms (PCA, LDA, and KNN) successfully classified gels based on ethanol treatment levels, achieving near-perfect accuracy. This integrated approach provided a multiscale perspective on ethanol-induced starch gel modification, from molecular interactions to macroscopic properties. Our findings demonstrate the potential of NIR spectroscopy, coupled with advanced data analysis, as a powerful tool for rapid, nondestructive quality assessment in starch gel production. This study contributes significantly to the understanding of starch modification processes and opens new avenues for research and industrial applications in food science, pharmaceuticals, and biomaterials.

Keywords: kudzu starch gel, near-infrared spectroscopy, gel strength prediction, support vector machine, pattern recognition algorithms, ethanol treatment

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Authors: Sudha T., Naveen C.

Abstract:

Dengue is a serious public health issue that causes significant annual economic and welfare burdens on nations. However, enhanced optimization techniques and quantitative modeling approaches can predict the incidence of dengue. By advocating for a data-driven approach, public health officials can make informed decisions, thereby improving the overall effectiveness of sudden disease outbreak control efforts. The National Oceanic and Atmospheric Administration and the Centers for Disease Control and Prevention are two of the U.S. Federal Government agencies from which this study uses environmental data. Based on environmental data that describe changes in temperature, precipitation, vegetation, and other factors known to affect dengue incidence, many predictive models are constructed that use different machine learning methods to estimate weekly dengue cases. The first step involves preparing the data, which includes handling outliers and missing values to make sure the data is prepared for subsequent processing and the creation of an accurate forecasting model. In the second phase, multiple feature selection procedures are applied using various machine learning models and optimization techniques. During the third phase of the research, machine learning models like the Huber Regressor, Support Vector Machine, Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR) are compared with several optimization techniques for feature selection, such as Harmony Search and Genetic Algorithm. In the fourth stage, the model's performance is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) as assistance. Selecting an optimization strategy with the least number of errors, lowest price, biggest productivity, or maximum potential results is the goal. In a variety of industries, including engineering, science, management, mathematics, finance, and medicine, optimization is widely employed. An effective optimization method based on harmony search and an integrated genetic algorithm is introduced for input feature selection, and it shows an important improvement in the model's predictive accuracy. The predictive models with Huber Regressor as the foundation perform the best for optimization and also prediction.

Keywords: deep learning model, dengue fever, prediction, optimization

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Authors: Vaibhavi Birle

Abstract:

Posterior reversible encephalopathy syndrome is a rare clinic-radiological syndrome associated with acute changes in blood pressure during pregnancy. It is characterized symptomatically by headache, seizures, altered mental status, and visual blurring with radiological changes of white matter (vasogenic oedema) affecting the posterior occipital and parietal lobes of the brain. It is being increasingly recognized due to increased institutional deliveries and advances in imaging particularly magnetic resonance imaging (MRI). In spite of the increasing diagnosis the prediction of PRES and patient factors affecting susceptibility is still not clear. Hence, we conducted the retrospective study to analyse the factors associated with PRES at our tertiary centre.

Keywords: pres syndrome, eclampsia, maternal outcome, fetal outcome

Procedia PDF Downloads 143

Authors: Ruchika Goyal, Ashwani Kumar, Sandeep Jain

Abstract:

The experimental data for anticancer potential of curcumin nanoparticles was calculated by means of eclectic data. The optimal descriptors were examined using Monte Carlo method based CORAL SEA software. The statistical quality of the model is following: n = 14, R² = 0.6809, Q² = 0.5943, s = 0.175, MAE = 0.114, F = 26 (sub-training set), n =5, R²= 0.9529, Q² = 0.7982, s = 0.086, MAE = 0.068, F = 61, Av Rm² = 0.7601, ∆R²m = 0.0840, k = 0.9856 and kk = 1.0146 (test set) and n = 5, R² = 0.6075 (validation set). This data can be used to build predictive QSAR models for anticancer activity.

Keywords: anticancer potential, curcumin, model, nanoparticles, optimal descriptors, QSAR

Procedia PDF Downloads 314