Search results for: high dimensional data

Authors: Wei Yongxiang, Zhu Haoyue, Tang Heng, Yuan Qunwei

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In order to study the preparation method of gear fatigue load spectrum for loaders, the load signal of four typical working conditions of loader is collected. The signal that reflects the law of load change is obtained by preprocessing the original signal. The torque of the drive axle is calculated by using the rain flow counting method. According to the operating time ratio of each working condition, the two-dimensional load spectrum based on the real working conditions of the drive axle of loader is established by the cycle extrapolation and synthesis method. The two-dimensional load spectrum is converted into one-dimensional load spectrum by means of the mean of torque equal damage method. Torque amplification includes the maximum load torque of the main reduction gear. Based on the theory of equal damage, the accelerated cycles are calculated. In this way, the load spectrum of the loading condition of the drive axle is prepared to reflect loading condition of the loader. The load spectrum can provide reference for fatigue life test and life prediction of loader drive axle.

Keywords: load spectrum, axle, torque, rain-flow counting method, extrapolation

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Authors: Guram Adamashvili

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Surface plasmon polariton is a surface optical wave which undergoes a strong enhancement and spatial confinement of its wave amplitude near an interface of two-dimensional layered structures. Phosphorene (single-layer black phosphorus) and other two-dimensional anisotropic phosphorene-like materials are recognized as promising materials for potential future applications of surface plasmon polariton. A theory of an optical breather of self-induced transparency for surface plasmon polariton propagating in monolayer or few-layer phosphorene is developed. A theory of an optical soliton of self-induced transparency for surface plasmon polariton propagating in monolayer or few-layer phosphorene have been investigated earlier Starting from the optical nonlinear wave equation for surface TM-modes interacting with a two-dimensional layer of atomic systems or semiconductor quantum dots and a phosphorene monolayer (or other two-dimensional anisotropic material), we have obtained the evolution equations for the electric field of the breather. In this case, one finds that the evolution of these pulses become described by the damped Bloch-Maxwell equations. For surface plasmon polariton fields, breathers are found to occur. Explicit relations of the dependence of breathers on the local media, phosphorene anisotropic conductivity, transition layer properties and transverse structures of the SPP, are obtained and will be given. It is shown that the phosphorene conductivity reduces exponentially the amplitude of the surface breather of SIT in the process of propagation. The direction of propagation corresponding to the maximum and minimum damping of the amplitude are assigned along the armchair and zigzag directions of black phosphorus nano-film, respectively. The most rapid damping of the intensity occurs when the polarization of breather is along the armchair direction.

Keywords: breathers, nonlinear waves, solitons, surface plasmon polaritons

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Authors: L. Maiza, A. Cantagrel, M. Forestier, G. Laucoin, T. Regali

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Evaluation of High Frequency Trading (HFT) impact on financial markets is very important for traders who use market analysis to detect winning transaction opportunity. Analysis of HFT data on tobacco commodity market is discussed here and interesting linear relationship has been shown between trading frequency and difference between averaged trading prices above and below considered trading frequency. This may open new perspectives on markets data understanding and could provide possible interpretation of Adam Smith invisible hand.

Keywords: financial market, high frequency trading, analysis, impacts, Adam Smith invisible hand

Procedia PDF Downloads 359

Authors: Serges Mendomo Meye, Li Guowei, Shen Zhenzhong, Gan Lei, Xu Liqun

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Due to the characteristics of high-heap and large-volume, the complex layers of waste and the high-water level of leachate, environmental pollution, and slope instability are easily produced. It is therefore of great significance to research the heterogeneous seepage field and stability of landfills. This paper focuses on the heterogeneous characteristics of the landfill piles and analyzes the seepage field and slope stability of the landfill using statistical and numerical analysis methods. The calculated results are compared with the field measurement and literature research data to verify the reliability of the model, which may provide the basis for the design, safe, and eco-friendly operation of the landfill. The main innovations are as follows: (1) The saturated-unsaturated seepage equation of heterogeneous soil is derived theoretically. The heterogeneous landfill is regarded as composed of infinite layers of homogeneous waste, and a method for establishing the heterogeneous seepage model is proposed. Then the formation law of the stagnant water level of heterogeneous landfills is studied. It is found that the maximum stagnant water level of landfills is higher when considering the heterogeneous seepage characteristics, which harms the stability of landfills. (2) Considering the heterogeneity weight and strength characteristics of waste, a method of establishing a heterogeneous stability model is proposed, and it is extended to the three-dimensional stability study. It is found that the distribution of heterogeneous characteristics has a great influence on the stability of landfill slope. During the operation and management of the landfill, the reservoir bank should also be considered while considering the capacity of the landfill.

Keywords: heterogeneous characteristics, leachate levels, saturated-unsaturated seepage, seepage field, slope stability

Procedia PDF Downloads 251

Authors: A. D. Tayal

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The energy industry is undergoing significant disruption. This research outlines that, whilst challenging; this disruption is also an emerging opportunity for electricity utilities. One such opportunity is leveraging the developments in data analytics and machine learning. As the uptake of renewable energy technologies and complimentary control systems increases, electricity grids will likely transform towards dense microgrids with high penetration of renewable generation sources, rich in network and customer data, and linked through intelligent, wireless communications. Data digitisation and analytics have already impacted numerous industries, and its influence on the energy sector is growing, as computational capabilities increase to manage big data, and as machines develop algorithms to solve the energy challenges of the future. The objective of this paper is to address how far the uptake of renewable technologies can go given the constraints of existing grid infrastructure and provides a qualitative assessment of how higher levels of renewable energy penetration can be facilitated by incorporating even broader technological advances in the fields of data analytics and machine learning. Western Australia is used as a contextualised case study, given its abundance and diverse renewable resources (solar, wind, biomass, and wave) and isolated networks, making a high penetration of renewables a feasible target for policy makers over coming decades.

Keywords: data, innovation, renewable, solar

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Authors: H. Medoukali, B. Zegnini

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Insulation techniques in high-voltage cables rely heavily on chemically synapsed polyethylene. The latter may contain manufacturing defects such as small cavities, for example. The presence of the cavity affects the distribution of the electric field at the level of the insulating layer; this change in the electric field is affected by the presence of different space charge densities within the insulating material. This study is carried out by performing simulations to determine the distribution of the electric field inside the insulator. The simulations are based on the creation of a two-dimensional model of a high-voltage cable of 154 kV using the COMSOL Multiphysics software. Each time we study the effect of changing the space charge density of on the electromechanical Constraints.

Keywords: COMSOL multiphysics, electric field, HVDC, microcavities, space charges, XLPE

Procedia PDF Downloads 133

Authors: Itsuo Yokoyama, Rikako Kikuti, Miti Sekikawa, Tosinori Asai, Sarai Tsuyoshi

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Introduction: Vascular access for hemodialysis therapy is often difficult, even for experienced medical personnel. Ultrasound guided needle placement have been performed occasionally but is not always helpful in certain cases with complicated vascular anatomy. Obtaining precise anatomical knowledge of the vascular structure is important to prevent access-related complications. With augmented reality (AR) device such as AR glasses, the virtual vascular structure is shown superimposed on the actual patient vessels, thus enabling the operator to maneuver catheter placement easily with free both hands. We herein report our method of AR guided vascular access method in dialysis treatment Methods: Three dimensional (3D) object of the arm with arteriovenous fistula is computer graphically created with 3D software from the data obtained by computer tomography, ultrasound echogram, and image scanner. The 3D vascular object thus created is viewed on the screen of the AR digital display device (such as AR glass or iPad). The picture of the vascular anatomical structure becomes visible, which is superimposed over the real patient’s arm, thereby the needle insertion be performed under the guidance of AR visualization with ease. By this method, technical difficulty in catheter placement for dialysis can be lessened and performed safely. Considerations: Virtual reality technology has been applied in various fields and medical use is not an exception. Yet AR devices have not been widely used among medical professions. Visualization of the virtual vascular object can be achieved by creation of accurate three dimensional object with the help of computer graphical technique. Although our experience is limited, this method is applicable with relative easiness and our accumulating evidence has suggested that our method of vascular access with the use of AR can be promising.

Keywords: abdominal-aorta, calcification, extraskeletal, dialysis, computer graphics, 3DCG, CT, calcium, phosphorus

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Authors: Thrivikraman Aswathi, S. Advaith

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As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data.

Keywords: GAN, transformer, classification, multivariate time series

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Authors: T. B. Karu Jayasundara

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Regions of low gravity and gravity anomalies both influence heavy mineral concentrations on placer deposits. Economically imported heavy minerals are likely to have higher levels of deposition in low gravity regions of placer deposits. This can be found in coastal regions of Southern Asia, particularly in Sri Lanka and Peninsula India and areas located in the lowest gravity region of the world. The area about 70 kilometers of the east coast of Sri Lanka is covered by a high percentage of ilmenite deposits, and the southwest coast of the island consists of Monazite placer deposit. These deposits are one of the largest placer deposits in the world. In India, the heavy mineral industry has a good market. On the other hand, based on the coastal placer deposits recorded, the high gravity region located around Papua New Guinea, has no such heavy mineral deposits. In low gravity regions, with the help of other depositional environmental factors, the grains have more time and space to float in the sea, this helps bring high concentrations of heavy mineral deposits to the coast. The effect of low and high gravity can be demonstrated by using heavy mineral separation devices.  The Wilfley heavy mineral separating table is one of these; it is extensively used in industries and in laboratories for heavy mineral separation. The horizontally oscillating Wilfley table helps to separate heavy and light mineral grains in to deferent fractions, with the use of water. In this experiment, the low and high angle of the Wilfley table are representing low and high gravity respectively. A sample mixture of grain size <0.85 mm of heavy and light mineral grains has been used for this experiment. The high and low angle of the table was 6⁰ and 2⁰ respectively for this experiment. The separated fractions from the table are again separated into heavy and light minerals, with the use of heavy liquid, which consists of a specific gravity of 2.85. The fractions of separated heavy and light minerals have been used for drawing the two-dimensional graphs. The graphs show that the low gravity stage has a high percentage of heavy minerals collected in the upper area of the table than in the high gravity stage. The results of the experiment can be used for the comparison of regional low gravity and high gravity levels of heavy minerals. If there are any heavy mineral deposits in the high gravity regions, these deposits will take place far away from the coast, within the continental shelf.

Keywords: anomaly, gravity, influence, mineral

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Authors: Reza Mohammadzadeh

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The major challenges in geotechnical engineering in underground spaces arise from uncertainties and different probabilities. The collection, collation, and collaboration of existing data to incorporate them in analysis and design for given prospect evaluation would be a reliable, practical problem solving method under uncertainty. Machine learning (ML) is a subfield of artificial intelligence in statistical science which applies different techniques (e.g., Regression, neural networks, support vector machines, decision trees, random forests, genetic programming, etc.) on data to automatically learn and improve from them without being explicitly programmed and make decisions and predictions. In this paper, a conceptual database schema of geotechnical risks in underground coal mining based on a cloud system architecture has been designed. A new approach of risk assessment using a three-dimensional risk matrix supported by the level of knowledge (LoK) has been proposed in this model. Subsequently, the model workflow methodology stages have been described. In order to train data and LoK models deployment, an ML platform has been implemented. IBM Watson Studio, as a leading data science tool and data-driven cloud integration ML platform, is employed in this study. As a Use case, a data set of geotechnical hazards and risk assessment in underground coal mining were prepared to demonstrate the performance of the model, and accordingly, the results have been outlined.

Keywords: data model, geotechnical risks, machine learning, underground coal mining

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Authors: Abdullah Bal

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This paper presents a one-class classification (OCC) technique based on Fukunaga-Koontz Transform (FKT) for binary classification problems. The FKT is originally a powerful tool to feature selection and ordering for two-class problems. To utilize the standard FKT for data domain description problem (i.e., one-class classification), in this paper, a set of non-class samples which exist outside of positive class (target class) describing boundary formed with limited training data has been constructed synthetically. The tunnel-like decision boundary around upper and lower border of target class samples has been designed using statistical properties of feature vectors belonging to the training data. To capture higher order of statistics of data and increase discrimination ability, the proposed method, termed one-class FKT (OC-FKT), has been extended to its nonlinear version via kernel machines and referred as OC-KFKT for short. Multiple kernel learning (MKL) is a favorable family of machine learning such that tries to find an optimal combination of a set of sub-kernels to achieve a better result. However, the discriminative ability of some of the base kernels may be low and the OC-KFKT designed by this type of kernels leads to unsatisfactory classification performance. To address this problem, the quality of sub-kernels should be evaluated, and the weak kernels must be discarded before the final decision making process. MKL/OC-FKT and selective MKL/OC-FKT frameworks have been designed stimulated by ensemble learning (EL) to weight and then select the sub-classifiers using the discriminability and diversities measured by eigenvalue ratios. The eigenvalue ratios have been assessed based on their regions on the FKT subspaces. The comparative experiments, performed on various low and high dimensional data, against state-of-the-art algorithms confirm the effectiveness of our techniques, especially in case of small sample size (SSS) conditions.

Keywords: ensemble methods, fukunaga-koontz transform, kernel-based methods, multiple kernel learning, one-class classification

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Authors: Mansour Mohammadpour

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Data transformation refers to the modification of any point in a data set by a mathematical function. When applying transformations, the measurement scale of the data is modified. Data transformations are commonly employed to turn data into the appropriate form, which can serve various functions in the quantitative analysis of the data. This study addresses the investigation of the use of data transformations in Data Envelopment Analysis (DEA). Although data transformations are important options for analysis, they do fundamentally alter the nature of the variable, making the interpretation of the results somewhat more complex.

Keywords: data transformation, data envelopment analysis, undesirable data, negative data

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Authors: Ahmad Shayeq Azizi, Yuji Toda

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In Afghanistan, floods are the most frequent and recurrent events among other natural disasters. On the other hand, lack of monitoring data is a severe problem, which increases the difficulty of making the appropriate flood countermeasures of flood forecasting. This study is carried out to simulate the flood inundation in Harirud River Basin by application of distributed hydrological model, Integrated Flood Analysis System (IFAS) and 2D hydrodynamic model, International River Interface Cooperative (iRIC) based on satellite rainfall combined with historical peak discharge and global accessed data. The results of the simulation can predict the inundation area, depth and velocity, and the hardware countermeasures such as the impact of levee installation can be discussed by using the present method. The methodology proposed in this study is suitable for the area where hydrological and geographical data including river survey data are poorly observed.

Keywords: distributed hydrological model, flood inundation, hydrodynamic model, ungauged basins

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Authors: Ginger Egberts, Marianne Schaaphok, Fred Vermolen, Paul van Zuijlen

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A few years ago, a promising morphoelastic model was developed for the simulation of contraction formation after burn injuries. Contraction can lead to a serious reduction in physical mobility, like a reduction in the range-of-motion of joints. If this is the case in a healing burn wound, then this is referred to as a contracture that needs medical intervention. The morphoelastic model consists of a set of partial differential equations describing both a chemical part and a mechanical part in dermal wound healing. These equations are solved with the numerical finite element method (FEM). In this method, many calculations are required on each of the chosen elements. In general, the more elements, the more accurate the solution. However, the number of elements increases rapidly if simulations are performed in 2D and 3D. In that case, it not only takes longer before a prediction is available, the computation also becomes more expensive. It is therefore important to investigate alternative possibilities to generate the same results, based on the input parameters only. In this study, a surrogate neural network has been designed to mimic the results of the one-dimensional morphoelastic model. The neural network generates predictions quickly, is easy to implement, and there is freedom in the choice of input and output. Because a neural network requires extensive training and a data set, it is ideal that the one-dimensional FEM code generates output quickly. These feed-forward-type neural network results are very promising. Not only can the network give faster predictions, but it also has a performance of over 99%. It reports on the relative surface area of the wound/scar, the total strain energy density, and the evolutions of the densities of the chemicals and mechanics. It is, therefore, interesting to investigate the applicability of a neural network for the two- and three-dimensional morphoelastic model for contraction after burn injuries.

Keywords: biomechanics, burns, feasibility, feed-forward NN, morphoelasticity, neural network, relative surface area wound

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Authors: N. E.Sam, S. R.Singh

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Pipeline infrastructure are a valuable asset to the country that help in transporting fluid and gas from one place to another and contribute in keeping the country functioning both physically and economically. During seismic activity, additional loads are acted on the buried pipelines becoming a salient parameter to be studied in soil pipe interaction. Winkler Beam Theory is a commonly used approach for design of underground buried structures however this theory does not take into account shear and dynamic loading parameters in consideration. Shear can be addressed in Pasternak Theory – an improved model of Winkler Theory. However dynamic loading condition and horizontal displacement is not considered in either method. A comprehensive critical review between Winkler Beam Method, Pasternak Method and Three-Dimensional Method in finite element analysis is to be done in this paper for seismic forces. Study of the influence of depth and displacement of soil in correspondence to stiffness value and influence of horizontal displacement for design of underground structures is considered.

Keywords: finite element, pasternak theory, seismic, soil-structure interaction, three-dimensional theory, winkler theory

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Authors: Yang Yang, Dan Liu

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Anomaly detection models for network flow based on machine learning have poor detection performance under extremely unbalanced training data conditions and also have slow detection speed and large resource consumption when deploying on network edge devices. Embedding multi-teacher knowledge distillation (mKD) in anomaly detection can transfer knowledge from multiple teacher models to a single model. Inspired by this, we proposed a state-of-the-art model, mKDNAD, to improve detection performance. mKDNAD mine and integrate the knowledge of one-dimensional sequence and two-dimensional image implicit in network flow to improve the detection accuracy of small sample classes. The multi-teacher knowledge distillation method guides the train of the student model, thus speeding up the model's detection speed and reducing the number of model parameters. Experiments in the CICIDS2017 dataset verify the improvements of our method in the detection speed and the detection accuracy in dealing with the small sample classes.

Keywords: network flow anomaly detection (NAD), multi-teacher knowledge distillation, machine learning, deep learning

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Authors: Ajish Sreedharan

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With the fast evolution of digital data exchange, security information becomes much important in data storage and transmission. Due to the increasing use of images in industrial process, it is essential to protect the confidential image data from unauthorized access. As encryption process is applied to the whole image in AES ,it is difficult to improve the efficiency. In this paper, wavelet decomposition is used to concentrate the main information of image to the low frequency part. Then, AES encryption is applied to the low frequency part. The high frequency parts are XORed with the encrypted low frequency part and a wavelet reconstruction is applied. Theoretical analysis and experimental results show that the proposed algorithm has high efficiency, and satisfied security suits for image data transmission.

Keywords: discrete wavelet transforms, AES, dynamic SBox

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Authors: M. R. Daneshgar, S. E. Habibi, E. Daneshgar, A. Daneshgar

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In this paper, a two-dimensional method is developed to simulate the fillet welds in a stiffened cylindrical shell, using finite element method. The stiffener material is aluminum 2519. The thermo-elasto-plastic analysis is used to analyze the thermo-mechanical behavior. Due to the high heat flux rate of the welding process, two uncouple thermal and mechanical analysis are carried out instead of performing a single couple thermo-mechanical simulation. In order to investigate the effects of the welding procedures, two different welding techniques are examined. The resulted residual stresses and distortions due to different welding procedures are obtained. Furthermore, this study employed the technique of element birth and death to simulate the weld filler variation with time in fillet welds. The obtained results are in good agreement with the published experimental and three-dimensional numerical simulation results. Therefore, the proposed 2D modeling technique can effectively give the corresponding results of 3D models. Furthermore, by inspection of the obtained residual hoop and transverse stresses and angular distortions, proper welding procedure is suggested.

Keywords: stiffened cylindrical shell, fillet welds, residual stress, angular distortion, finite element method

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Authors: M. Mokhber, J. Hedayati

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From the managerial perspective, it is important to devise an operational plan based on top priorities due to limited resources, diversity of measures and high costs needed to improve safety in infrastructure. Dealing with the high-risk corridors across Iran, this study prioritized the corridors according to statistical data on accidents involving fatalities, injury or damage over three consecutive years. In collaboration with the Iranian Police Department, data were collected and modified. Then, the prioritization criteria were specified based on the expertise opinions and international standards. In this study, the prioritization criteria included accident severity and accident density. Finally, the criteria were standardized and weighted (equal weights) to score each high-risk corridor. The prioritization phase involved the scoring and weighting procedure. The high-risk corridors were divided into twelve groups out of 50. The results of data analysis for a three-year span suggested that the first three groups (150 corridors) along with a quarter of Iranian road network length account for nearly 60% of traffic accidents. In the next step, according to variables including weather conditions particular roads for the purpose of winter safety measures were extracted from the abovementioned categories. According to the results ranking, ‎‏9‏‎ roads with the overall ‎length of about ‎‎‏1000‏‎ Km of high-risk corridors are considered as preferences of ‎safety measures‎.

Keywords: high-risk corridors, HRCs, road safety rating, road scoring, winter safety measures

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Authors: M. N. Islam, B. Boswell, Y. R. Ginting

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The influence of canned cycles and cutting parameters on hole quality in cryogenic drilling has been investigated experimentally and analytically. A three-level, three-parameter experiment was conducted by using the design-of-experiment methodology. The three levels of independent input parameters were the following: for canned cycles—a chip-breaking canned cycle (G73), a spot drilling canned cycle (G81), and a deep hole canned cycle (G83); for feed rates—0.2, 0.3, and 0.4 mm/rev; and for cutting speeds—60, 75, and 100 m/min. The selected work and tool materials were aluminum 6061-6T and high-speed steel (HSS), respectively. For cryogenic cooling, liquid nitrogen (LN2) was used and was applied externally. The measured output parameters were the three widely used quality characteristics of drilled holes—diameter error, circularity, and surface roughness. Pareto ANOVA was applied for analyzing the results. The findings revealed that the canned cycle has a significant effect on diameter error (contribution ratio 44.09%) and small effects on circularity and surface finish (contribution ratio 7.25% and 6.60%, respectively). The best results for the dimensional accuracy and surface roughness were achieved by G81. G73 produced the best circularity results; however, for dimensional accuracy, it was the worst level.

Keywords: circularity, diameter error, drilling canned cycle, pareto ANOVA, surface roughness

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Authors: S. Sadegzadeh, A. Mousavi

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Temperature-dependent tunable photonic crystals have attracted widespread interest in recent years. In this research, transmission characteristics of a one-dimensional photonic crystal structure with a single defect have been studied. Here, we assume two different defect layers: InSb as a semiconducting layer and HgBa₂Ca₂Cu₃O₁₀ as a high-temperature superconducting layer. Both the defect layers have temperature-dependent refractive indexes. Two different types of dielectric materials (Si as a high-refractive index dielectric and MgF₂ as a low-refractive index dielectric) are used to construct the asymmetric structures (Si/MgF₂)^NInSb(Si/MgF₂)^N named S.I, and (Si/MgF₂)^NHgBa₂Ca₂Cu₃O₁₀(Si/MgF₂)^N named S.II. It is found that in response to the temperature changes, transmission peaks within the photonic band gap of the S.II structure, in contrast to S.I, show a small wavelength shift. Furthermore, the results show that under the same conditions, S.I structure generates an extra defect mode in the transmission spectra. Besides high efficiency transmission property of S.II structure, it can be concluded that the semiconductor-dielectric photonic crystals are more sensitive to temperature variation than superconductor types.

Keywords: defect modes, photonic crystals, semiconductor, superconductor, transmission

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Authors: Ahmed Mohammed Alsalama

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Recommendation systems are widely used in e-commerce applications. The engine of a current recommendation system recommends items to a particular user based on user preferences and previous high ratings. Various recommendation schemes such as collaborative filtering and content-based approaches are used to build a recommendation system. Most of the current recommendation systems were developed to fit a certain domain such as books, articles, and movies. We propose a hybrid framework recommendation system to be applied on two-dimensional spaces (User x Item) with a large number of Users and a small number of Items. Moreover, our proposed framework makes use of both favorite and non-favorite items of a particular user. The proposed framework is built upon the integration of association rules mining and the content-based approach. The results of experiments show that our proposed framework can provide accurate recommendations to users.

Keywords: data mining, association rules, recommendation systems, hybrid systems

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Authors: Maged Assem Soliman Mossallam

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The conversion of solar thruster model to an upper stage hydrogen rocket is considered. Solar thruster categorization limits its capabilities to low and moderate thrust system with high specific impulse. The current study proposes a different concept for such systems by increasing the thrust which enables using as an upper stage rocket and for future launching purposes. A computational model for the thruster is discussed for solar thruster subsystems. The first module depends on ray tracing technique to determine the intercepted solar power by the hydrogen combustion chamber. The cavity receiver is modeled using finite volume technique. The final module imports the heated hydrogen properties to the nozzle using quasi one dimensional simulation. The probability of shock waves formulation inside the nozzle is almost diminished as the outlet pressure in space environment tends to zero. The computational model relates the high thrust hydrogen rocket conversion to the design parameters and operating conditions of the thruster. Three different designs for solar thruster systems are discussed. The first design is a low thrust high specific impulse design that produces about 10 Newton of thrust .The second one output thrust is about 250 Newton and the third design produces about 1000 Newton.

Keywords: space propulsion, hydrogen rocket, thrust, specific impulse

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Authors: Douglas A. Menezes, Isabel D. Nunes, Ulrich Schiel

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Most Virtual Learning Environments do not provide support mechanisms for the integrated planning, construction and follow-up of Instructional Design supported by Learning Analytic results. The present work aims to present an authoring tool that will be responsible for constructing the structure of an Instructional Design (ID), without the data being altered during the execution of the course. The visual interface aims to present the critical situations present in this ID, serving as a support tool for the course follow-up and possible improvements, which can be made during its execution or in the planning of a new edition of this course. The model for the ID is based on High-Level Petri Nets and the visualization forms are determined by the specific kind of the data generated by an e-course, a population of students generating sequentially dependent data.

Keywords: educational data visualization, high-level petri nets, instructional design, learning analytics

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Authors: Xin Li, Ji-Yang Yu, Yue-Hua Niu, Lu-Yuan Wang

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As an inevitable chain of the space data acquirement system, space-borne storage system based on Nand Flash has gradually been implemented in spacecraft. In face of massive, parallel and varied data on board, efficient data management become an important issue of storage research. Face to the requirements of high-performance and reliability in Nand Flash storage system, a combination of hardware and file system design can drastically increase system dependability, even for missions with a very long duration. More sophisticated flash storage concepts with advanced operating systems have been researched to improve the reliability of Nand Flash storage system on satellites. In this paper, architecture of file system with multi-channel data acquisition and storage on board is proposed, which obtains large-capacity and high-performance with the combine of intra-channel pipeline and device partition in Nand Flash. Multi-channel data in different rate are stored as independent files with parallel-storage system in device partition, which assures the high-effective and reliable throughput of file treatments. For massive and high-speed data storage, an efficiency assessment model is established to calculate the bandwidth formula of intra-channel pipeline. Information tables designed in Magnetoresistive RAM (MRAM) hold the management of bad block in Nand Flash and the arrangement of file system address for the high-reliability of data storage. During the full-load test, the throughput of 3D PLUS Module 160Gb Nand Flash can reach 120Mbps for store and reach 120Mbps for playback, which efficiently satisfies the requirement of multi-channel data acquisition in Satellite. Compared with previous literature, the results of experiments verify the advantages of the proposed system.

Keywords: device partition architecture, intra-channel pipelining, nand flash, parallel storage

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Authors: Ruzanna Muradyan

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Medical data mining is a crucial field in contemporary healthcare that offers cutting-edge tactics with enormous potential to transform patient care. This abstract examines how sophisticated data mining techniques could transform the healthcare industry, with a special focus on how they might improve patient outcomes. Healthcare data repositories have dynamically evolved, producing a rich tapestry of different, multi-dimensional information that includes genetic profiles, lifestyle markers, electronic health records, and more. By utilizing data mining techniques inside this vast library, a variety of prospects for precision medicine, predictive analytics, and insight production become visible. Predictive modeling for illness prediction, risk stratification, and therapy efficacy evaluations are important points of focus. Healthcare providers may use this abundance of data to tailor treatment plans, identify high-risk patient populations, and forecast disease trajectories by applying machine learning algorithms and predictive analytics. Better patient outcomes, more efficient use of resources, and early treatments are made possible by this proactive strategy. Furthermore, data mining techniques act as catalysts to reveal complex relationships between apparently unrelated data pieces, providing enhanced insights into the cause of disease, genetic susceptibilities, and environmental factors. Healthcare practitioners can get practical insights that guide disease prevention, customized patient counseling, and focused therapies by analyzing these associations. The abstract explores the problems and ethical issues that come with using data mining techniques in the healthcare industry. In order to properly use these approaches, it is essential to find a balance between data privacy, security issues, and the interpretability of complex models. Finally, this abstract demonstrates the revolutionary power of modern data mining methodologies in transforming the healthcare sector. Healthcare practitioners and researchers can uncover unique insights, enhance clinical decision-making, and ultimately elevate patient care to unprecedented levels of precision and efficacy by employing cutting-edge methodologies.

Keywords: data mining, healthcare, patient care, predictive analytics, precision medicine, electronic health records, machine learning, predictive modeling, disease prognosis, risk stratification, treatment efficacy, genetic profiles, precision health

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Authors: Richard Pincak

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Starting from a 4D spacetime model using a partially negative dimensional product manifold (PNDP-manifold), which emerges as a 2D spacetime, we developed an analysis of tidal forces and Hawking radiation near the event horizon of a Schwarzchild black hole. The modified 2D metric, incorporating the effects of the four-dimensional Weyl tensor, with the dilatonic field and the newly derived time relation \(2\alpha t = \ln \epsilon\), can enable a deeper understanding of quantum gravity. The analysis shows how the modified Hawking temperature and distribution of emitted particles are affected by additional fields, providing potential observables for future experiments.

Keywords: black holes, Hawking radiation, Weyl tensor, information paradox

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Authors: Fatemah A. Alqallaf, Debasis Kundu

Abstract:

The aim of this paper is to introduce a bivariate inverse generalized exponential distribution which has a singular component. The proposed bivariate distribution can be used when the marginals have heavy-tailed distributions, and they have non-monotone hazard functions. Due to the presence of the singular component, it can be used quite effectively when there are ties in the data. Since it has four parameters, it is a very flexible bivariate distribution, and it can be used quite effectively for analyzing various bivariate data sets. Several dependency properties and dependency measures have been obtained. The maximum likelihood estimators cannot be obtained in closed form, and it involves solving a four-dimensional optimization problem. To avoid that, we have proposed to use an EM algorithm, and it involves solving only one non-linear equation at each `E'-step. Hence, the implementation of the proposed EM algorithm is very straight forward in practice. Extensive simulation experiments and the analysis of one data set have been performed. We have observed that the proposed bivariate inverse generalized exponential distribution can be used for modeling dependent competing risks data. One data set has been analyzed to show the effectiveness of the proposed model.

Keywords: Block and Basu bivariate distributions, competing risks, EM algorithm, Marshall-Olkin bivariate exponential distribution, maximum likelihood estimators

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Authors: Achut Manandhar, Kenneth D. Morton, Peter A. Torrione, Leslie M. Collins

Abstract:

The current trends in affect recognition research are to consider continuous observations from spontaneous natural interactions in people using multiple feature modalities, and to represent affect in terms of continuous dimensions, incorporate spatio-temporal correlation among affect dimensions, and provide fast affect predictions. These research efforts have been propelled by a growing effort to develop affect recognition system that can be implemented to enable seamless real-time human-computer interaction in a wide variety of applications. Motivated by these desired attributes of an affect recognition system, in this work a multi-dimensional affect prediction approach is proposed by integrating multivariate Relevance Vector Machine (MVRVM) with a recently developed Output-associative Relevance Vector Machine (OARVM) approach. The resulting approach can provide fast continuous affect predictions by jointly modeling the multiple affect dimensions and their correlations. Experiments on the RECOLA database show that the proposed approach performs competitively with the OARVM while providing faster predictions during testing.

Keywords: dimensional affect prediction, output-associative RVM, multivariate regression, fast testing

Procedia PDF Downloads 286

Authors: P. Behera, K. K. Singh, D. K. Saini, M. De

Abstract:

Implementation of 2D materials in the detection of bio analytes is highly advantageous in the field of sensing because of its high surface to volume ratio. We have designed our sensor array with different cationic two-dimensional MoS₂, where surface modification was achieved by cationic thiol ligands with different functionality. Green fluorescent protein (GFP) was chosen as signal transducers for its biocompatibility and anionic nature, which can bind to the cationic MoS₂ surface easily, followed by fluorescence quenching. The addition of bio-analyte to the sensor can decomplex the cationic MoS₂ and GFP conjugates, followed by the regeneration of GFP fluorescence. The fluorescence response pattern belongs to various analytes collected and transformed to linear discriminant analysis (LDA) for classification. At first, 15 different proteins having wide range of molecular weight and isoelectric points were successfully discriminated at 50 nM with detection limit of 1 nM. The sensor system was also executed in biofluids such as serum, where 10 different proteins at 2.5 μM were well separated. After successful discrimination of protein analytes, the sensor array was implemented for bacteria sensing. Six different bacteria were successfully classified at OD = 0.05 with a detection limit corresponding to OD = 0.005. The optimized sensor array was able to classify uropathogens from non-uropathogens in urine medium. Further, the technique was applied for discrimination of bacteria possessing resistance to different types and amounts of drugs. We found out the mechanism of sensing through optical and electrodynamic studies, which indicates the interaction between bacteria with the sensor system was mainly due to electrostatic force of interactions, but the separation of native bacteria from their drug resistant variant was due to Van der Waals forces. There are two ways bacteria can be detected, i.e., through bacterial cells and lysates. The bacterial lysates contain intracellular information and also safe to analysis as it does not contain live cells. Lysates of different drug resistant bacteria were patterned effectively from the native strain. From unknown sample analysis, we found that discrimination of bacterial cells is more sensitive than that of lysates. But the analyst can prefer bacterial lysates over live cells for safer analysis.

Keywords: array-based sensing, drug resistant bacteria, linear discriminant analysis, two-dimensional MoS₂

Procedia PDF Downloads 143