Search results for: continuous time domain estimation

Authors: Sara Arafeh, Kovriguine Evguine

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Cytochromes P450 are enzymes that require a supply of electrons to catalyze the synthesis of steroid hormones, fatty acids, and prostaglandin hormone. Cytochrome P450 Oxidoreductase (CYPOR), a membrane bound enzyme, provides these electrons in its open conformation. CYPOR has two cytosolic domains (FAD domain and FMN domain) and an N-terminal in the membrane. In its open conformation, electrons flow from NADPH, FAD, and finally to FMN where cytochrome P450 picks up these electrons. In the closed conformation, cytochrome P450 does not bind to the FMN domain to take the electrons. It was found that when the cytosolic domains are isolated, CYPOR could not bind to cytochrome P450. This suggested that the membrane environment is important for CYPOR function. This project takes the initiative to better understand the dynamics of CYPOR in its full length. Here, we determine the distance between specific sites in the FAD and FMN binding domains in CYPOR by Forster Resonance Energy Transfer (FRET) and Ultrafast TA spectroscopy with and without NADPH. The approach to determine these distances will rely on labeling these sites with red and infrared fluorophores. Mimic membrane attachment is done by inserting CYPOR in lipid nanodiscs. By determining the distances between the donor-acceptor sites in these domains, we can observe the open/closed conformations upon reducing CYPOR in the presence and absence of cytochrome P450. Such study is important to better understand CYPOR mechanism of action in various endosomal membranes including hepatic CYPOR which is vital in plasma cholesterol homeostasis. By investigating the conformational cycles of CYPOR, we can synthesize drugs that would be more efficient in affecting the steroid hormonal levels and metabolism of toxins catalyzed by Cytochrome P450.

Keywords: conformational cycle of CYPOR, cytochrome P450, cytochrome P450 oxidoreductase, FAD domain, FMN domain, FRET, Ultrafast TA Spectroscopy

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Authors: Alexandre Marin, Alexandra Bac, Laurent Astart

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New numerical methods for PDE resolution (such as Finite Volumes (FV) or Virtual Elements Method (VEM)) open new needs in terms of meshing of domains of interest, and in particular, polyhedral meshes have many advantages. One way to build such meshes consists of constructing Restricted Voronoi Diagrams (RVDs) whose boundaries respect the domain of interest. By minimizing a function defined for RVDs, the shapes of cells can be controlled, e.g., elongated according to user-defined directions or adjusted to comply with given aspect ratios (anisotropy) and density variations. In this paper, our contribution is threefold: First, we introduce a new gradient formula for the Voronoi tessellation energy under a continuous anisotropy field. Second, we describe a meshing algorithm based on the optimisation of this function that we validate against state-of-the-art approaches. Finally, we propose a hierarchical approach to speed up our meshing algorithm.

Keywords: anisotropic Voronoi diagrams, meshes for numerical simulations, optimisation, volumic polyhedral meshing

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Authors: T. N. Mac, B. Shahbodaghkhan, N. Khalili

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A new elastic-viscoplastic (EVP) constitutive model is proposed for the analysis of time-dependent behavior of clay. The proposed model is based on the bounding surface plasticity and the concept of viscoplastic consistency framework to establish continuous transition from plasticity to rate dependent viscoplasticity. Unlike the overstress based models, this model will meet the consistency condition in formulating the constitutive equation for EVP model. The procedure of deriving the constitutive relationship is also presented. Simulation results and comparisons with experimental data are then presented to demonstrate the performance of the model.

Keywords: bounding surface, consistency theory, constitutive model, viscosity

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Authors: Tugba Bayoglu

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Aerodynamic parameter estimation is very crucial in missile design phase, since accurate high fidelity aerodynamic model is required for designing high performance and robust control system, developing high fidelity flight simulations and verification of computational and wind tunnel test results. However, in literature, there is not enough missile aerodynamic parameter identification study for three main reasons: (1) most air to air missiles cannot fly with constant speed, (2) missile flight test number and flight duration are much less than that of fixed wing aircraft, (3) variation of the missile aerodynamic parameters with respect to Mach number is higher than that of fixed wing aircraft. In addition to these challenges, identification of aerodynamic parameters for high wind angles by using classical estimation techniques brings another difficulty in the estimation process. The reason for this, most of the estimation techniques require employing polynomials or splines to model the behavior of the aerodynamics. However, for the missiles with a large variation of aerodynamic parameters with respect to flight variables, the order of the proposed model increases, which brings computational burden and complexity. Therefore, in this study, it is aimed to solve nonlinear aerodynamic parameter identification problem for a supersonic air to air missile by using Artificial Neural Networks. The method proposed will be tested by using simulated data which will be generated with a six degree of freedom missile model, involving a nonlinear aerodynamic database. The data will be corrupted by adding noise to the measurement model. Then, by using the flight variables and measurements, the parameters will be estimated. Finally, the prediction accuracy will be investigated.

Keywords: air to air missile, artificial neural networks, open loop simulation, parameter identification

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Authors: Elke Mackensen

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Systems Engineering plays a key role during industrial product development of complex technical systems. The need for systems engineers in industry is growing. However, there is a gap between the industrial need and the academic education. Normally the academic education is focused on the domain specific design, implementation and testing of technical systems. Necessary systems engineering expertise like knowledge about requirements analysis, product cost estimation, management or social skills are poorly taught. Thus, there is the need of new academic concepts for teaching systems engineering skills. This paper presents a project-orientated training concept to prepare students from different technical degree programs for systems engineering activities. The training concept has been initially implemented and applied in the industrial engineering master program of the University of Applied Sciences Offenburg.

Keywords: educational systems engineering training, requirements analysis, system modelling, SysML

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Authors: Somayeh Komeylian

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Array signal processing techniques have been recently developing in a variety application of the performance enhancement of receivers by refraining the power of jamming and interference signals. In this scenario, biases induced to the antenna array receiver degrade significantly the accurate estimation of the carrier phase. Owing to the integration of frequency becomes the carrier phase, we have obtained the unbiased doppler frequency for the high precision estimation of carrier phase. The unbiased characteristic of Doppler frequency to the power jamming and the other interference signals allows achieving the highly accurate estimation of phase carrier. In this study, we have rigorously investigated the unbiased characteristic of Doppler frequency to the variation of the antenna array geometries. The simulation results have efficiently verified that the Doppler frequency remains also unbiased and accurate to the variation of antenna array geometries.

Keywords: array signal processing, unbiased doppler frequency, GNSS, carrier phase, and slowly fluctuating point target

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Authors: Yingqi Cui, Changran Huang, Raymond Lee

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In this paper, we proposed an AI Tutor using ontology and natural language process techniques to generate a computer science domain knowledge graph and answer users’ questions based on the knowledge graph. We define eight types of relation to extract relationships between entities according to the computer science domain text. The AI tutor is separated into two agents: learning agent and Question-Answer (QA) agent and developed on JADE (a multi-agent system) platform. The learning agent is responsible for reading text to extract information and generate a corresponding knowledge graph by defined patterns. The QA agent can understand the users’ questions and answer humans’ questions based on the knowledge graph generated by the learning agent.

Keywords: artificial intelligence, natural Language processing, knowledge graph, intelligent agents, QA system

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Authors: Alam Ali, Ashok Kumar Pathak

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

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

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Authors: Mohamed Amhal, Jose Sayritupac

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Photovoltaic (PV) systems have risen as one of the modern renewable energy sources that are used in wide ranges to produce electricity and deliver it to the electrical grid. In parallel, monitoring systems have been deployed as a key element to track the energy production and to forecast the total production for the next days. The reliability of the PV energy production has become a crucial point in the analysis of PV systems. A deeper understanding of each phenomenon that causes a gain or a loss of energy is needed to better design, operate and maintain the PV systems. This work analyzes the current losses distribution in PV systems starting from the available solar energy, going through the DC side and AC side, to the delivery point. Most of the phenomena linked to energy losses and gains are considered and modeled, based on real time monitoring data and datasheets of the PV system components. An analysis of the order of magnitude of each loss is compared to the current literature and commercial software. To date, the analysis of PV systems performance based on a breakdown structure of energy losses and gains is not covered enough in the literature, except in some software where the concept is very common. The cutting-edge of the current analysis is the implementation of software tools for energy losses estimation in PV systems based on several energy losses definitions and estimation technics. The developed tools have been validated and tested on some PV plants in France, which are operating for years. Among the major findings of the current study: First, PV plants in France show very low rates of soiling and aging. Second, the distribution of other losses is comparable to the literature. Third, all losses reported are correlated to operational and environmental conditions. For future work, an extended analysis on further PV plants in France and abroad will be performed.

Keywords: energy gains, energy losses, losses distribution, monitoring, photovoltaic, photovoltaic systems

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Authors: Kamal Amarouche, Houda Benbrahim, Ismail Kassou

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Nowadays, e-commerce shopping websites have experienced noticeable growth. These websites have gained consumers’ trust. After purchasing a product, many consumers share comments where opinions are usually embedded about the given product. Research on the automatic management of opinions that gives suggestions to potential consumers and portrays an image of the product to manufactures has been growing recently. After launching the product in the market, the reviews generated around it do not usually contain helpful information or generic opinions about this product (e.g. telephone: great phone...); in the sense that the product is still in the launching phase in the market. Within time, the product becomes old. Therefore, consumers perceive the advantages/ disadvantages about each specific product feature. Therefore, they will generate comments that contain their sentiments about these features. In this paper, we present an unsupervised method to extract different product features hidden in the opinions which influence its purchase, and that combines Time Weighting (TW) which depends on the time opinions were expressed with Term Frequency-Inverse Document Frequency (TF-IDF). We conduct several experiments using two different datasets about cell phones and hotels. The results show the effectiveness of our automatic feature extraction, as well as its domain independent characteristic.

Keywords: opinion mining, product feature extraction, sentiment analysis, SentiWordNet

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Authors: A. Guellati, F-M Lounis, N. Guemras, K. Daoud

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Micro droplet formation is considered as a growing emerging area of research due to its wide-range application in chemistry as well as biology. The mechanism of micro droplet formation using two immiscible liquids running through a T-junction has been widely studied. We believe that the flow of these two immiscible phases can be of greater important factor that could have an impact on out-flow hydrodynamic behavior, the droplets generated and the size of the droplets. In this study, the type of the capillary tubes used also represents another important factor that can have an impact on the generation of micro droplets. The tygon capillary tubing with hydrophilic inner surface doesn't allow regular out-flows due to the fact that the continuous phase doesn't adhere to the wall of the capillary inner surface. Teflon capillary tubing, presents better wettability than tygon tubing, and allows to obtain steady and regular regimes of out-flow, and the micro droplets are homogeneoussize. The size of the droplets is directly dependent on the flows of the continuous and dispersed phases. Thus, as increasing the flow of the continuous phase, to flow of the dispersed phase stationary, the size of the drops decreases. Inversely, while increasing the flow of the dispersed phase, to flow of the continuous phase stationary, the size of the droplet increases.

Keywords: microfluidic system, micro droplets generation, t-junction, fluids engineering

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Authors: Adamu S. Salawu, Ibrahim O. Isah

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Direct solution to several forms of fourth-order ordinary differential equations is not easily obtained without first reducing them to a system of first-order equations. Thus, numerical methods are being developed with the underlying techniques in the literature, which seeks to approximate some classes of fourth-order initial value problems with admissible error bounds. Multistep methods present a great advantage of the ease of implementation but with a setback of several functions evaluation for every stage of implementation. However, hybrid methods conventionally show a slightly higher order of truncation for any k-step linear multistep method, with the possibility of obtaining solutions at off mesh points within the interval of solution. In the light of the foregoing, we propose the continuous form of a hybrid multistep method with Chebyshev polynomial as a basis function for the numerical integration of fourth-order initial value problems of ordinary differential equations. The basis function is interpolated and collocated at some points on the interval [0, 2] to yield a system of equations, which is solved to obtain the unknowns of the approximating polynomial. The continuous form obtained, its first and second derivatives are evaluated at carefully chosen points to obtain the proposed block method needed to directly approximate fourth-order initial value problems. The method is analyzed for convergence. Implementation of the method is done by conducting numerical experiments on some test problems. The outcome of the implementation of the method suggests that the method performs well on problems with oscillatory or trigonometric terms since the approximations at several points on the solution domain did not deviate too far from the theoretical solutions. The method also shows better performance compared with an existing hybrid method when implemented on a larger interval of solution.

Keywords: Chebyshev polynomial, collocation, hybrid multistep method, initial value problems, interpolation

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Authors: A. Ojha, Y. K. Gupta

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Organophosphate (OP) pesticides are among the most widely used synthetic chemicals for controlling a wide variety of pests throughout the world. Chlorpyrifos (CPF), methyl parathion (MPT), and malathion (MLT) are among the most extensively used OP pesticides in India. DNA strand breaks and DNA-protein crosslinks (DPC) are toxic lesions associated with the mechanisms of toxicity of genotoxic compounds. In the present study, we have examined the potential of CPF, MPT, and MLT individually and in combination, to cause DNA strand breakage and DPC formation. Peripheral blood lymphocytes of rat were exposed to 1/4 and 1/10 LC50 dose of CPF, MPT, and MLT for 2, 4, 8, and 12h. The DNA strand break was measured by the comet assay and expressed as DNA damage index while DPC estimation was done by fluorescence emission. There was significantly marked increase in DNA damage and DNA-protein crosslink formation in time and dose dependent manner. It was also observed that MPT caused the highest level of DNA damage as compared to other studied OP compounds. Thus, from present study, we can conclude that studied pesticides have genotoxic potential. The pesticides mixture does not potentiate the toxicity of each other. Nonetheless, additional in vivo data are required before a definitive conclusion can be drawn regarding hazard prediction to humans.

Keywords: organophosphate, pesticides, DNA damage, DNA protein crosslink, genotoxic

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Authors: Nuray Ucar, Pelin Altay, Ilkay Ozsev Yuksek

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Graphene oxide fibers have recently received increasing attention due to their excellent properties such as high specific surface area, high mechanical strength, good thermal properties and high electrical conductivity. They have shown notable potential in various applications including batteries, sensors, filtration and separation and wearable electronics. Carbon nanotubes (CNTs) have unique structural, mechanical, and electrical properties and can be used together with graphene oxide fibers for several application areas such as lithium ion batteries, wearable electronics, etc. Metals salts that can be converted into metal ions and metal oxide can be also used for several application areas such as battery, purification natural gas, filtration, absorption. This study investigates the effects of CNT and metal complex compounds (MnCl₂, metal salts) on the morphological structure of graphene oxide fibers. The graphene oxide dispersion was manufactured by modified Hummers method, and continuous graphene oxide fibers were produced with wet spinning. The CNT and MnCl₂ were incorporated into the coagulation baths during wet spinning process. Produced composite continuous fibers were analyzed with SEM, SEM-EDS and AFM microscopies and as spun fiber counts were measured.

Keywords: continuous graphene oxide fiber, Hummers' method, CNT, MnCl₂

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Authors: Abdelghani Boudiaf

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The profusion of studies relating to the concept of organizational learning shows the importance that has been given to this concept in the management sciences. A few years ago, companies leaned towards ISO 9001 certification; this requires the implementation of the quality management system (QMS). In order for this objective to be achieved, companies must have a set of skills, which pushes them to develop learning through continuous training. The results of empirical research have shown that implementation of the QMS in the company promotes the development of learning. It should also be noted that several types of learning are developed in this sense. Given the nature of skills development is normative in the context of the quality demarche, companies are obliged to qualify and improve the skills of their human resources. Continuous training is the keystone to develop the necessary learning. To carry out continuous training, companies need to be able to identify their real needs by developing training plans based on well-defined engineering. The training process goes obviously through several stages. Initially, training has a general aspect, that is to say, it focuses on topics and actions of a general nature. Subsequently, this is done in a more targeted and more precise way to accompany the evolution of the QMS and also to make the changes decided each time (change of working method, change of practices, change of objectives, change of mentality, etc.). To answer our problematic we opted for the method of qualitative research. It should be noted that the case study method crosses several data collection techniques to explain and understand a phenomenon. Three cases of companies were studied as part of this research work using different data collection techniques related to this method.

Keywords: changing mentalities, continuing training, organizational learning, quality management system, skills development

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Authors: Elias Nemer, Greg Vines

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Time-frequency transformation and spectral representations of audio signals are commonly used in various machine learning applications. Training networks on frequency features such as the Mel-Spectrogram or Cochleogram have been proven more effective and convenient than training on-time samples. In practical realizations, these features are created on a different processor and/or pre-computed and stored on disk, requiring additional efforts and making it difficult to experiment with different features. In this paper, we provide a PyTorch framework for creating various spectral features as well as time-frequency transformation and time-domain filter-banks using the built-in trainable conv1d() layer. This allows computing these features on the fly as part of a larger network and enabling easier experimentation with various combinations and parameters. Our work extends the work in the literature developed for that end: First, by adding more of these features and also by allowing the possibility of either starting from initialized kernels or training them from random values. The code is written as a template of classes and scripts that users may integrate into their own PyTorch classes or simply use as is and add more layers for various applications.

Keywords: neural networks Mel-Spectrogram, chromagram, cochleogram, discrete Fourrier transform, PyTorch conv1d()

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Authors: Maher Gzam, Noureddine Elmejdoub, Younes Jedoui

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Physical parameters involved in the depositional process on stromatolites, which grow in salt marsh domain, are elucidated in this study. Stromatolites start to grow where surface altimetry of the intertidal flat is high enough to reduce water cover (above mean high tide) and to guarantee a lamellar stream flow. Stromatolite aggrades as a thick laminated layer (stromatolite package) allowing pioneer vascular plants (Salicornia Arabica) to colonize this elevated area (6 cm a.m.s.l). In turn halophytic plant, regularly flooded on spring tide, reduce hydrodynamics velocities causing deposition of sediment, as a result, intertidal zone shift on the flat surface with an expanded marsh domain. This positive feedback invokes self organization between stromatolite growth, vegetation proliferation and deposition of sediment and may be applicable to ancient progradational sequence.

Keywords: stromatolites, marsh, deposition of sediment, aggradation, progradation, gulf of Gabes, Tunisia

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Authors: A. C. Blaga, A. Cârlescu, M. Turnea, A. I. Galaction, D. Caşcaval

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The performance of an aerobic stirred bioreactor for fungal fermentation was analyzed on the basis of mixing time and oxygen mass transfer coefficient, by quantifying the influence of some specific geometrical and operational parameters of the bioreactor, as well as the rheological behavior of Penicillium chrysogenum broth (free mycelia and mycelia aggregates). The rheological properties of the fungus broth, controlled by the biomass concentration, its growth rate, and morphology strongly affect the performance of the bioreactor. Experimental data showed that for both morphological structures the accumulation of fungus biomass induces a significant increase of broths viscosity and modifies the rheological behavior. For lower P. chrysogenum concentrations (both morphological conformations), the mixing time initially increases with aeration rate, reaches a maximum value and decreases. This variation can be explained by the formation of small bubbles, due to the presence of solid phase which hinders the bubbles coalescence, the rising velocity of bubbles being reduced by the high apparent viscosity of fungus broths. By biomass accumulation, the variation of mixing time with aeration rate is gradually changed, the continuous reduction of mixing time with air input flow increase being obtained for 33.5 g/l d.w. P. chrysogenum. Owing to the superior apparent viscosity, which reduces considerably the relative contribution of mechanical agitation to the broths mixing, these phenomena are more pronounced for P. chrysogenum free mycelia. Due to the increase of broth apparent viscosity, the biomass accumulation induces two significant effects on oxygen transfer rate: the diminution of turbulence and perturbation of bubbles dispersion - coalescence equilibrium. The increase of P. chrysogenum free mycelia concentration leads to the decrease of kla values. Thus, for the considered variation domain of the main parameters taken into account, namely air superficial velocity from 8.36 10-4 to 5.02 10-3 m/s and specific power input from 100 to 500 W/m3, kla was reduced for 3.7 times for biomass concentration increase from 4 to 36.5 g/l d.w. The broth containing P. crysogenum mycelia aggregates exhibits a particular behavior from the point of view of oxygen transfer. Regardless of bioreactor operating conditions, the increase of biomass concentration leads initially to the increase of oxygen mass transfer rate, the phenomenon that can be explained by the interaction of pellets with bubbles. The results are in relation with the increase of apparent viscosity of broths corresponding to the variation of biomass concentration between the mentioned limits. Thus, the apparent viscosity of the suspension of fungus mycelia aggregates increased for 44.2 times and fungus free mycelia for 63.9 times for CX increase from 4 to 36.5 g/l d.w. By means of the experimental data, some mathematical correlations describing the influences of the considered factors on mixing time and kla have been proposed. The proposed correlations can be used in bioreactor performance evaluation, optimization, and scaling-up.

Keywords: biomass concentration, mixing time, oxygen mass transfer, P. chrysogenum broth, stirred bioreactor

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Authors: Kripesh Adhikari, Hamid Bouchachia, Hammadi Nait-Charif

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Falls are one of the major causes of injury and death among elderly people aged 65 and above. A support system to identify such kind of abnormal activities have become extremely important with the increase in ageing population. Pose estimation is a challenging task and to add more to this, it is even more challenging when pose estimations are performed on challenging poses that may occur during fall. Location of the body provides a clue where the person is at the time of fall. This paper presents a vision-based tracking strategy where available joints are grouped into three different feature points depending upon the section they are located in the body. The three feature points derived from different joints combinations represents the upper region or head region, mid-region or torso and lower region or leg region. Tracking is always challenging when a motion is involved. Hence the idea is to locate the regions in the body in every frame and consider it as the tracking strategy. Grouping these joints can be beneficial to achieve a stable region for tracking. The location of the body parts provides a crucial information to distinguish normal activities from falls.

Keywords: fall detection, machine learning, deep learning, pose estimation, tracking

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Authors: Nattawat Thongpraphai, Anusorn Boonpoke

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This study aimed to determine the removal of colour and curcuminoids from turmeric wastewater using granular activated carbon (GAC) adsorption. The adsorption isotherm and kinetic behavior of colour and curcuminoids was invested using batch and fixed bed columns tests. The results indicated that the removal efficiency of colour and curcuminoids were 80.13 and 78.64%, respectively at 8 hr of equilibrium time. The adsorption isotherm of colour and curcuminoids were well fitted with the Freundlich adsorption model. The maximum adsorption capacity of colour and curcuminoids were 130 Pt-Co/g and 17 mg/g, respectively. The continuous experiment data showed that the exhaustion concentration of colour and curcuminoids occurred at 39 hr of operation time. The adsorption characteristic of colour and curcuminoids from turmeric wastewater by GAC can be described by the Thomas model. The maximum adsorption capacity obtained from kinetic approach were 39954 Pt-Co/g and 0.0516 mg/kg for colour and curcuminoids, respectively. Moreover, the decrease of colour and curcuminoids concentration during the service time showed a similar trend.

Keywords: adsorption, turmeric, colour, curcuminoids, activated carbon

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Authors: JaeHwan Yang, Da-Woon Jeong, Seung-Young Kho, Dong-Kyu Kim

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In terms of ITS, information on link characteristic is an essential factor for plan or operation. But in practical cases, not every link has installed sensors on it. The link that does not have data on it is called “Missing Link”. The purpose of this study is to impute data of these missing links. To get these data, this study applies the machine learning method. With the machine learning process, especially for the deep learning process, missing link data can be estimated from present link data. For deep learning process, this study uses “Recurrent Neural Network” to take time-series data of road. As input data, Dedicated Short-range Communications (DSRC) data of Dalgubul-daero of Daegu Metropolitan Area had been fed into the learning process. Neural Network structure has 17 links with present data as input, 2 hidden layers, for 1 missing link data. As a result, forecasted data of target link show about 94% of accuracy compared with actual data.

Keywords: data estimation, link data, machine learning, road network

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Authors: Daniel Fundi Murithi

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Data from economic, social, clinical, and industrial studies are in some way incomplete or incorrect due to censoring. Such data may have adverse effects if used in the estimation problem. We propose the use of Maximum Likelihood Estimation (MLE) under a progressive type-II censoring scheme to remedy this problem. In particular, maximum likelihood estimates (MLEs) for the location (µ) and scale (λ) parameters of two Parameter Rayleigh distribution are realized under a progressive type-II censoring scheme using the Expectation-Maximization (EM) and the Newton-Raphson (NR) algorithms. These algorithms are used comparatively because they iteratively produce satisfactory results in the estimation problem. The progressively type-II censoring scheme is used because it allows the removal of test units before the termination of the experiment. Approximate asymptotic variances and confidence intervals for the location and scale parameters are derived/constructed. The efficiency of EM and the NR algorithms is compared given root mean squared error (RMSE), bias, and the coverage rate. The simulation study showed that in most sets of simulation cases, the estimates obtained using the Expectation-maximization algorithm had small biases, small variances, narrower/small confidence intervals width, and small root of mean squared error compared to those generated via the Newton-Raphson (NR) algorithm. Further, the analysis of a real-life data set (data from simple experimental trials) showed that the Expectation-Maximization (EM) algorithm performs better compared to Newton-Raphson (NR) algorithm in all simulation cases under the progressive type-II censoring scheme.

Keywords: expectation-maximization algorithm, maximum likelihood estimation, Newton-Raphson method, two-parameter Rayleigh distribution, progressive type-II censoring

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Authors: Ranjitkumar Patil

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Age estimation has importance in forensic dentistry. Dental age estimation has emerged as an alternative to skeletal age determination. The methods based on stages of tooth formation, as appreciated on radiographs, seem to be more appropriate in the assessment of age than those based on skeletal development. The study was done to evaluate dental age in the north Indian population using Cameriere’s method. Aims/Objectives: The study was conducted to assess the dental age of North Indian children using Cameriere’s method and to compare the chronological age and dental age for validation of the Cameriere’s method in the north Indian population. A comparative study of 02-year duration on the OPG (using PLANMECA Promax 3D) data of 497 individuals with ages ranging from 5 to 15 years was done based on simple random technique ethical approval obtained from institutional ethical committee. The data was obtained based on inclusion and exclusion criteria and was analyzed by software for dental age estimation. Statistical analysis: The student’s t-test was used to compare the morphological variables of males with those of females and to compare observed age with estimated age. The regression formula was also calculated. Results: Present study was a comparative study of 497 subjects with a distribution between males and females, with their dental age assessed by using a Panoramic radiograph, following the method described by Cameriere, which is widely accepted. Statistical analysis in our study indicated that gender does not have a significant influence on age estimation. (R2= 0.787). Conclusion: This infers that Cameriere’s method can be effectively applied to the north Indian population.

Keywords: forensic, dental age, skeletal age, chronological age, Cameriere’s method

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Authors: Kyu Jin Kim, Byung Kwan Oh, Hyo Seon Park

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The seismic responses-based structural health monitoring system has been performed to reduce seismic damage. The inter-story drift ratio which is the major index of the seismic capacity assessment is employed for estimating the seismic damage of buildings. Meanwhile, seismic response analysis to estimate the structural responses of building demands significantly high computational cost due to increasing number of high-rise and large buildings. To estimate the inter-story drift ratio of buildings from the earthquake efficiently, this paper suggests the estimation method of inter-story drift for buildings using an artificial neural network (ANN). In the method, the radial basis function neural network (RBFNN) is integrated with optimization algorithm to optimize the variable through evolutionary learning that refers to evolutionary radial basis function neural network (ERBFNN). The estimation method estimates the inter-story drift without seismic response analysis when the new earthquakes are subjected to buildings. The effectiveness of the estimation method is verified through a simulation using multi-degree of freedom system.

Keywords: structural health monitoring, inter-story drift ratio, artificial neural network, radial basis function neural network, genetic algorithm

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Authors: Sergio Celaschi, Henrique Canavarro de Alencar, Claaudecir Biazoli

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Effluent quality is of the highest priority for compliance with the permit limits of environmental protection agencies and ensures the protection of their local water system. Of the pollutants monitored, the biochemical oxygen demand (BOD) posed one of the greatest challenges. This work presents a solution for wastewater treatment plants - WWTP’s ability to react to different situations and meet treatment goals. Delayed BOD5 results from the lab take 7 to 8 analysis days, hindered the WWTP’s ability to react to different situations and meet treatment goals. Reducing BOD turnaround time from days to hours is our quest. Such a solution is based on a system of two BOD bioreactors associated with Digital Twin (DT) and Machine Learning (ML) methodologies via an Internet of Things (IoT) platform to monitor and control a WWTP to support decision making. DT is a virtual and dynamic replica of a production process. DT requires the ability to collect and store real-time sensor data related to the operating environment. Furthermore, it integrates and organizes the data on a digital platform and applies analytical models allowing a deeper understanding of the real process to catch sooner anomalies. In our system of continuous time monitoring of the BOD suppressed by the effluent treatment process, the DT algorithm for analyzing the data uses ML on a chemical kinetic parameterized model. The continuous BOD monitoring system, capable of providing results in a fraction of the time required by BOD5 analysis, is composed of two thermally isolated batch bioreactors. Each bioreactor contains input/output access to wastewater sample (influent and effluent), hydraulic conduction tubes, pumps, and valves for batch sample and dilution water, air supply for dissolved oxygen (DO) saturation, cooler/heater for sample thermal stability, optical ODO sensor based on fluorescence quenching, pH, ORP, temperature, and atmospheric pressure sensors, local PLC/CPU for TCP/IP data transmission interface. The dynamic BOD system monitoring range covers 2 mg/L < BOD < 2,000 mg/L. In addition to the BOD monitoring system, there are many other operational WWTP sensors. The CPU data is transmitted/received to/from the digital platform, which in turn performs analyses at periodic intervals, aiming to feed the learning process. BOD bulletins and their credibility intervals are made available in 12-hour intervals to web users. The chemical kinetics ML algorithm is composed of a coupled system of four first-order ordinary differential equations for the molar masses of DO, organic material present in the sample, biomass, and products (CO₂ and H₂O) of the reaction. This system is solved numerically linked to its initial conditions: DO (saturated) and initial products of the kinetic oxidation process; CO₂ = H₂0 = 0. The initial values for organic matter and biomass are estimated by the method of minimization of the mean square deviations. A real case of continuous monitoring of BOD wastewater effluent quality is being conducted by deploying an IoT application on a large wastewater purification system located in S. Paulo, Brazil.

Keywords: effluent treatment, biochemical oxygen demand, continuous monitoring, IoT, machine learning

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Authors: Daniil Karzanov

Abstract:

This paper studies the effect of quarterly earnings reports on the stock price. The profitability of the stock is modeled by geometric Brownian diffusion and the Constant Elasticity of Variance model. We fit several variations of stochastic differential equations to the pre-and after-report period using the Maximum Likelihood Estimation and Grid Search of parameters method. By examining the change in the model parameters after reports’ publication, the study reveals that the reports have enough evidence to be a structural breakpoint, meaning that all the forecast models exploited are not applicable for forecasting and should be refitted shortly.

Keywords: stock market, earnings reports, financial time series, structural breaks, stochastic differential equations

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Authors: Khadidja Bellifa, Sidi Mohamed Mekelleche

Abstract:

Phenols and especially halogenated phenols represent a substantial part of the chemicals produced worldwide and are known as aquatic pollutants. Quantitative structure–toxicity relationship (QSTR) models are useful for understanding how chemical structure relates to the toxicity of chemicals. In the present study, the acute toxicities of 45 halogenated phenols to Tetrahymena Pyriformis are estimated using no cost semi-empirical quantum chemistry methods. QSTR models were established using the multiple linear regression technique and the predictive ability of the models was evaluated by the internal cross-validation, the Y-randomization and the external validation. Their structural chemical domain has been defined by the leverage approach. The results show that the best model is obtained with the AM1 method (R²= 0.91, R²CV= 0.90, SD= 0.20 for the training set and R²= 0.96, SD= 0.11 for the test set). Moreover, all the Tropsha’ criteria for a predictive QSTR model are verified.

Keywords: halogenated phenols, toxicity mechanism, hydrophobicity, electrophilicity index, quantitative stucture-toxicity relationships

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Authors: Mohammad A. Obeidat, Ayman M. Mansour

Abstract:

Capturing the dynamic behavior of the heart to improve control performance, enhance robustness, and support diagnosis is very important in establishing real time models for the heart. Control Techniques and strategies have been utilized to improve system costs, reliability, and estimation accuracy for different types of systems such as biomedical, industrial, and other systems that required tuning input/output relation and/or monitoring. Simulations are performed to illustrate potential applications of the technology. In this research, a new control technology scheme is used to enhance the performance of the Af system and meet the design specifications.

Keywords: atrial fibrillation, dynamic behavior, closed loop, signal, filter

Procedia PDF Downloads 397

Authors: Linyu Wang, Jiahui Ma, Jianhong Xiang, Hanyu Jiang

Abstract:

For the pilot design of the sparse channel estimation model in Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, the observation matrix constructed according to the matrix cross-correlation criterion, total correlation criterion and other optimization criteria are not optimal, resulting in inaccurate channel estimation and high bit error rate at the receiver. This paper proposes a pilot design method combining high-power sum and high-power variance criteria, which can more accurately estimate the channel. First, the pilot insertion position is designed according to the high-power variance criterion under the condition of equal power. Then, according to the high power sum criterion, the pilot power allocation is converted into a cone programming problem, and the power allocation is carried out. Finally, the optimal pilot is determined by calculating the weighted sum of the high power sum and the high power variance. Compared with the traditional pilot frequency, under the same conditions, the constructed MIMO-OFDM system uses the optimal pilot frequency for channel estimation, and the communication bit error rate performance obtains a gain of 6~7dB.

Keywords: MIMO-OFDM, pilot optimization, compressed sensing, channel estimation

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Authors: Chia Jui Hsieh, Jyh Cheng Chen, Chih Wei Kuo, Ruei Teng Wang, Woei Chyn Chu

Abstract:

Compressed sensing (CS) based computed tomographic (CT) reconstruction algorithm utilizes total variation (TV) to transform CT image into sparse domain and minimizes L1-norm of sparse image for reconstruction. Different from the traditional CS based reconstruction which only calculates x-coordinate and y-coordinate TV to transform CT images into sparse domain, we propose a multi-directional TV to transform tomographic image into sparse domain for low-dose reconstruction. Our method considers all possible directions of TV calculations around a pixel, so the sparse transform for CS based reconstruction is more accurate. In 2D CT reconstruction, we use eight-directional TV to transform CT image into sparse domain. Furthermore, we also use 26-directional TV for 3D reconstruction. This multi-directional sparse transform method makes CS based reconstruction algorithm more powerful to reduce noise and increase image quality. To validate and evaluate the performance of this multi-directional sparse transform method, we use both Shepp-Logan phantom and a head phantom as the targets for reconstruction with the corresponding simulated sparse projection data (angular sampling interval is 5 deg and 6 deg, respectively). From the results, the multi-directional TV method can reconstruct images with relatively less artifacts compared with traditional CS based reconstruction algorithm which only calculates x-coordinate and y-coordinate TV. We also choose RMSE, PSNR, UQI to be the parameters for quantitative analysis. From the results of quantitative analysis, no matter which parameter is calculated, the multi-directional TV method, which we proposed, is better.

Keywords: compressed sensing (CS), low-dose CT reconstruction, total variation (TV), multi-directional gradient operator

Procedia PDF Downloads 242