Search results for: water jet model.

Authors: Fahimeh Golestani, Mohammad Kazemeini, Moslem Fattahi, Ali Amjadian

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In this research a mathematical model for direct oxidization of hydrogen sulfide into elemental sulfur in a fluidized bed reactor with external circulation was developed. As the catalyst is deactivated in the fluidized bed, it might be placed in a reduction tank in order to remove sulfur through heating above its dew point. The reactor model demonstrated via MATLAB software. It was shown that variations of H2S conversion as well as; products formed were reasonable in comparison with corresponding results of a fixed bed reactor. Through analyzing results of this model, it became possible to propose the main optimized operating conditions for the process considered. These conditions included; the temperature range of 100-130ºC and utilizing the catalyst as much as possible providing the highest bed density respect to dimensions of bed, economical aspects that the bed ever remained in fluidized mode. A high active and stable catalyst under the optimum conditions exhibited 100% conversion in a fluidized bed reactor.

Keywords: Direct oxidization, Fluidized bed, H2S, Mathematical modeling, Optimum conditions.

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Authors: Elysia Barker, Jian Guo Zhou, Ling Qian, Steve Decent

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A method of modelling topography used in the simulation of riverbeds is proposed in this paper which removes the need for datapoints and measurements of a physical terrain. While complex scans of the contours of a surface can be achieved with other methods, this requires specialised tools which the proposed method overcomes by using fractional Brownian motion (FBM) as a basis to estimate the real surface within a 15% margin of error while attempting to optimise algorithmic efficiency. This removes the need for complex, expensive equipment and reduces resources spent modelling bed topography. This method also accounts for the change in topography over time due to erosion, sediment transport, and other external factors which could affect the topography of the ground by updating its parameters and generating a new bed. The lattice Boltzmann method (LBM) is used to simulate both stationary and steady flow cases in a side-by-side comparison over the generated bed topography using the proposed method, and a test case taken from an external source. The method, if successful, will be incorporated into the current LBM program used in the testing phase, which will allow an automatic generation of topography for the given situation in future research, removing the need for bed data to be specified.

Keywords: Bed topography, FBM, LBM, shallow water, simulations.

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Authors: Mohamed Asaad Abdelrazek, Amir Taher El-Sheikh, M. Zayan, A.M. Elhady

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The successful realization of complex systems is dependent not only on the technology issues and the process for implementing them, but on the management issues as well. Managing the systems development lifecycle requires technical management. Systems engineering management is the technical management. Systems engineering management is accomplished by incorporating many activities. The three major activities are development phasing, systems engineering process and lifecycle integration. Systems engineering management activities are performed across the system development lifecycle. Due to the ever-increasing complexity of systems as well the difficulty of managing and tracking the development activities, new ways to achieve systems engineering management activities are required. This paper presents a systematic approach used as a design management tool applied across systems engineering management roles. In this approach, Transdisciplinary System Development Lifecycle (TSDL) Model has been modified and integrated with Quality Function Deployment. Hereinafter, the name of the systematic approach is the Transdisciplinary Quality System Development Lifecycle (TQSDL) Model. The QFD translates the voice of customers (VOC) into measurable technical characteristics. The modified TSDL model is based on Axiomatic Design developed by Suh which is applicable to all designs: products, processes, systems and organizations. The TQSDL model aims to provide a robust structure and systematic thinking to support the implementation of systems engineering management roles. This approach ensures that the customer requirements are fulfilled as well as satisfies all the systems engineering manager roles and activities.

Keywords: Axiomatic design, quality function deployment, systems engineering management, system development lifecycle.

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Authors: Snehal R. Pathak, Sachin S. Munnoli

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Evaluation of dynamic earth pressure on retaining wall is a topic of primary importance. In present paper, dynamic active earth pressure and displacement of flexible cantilever retaining wall has been evaluated analytically using 2-DOF mass-spring-dashpot model by incorporating both wall and backfill properties. The effect of wall flexibility on dynamic active earth pressure and wall displacement are studied and presented in graphical form. The obtained results are then compared with the various conventional methods, experimental analysis and also with PLAXIS analysis. It is observed that the dynamic active earth pressure decreases with increase in the wall flexibility while wall displacement increases linearly with flexibility of the wall. The results obtained by proposed 2-DOF analytical model are found to be more realistic and economical.

Keywords: Earth pressure, earthquake, 2-DOF model, plaxis, wall movement, retaining walls.

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Authors: Weifeng Wang, Xilong Chen, Xianwei Zeng

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The cable tower of Liede Bridge is a double-column curved-lever arched-beam portal framed structure. Being novel and unique in structure, its cable tower differs in complexity from traditional ones. This paper analyzes the ultimate load capacity of cable tower by adopting the finite element calculations and model tests which indicate that constitutive relations applied here give a better simulation of actual failure process of prestressed reinforced concrete. In vertical load, horizontal load and overloading tests, the stepped loading of the tower model is of linear relationship, and the test data has good repeatability. All suggests that the cable tower has good bearing capacity, rational design and high emergency capacity.

Keywords: Cable tower of Liede Bridge, ultimate load capacity, model test, nonlinear finite element method

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Authors: R. Behmanesh, I. Rahimi

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recurrent neural network (RNN) is an efficient tool for modeling production control process as well as modeling services. In this paper one RNN was combined with regression model and were employed in order to be checked whether the obtained data by the model in comparison with actual data, are valid for variable process control chart. Therefore, one maintenance process in workshop of Esfahan Oil Refining Co. (EORC) was taken for illustration of models. First, the regression was made for predicting the response time of process based upon determined factors, and then the error between actual and predicted response time as output and also the same factors as input were used in RNN. Finally, according to predicted data from combined model, it is scrutinized for test values in statistical process control whether forecasting efficiency is acceptable. Meanwhile, in training process of RNN, design of experiments was set so as to optimize the RNN.

Keywords: RNN, DOE, regression, control chart.

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Authors: Gilbert Makanda, Roelf Sypkens

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A mathematical model for knowledge acquisition in teaching and learning is proposed. In this study we adopt the mathematical model that is normally used for disease modelling into teaching and learning. We derive mathematical conditions which facilitate knowledge acquisition. This study compares the effects of dropping out of the course at early stages with later stages of learning. The study also investigates effect of individual interaction and learning from other sources to facilitate learning. The study fits actual data to a general mathematical model using Matlab ODE45 and lsqnonlin to obtain a unique mathematical model that can be used to predict knowledge acquisition. The data used in this study was obtained from the tutorial test results for mathematics 2 students from the Central University of Technology, Free State, South Africa in the department of Mathematical and Physical Sciences. The study confirms already known results that increasing dropout rates and forgetting taught concepts reduce the population of knowledgeable students. Increasing teaching contacts and access to other learning materials facilitate knowledge acquisition. The effect of increasing dropout rates is more enhanced in the later stages of learning than earlier stages. The study opens up a new direction in further investigations in teaching and learning using differential equations.

Keywords: Differential equations, knowledge acquisition, least squares nonlinear, dynamical systems.

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Authors: Truong Quang Dang Khoa, Masahiro Nakagawa

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In this paper, a novel associative memory model will be proposed and applied to memory retrievals based on the conventional continuous time model. The conventional model presents memory capacity is very low and retrieval process easily converges to an equilibrium state which is very different from the stored patterns. Genetic Algorithms is well-known with the capability of global optimal search escaping local optimum on progress to reach a global optimum. Based on the well-known idea of Genetic Algorithms, this work proposes a heuristic rule to make a mutation when the state of the network is trapped in a spurious memory. The proposal heuristic associative memory show the stored capacity does not depend on the number of stored patterns and the retrieval ability is up to ~ 1.

Keywords: Artificial Intelligent, Soft Computing, NeuralNetworks, Genetic Algorithms, Hopfield Neural Networks, andAssociative Memories.

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Authors: Francesco Marotti de Sciarra, Raffaele Barretta

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Starting from nonlocal continuum mechanics, a thermodynamically new nonlocal model of Euler-Bernoulli nanobeams is provided. The nonlocal variational formulation is consistently provided and the governing differential equation for transverse displacement is presented. Higher-order boundary conditions are then consistently derived. An example is contributed in order to show the effectiveness of the proposed model.

Keywords: Bernoulli-Euler beams, Nanobeams, nonlocal elasticity.

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Authors: A. Vairis, M. Petousis, B. Kandyla, C. Chrisoulakis

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The human knee joint has a three dimensional geometry with multiple body articulations that produce complex mechanical responses under loads that occur in everyday life and sports activities. To produce the necessary joint compliance and stability for optimal daily function various menisci and ligaments are present while muscle forces are used to this effect. Therefore, knowledge of the complex mechanical interactions of these load bearing structures is necessary when treatment of relevant diseases is evaluated and assisting devices are designed. Numerical tools such as finite element analysis are suitable for modeling such joints in order to understand their physics. They have been used in the current study to develop an accurate human knee joint and model its mechanical behavior. To evaluate the efficacy of this articulated model, static load cases were used for comparison purposes with previous experimentally verified modeling works drawn from literature.

Keywords: biomechanics, finite element modeling, knee joint

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Authors: Mingren Shi, Michael Renton

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There is a world-wide need for the development of sustainable management strategies to control pest infestation and the development of phosphine (PH3) resistance in lesser grain borer (Rhyzopertha dominica). Computer simulation models can provide a relatively fast, safe and inexpensive way to weigh the merits of various management options. However, the usefulness of simulation models relies on the accurate estimation of important model parameters, such as mortality. Concentration and time of exposure are both important in determining mortality in response to a toxic agent. Recent research indicated the existence of two resistance phenotypes in R. dominica in Australia, weak and strong, and revealed that the presence of resistance alleles at two loci confers strong resistance, thus motivating the construction of a two-locus model of resistance. Experimental data sets on purified pest strains, each corresponding to a single genotype of our two-locus model, were also available. Hence it became possible to explicitly include mortalities of the different genotypes in the model. In this paper we described how we used two generalized linear models (GLM), probit and logistic models, to fit the available experimental data sets. We used a direct algebraic approach generalized inverse matrix technique, rather than the traditional maximum likelihood estimation, to estimate the model parameters. The results show that both probit and logistic models fit the data sets well but the former is much better in terms of small least squares (numerical) errors. Meanwhile, the generalized inverse matrix technique achieved similar accuracy results to those from the maximum likelihood estimation, but is less time consuming and computationally demanding.

Keywords: mortality estimation, probit models, logistic model, generalized inverse matrix approach, pest control simulation

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Authors: Zaineb Ben Messaoud, Dorra Gargouri, Saida Zribi, Ahmed Ben Hamida

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This paper presents a formant-tracking linear prediction (FTLP) model for speech processing in noise. The main focus of this work is the detection of formant trajectory based on Hidden Markov Models (HMM), for improved formant estimation in noise. The approach proposed in this paper provides a systematic framework for modelling and utilization of a time- sequence of peaks which satisfies continuity constraints on parameter; the within peaks are modelled by the LP parameters. The formant tracking LP model estimation is composed of three stages: (1) a pre-cleaning multi-band spectral subtraction stage to reduce the effect of residue noise on formants (2) estimation stage where an initial estimate of the LP model of speech for each frame is obtained (3) a formant classification using probability models of formants and Viterbi-decoders. The evaluation results for the estimation of the formant tracking LP model tested in Gaussian white noise background, demonstrate that the proposed combination of the initial noise reduction stage with formant tracking and LPC variable order analysis, results in a significant reduction in errors and distortions. The performance was evaluated with noisy natual vowels extracted from international french and English vocabulary speech signals at SNR value of 10dB. In each case, the estimated formants are compared to reference formants.

Keywords: Formants Estimation, HMM, Multi Band Spectral Subtraction, Variable order LPC coding, White Gauusien Noise.

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Authors: Mogari I. Rapoo, Diteboho Xaba

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This paper examines the forecasting performance of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) models with the published exchange rate obtained from South African Reserve Bank (SARB). ARIMA is one of the popular linear models in time series forecasting for the past decades. ARIMA and ANN models are often compared and literature revealed mixed results in terms of forecasting performance. The study used the MSE and MAE to measure the forecasting performance of the models. The empirical results obtained reveal the superiority of ARIMA model over ANN model. The findings further resolve and clarify the contradiction reported in literature over the superiority of ARIMA and ANN models.

Keywords: ARIMA, artificial neural networks models, error metrics, exchange rates.

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Authors: Le Xuan Cau

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Flash flood is occurred in short time rainfall interval: from 1 hour to 12 hours in small and medium basins. Flash floods typically have two characteristics: large water flow and big flow velocity. Flash flood is occurred at hill valley site (strip of lowland of terrain) in a catchment with large enough distribution area, steep basin slope, and heavy rainfall. The risk of flash floods is determined through Gridded Basin Flash Flood Potential Index (GBFFPI). Flash Flood Potential Index (FFPI) is determined through terrain slope flash flood index, soil erosion flash flood index, land cover flash floods index, land use flash flood index, rainfall flash flood index. Determining GBFFPI, each cell in a map can be considered as outlet of a water accumulation basin. GBFFPI of the cell is determined as basin average value of FFPI of the corresponding water accumulation basin. Based on GIS, a tool is developed to compute GBFFPI using ArcObjects SDK for .NET. The maps of GBFFPI are built in two types: GBFFPI including rainfall flash flood index (real time flash flood warning) or GBFFPI excluding rainfall flash flood index. GBFFPI Tool can be used to determine a high flash flood potential site in a large region as quick as possible. The GBFFPI is improved from conventional FFPI. The advantage of GBFFPI is that GBFFPI is taking into account the basin response (interaction of cells) and determines more true flash flood site (strip of lowland of terrain) while conventional FFPI is taking into account single cell and does not consider the interaction between cells. The GBFFPI Map of QuangNam, QuangNgai, DaNang, Hue is built and exported to Google Earth. The obtained map proves scientific basis of GBFFPI.

Keywords: ArcObjects SDK for .NET, Basin average value of FFPI, Gridded basin flash flood potential index, GBFFPI map.

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Authors: Andrés Carrión García, Aura López de Murillo, José Jabaloyes Vivas, Angela Grisales del Río

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Some quality control tools use non metric subjective information coming from experts, who qualify the intensity of relations existing inside processes, but without quantifying them. In this paper we have developed a quality control analytic tool, measuring the impact or strength of the relationship between process operations and product characteristics. The tool includes two models: a qualitative model, allowing relationships description and analysis; and a formal quantitative model, by means of which relationship quantification is achieved. In the first one, concepts from the Graphs Theory were applied to identify those process elements which can be sources of variation, that is, those quality characteristics or operations that have some sort of prelacy over the others and that should become control items. Also the most dependent elements can be identified, that is those elements receiving the effects of elements identified as variation sources. If controls are focused in those dependent elements, efficiency of control is compromised by the fact that we are controlling effects, not causes. The second model applied adapts the multivariate statistical technique of Covariance Structural Analysis. This approach allowed us to quantify the relationships. The computer package LISREL was used to obtain statistics and to validate the model.

Keywords: Characteristics matrix, covariance structure analysis, LISREL.

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Authors: Farrokh J., Azhar K. Mansur

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There exists a strong correlation between efficient project management and competitive advantage for organizations. Therefore, organizations are striving to standardize and assess the rigor of their project management processes and capabilities i.e. project management maturity. Researchers and standardization organizations have developed several project management maturity models (PMMMs) to assess project management maturity of the organizations. This study presents a critical evaluation of some of the leading PMMMs against OPM3® in a multitude of ways to look at which PMMM is the most comprehensive model - which could assess most aspects of organizations and also help the organizations in gaining competitive advantage over competitors. After a detailed morphological analysis of the models, it is concluded that OPM3® is the most promising maturity model that can really provide a competitive advantage to the organizations due to its unique approach of assessment and improvement strategies.

Keywords: Project management maturity, project managemen tmaturity models, competitive advantage.

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Authors: Ksheeraj Sai Vepuri, Nada Attar

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We as humans use words with accompanying visual and facial cues to communicate effectively. Classifying facial emotion using computer vision methodologies has been an active research area in the computer vision field. In this paper, we propose a simple method for facial expression recognition that enhances accuracy. We tested our method on the FER-2013 dataset that contains static images. Instead of using Histogram equalization to preprocess the dataset, we used Unsharp Mask to emphasize texture and details and sharpened the edges. We also used ImageDataGenerator from Keras library for data augmentation. Then we used Convolutional Neural Networks (CNN) model to classify the images into 7 different facial expressions, yielding an accuracy of 69.46% on the test set. Our results show that using image preprocessing such as the sharpening technique for a CNN model can improve the performance, even when the CNN model is relatively simple.

Keywords: Facial expression recognition, image pre-processing, deep learning, CNN.

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Authors: Kandpal M. , Gundampati R. K , Debnath M.

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Microbial contamination, most of which are fecal born in drinking water and food industry is a serious threat to humans. Escherichia coli is one of the most common and prevalent among them. We have developed a sensor for rapid and an early detection of contaminants, taking E.coli as a threat indicator organism. The sensor is based on co-polymerizations of aniline and formaldehyde in form of thin film over glass surface using the vacuum deposition technique. The particular doping combination of thin film with Fe-Al and Fe-Cu in different concentrations changes its non conducting properties to p- type semi conductor. This property is exploited to detect the different contaminants, believed to have the different surface charge. It was found through experiments that different microbes at same OD (0.600 at 600 nm) have different conductivity in solution. Also the doping concentration is found to be specific for attracting microbes on the basis of surface charge. This is a simple, cost effective and quick detection method which not only decreases the measurement time but also gives early warnings for highly contaminated samples.

Keywords: Sensor, Vacuum deposition technique, thin film, E.coli detection, doping concentration.

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Authors: Hideki.Kobayashi, Hiroshi.Osada

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The service industry accounts for about 70% of GDP of Japan, and the importance of the service innovation is pointed out. The importance of the system use and the support service increases in the information system that is one of the service industries. However, because the system is not used enough, the purpose for which it was originally intended cannot often be achieved in the CRM system. To promote the use of the system, the effective service method is needed. It is thought that the service model's making and the clarification of the success factors are necessary to improve the operation service of the CRM system. In this research the model of the operation service in the CRM system is made.

Keywords: Information system, Operation service, Serviceinnovation, Solution

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Authors: Niloufar Ghoreishi, Ali Nekouzadeh

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The stability of the flight during maneuvering and in response to probable perturbations is one of the most essential features of an aircraft that should be analyzed and designed for. In this study, we derived the non-linear governing equations of aircraft dynamics during the climb/descend phase and simulated a model aircraft. The corresponding force and moment dimensionless coefficients of the model and their variations with elevator angle and other relevant aerodynamic parameters were measured experimentally. The short-period mode and phugoid mode response were simulated by solving the governing equations numerically and then compared with the desired stability parameters for the particular level, category, and class of the aircraft model. To meet the target stability, a controller was designed and used. This resulted in significant improvement in the stability parameters of the flight.

Keywords: Flight stability, phugoid mode, short period mode, climb phase, damping coefficient.

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Authors: Igor Astrov, Mikhail Pikkov

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This paper focuses on a critical component of the situational awareness (SA), the neural control of depth flight of an autonomous underwater vehicle (AUV). Constant depth flight is a challenging but important task for AUVs to achieve high level of autonomy under adverse conditions. With the SA strategy, we proposed a multirate neural control of an AUV trajectory using neural network model reference controller for a nontrivial mid-small size AUV "r2D4" stochastic model. This control system has been demonstrated and evaluated by simulation of diving maneuvers using software package Simulink. From the simulation results it can be seen that the chosen AUV model is stable in the presence of high noise, and also can be concluded that the fast SA of similar AUV systems with economy in energy of batteries can be asserted during the underwater missions in search-and-rescue operations.

Keywords: Autonomous underwater vehicles, multirate systems, neurocontrollers, situational awareness.

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Authors: M. M. Rashid

Abstract:

Allah (SWT) bestowed us with the Divine blessing, providing the wonderful source of water as stated in verse 15:22 of Al-Quran. Arabic “Ar-Riaaha Lawaaqiha (ٱلرِّيَـٰحَ لَوَٰقِحَ)” of this verse is translated as “fertilizing winds.” The “fertilizing winds” literally, refer to the winds having the roles: to fertilize something similar to the “zygotes” in humans and animals (formation of clouds in the sky in this case); to produce fertilizers for the plants, crops, etc.; and to pollinate the plants. In this paper, these roles of “fertilizing winds” have been validated by presenting the modern knowledge of science in this regard. Existing interpretations are mostly focused on the “formation of clouds in the sky” while few of them mention about the pollination of trees. The production of fertilizers, in this regard, may also be considered for the interpteration of this verse. It has been observed that the winds contain the necessary components of forming the clouds; the necessary components of producing the fertilizers; and the necessary components to pollinate the plants. The science of meteorology gives us a clear understanding of the formation of clouds. Moreover, we know that the lightning bolts break the nitrogen molecules of winds and the water molecules of vapor to form fertilizers. Pollination is a common role of winds in plant fertilization. All the scientific phenomena presented here give us better interpretations of “fertilizing winds.”

Keywords: Al-Quran, fertilizing winds, meteorology, cloud droplets.

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Authors: A. I. Augie, M. Saleh, A. A. Gado

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In this research, Self-Potential (SP) method was employed to locate anomalous electrical conductivity located in Goronyo area and also to determine the condition of the embankment of the dam. SP data were plotted against distance along with the profile and spacing of electrode using surfer software (version 12). High and low zones of SP values were identified along the right and left abutments of the dam reservoir. The regions with high SP values were described to be high tendency of fluid flow associate with wet sandy soil. These zones have the SP values ranging from 200 mV and above. High SP values were due to the high moisture content that may lead to the seepage of water leaking through this zone. The zones with high SP values occupied Profiles S1, S2, S3, S4 and S5 indicating the presence of potential seepage paths within the subsurface of the embankment. These regions of seepage were identified as weak zones and potential pathways through which water could be lost from the dam reservoir. The SP values for the regions range from 250 m to 400 m (S1), 306 m to 400 m (S2), 192 m to 400 m (S3), 48 m to 200 m (S4) and 7 m to 170 m (S5) with their corresponding maximum depths of 30 m, 28 m, 28 m, 30 m and 26 m respectively. However, zones of low SP values in the overburden were observed which shows the presence of intact regions, which may be due to the compactness and dryness around the dam. The weak zones were considered as geological features (such as fractures, joints, and faults) that have undermined the integrity of the dam structure, which has led to the abnormal seepage.

Keywords: Self-potential, subsurface, seepage, condition and dam.

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Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz

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Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.

Keywords: Customer relationship management, churn prediction, telecom industry, deep learning, Artificial Neural Networks, ANN.

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Authors: Francisco Liaño-Carrera, José Isaac Ramírez-Macías, David Salas-Monreal, Mayra Lorena Riveron-Enzastiga, Marcos Rangel-Avalos, Adriana Andrea Roldán-Ubando

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The construction of most coastal infrastructure developments around the world are usually made considering wave height, current velocities and river discharges; however, little effort has been paid to surveying sediment transport during dredging or the modification to currents outside the ports or marinas during and after the construction. This study shows a complete survey during the construction of one of the largest ports of the Gulf of Mexico. An anchored Acoustic Doppler Current Velocity profiler (ADCP), a towed ADCP and a combination of model outputs were used at the Veracruz port construction in order to describe the hourly sediment transport and current modifications in and out of the new port. Owing to the stability of the system the new port was construction inside Vergara Bay, a low wave energy system with a tidal range of up to 0.40 m. The results show a two-current system pattern within the bay. The north side of the bay has an anticyclonic gyre, while the southern part of the bay shows a cyclonic gyre. Sediment transport trajectories were made every hour using the anchored ADCP, a numerical model and the weekly data obtained from the towed ADCP within the entire bay. The sediment transport trajectories were carefully tracked since the bay is surrounded by coral reef structures which are sensitive to sedimentation rate and water turbidity. The survey shows that during dredging and rock input used to build the wave breaker sediments were locally added (< 2500 m²) and local currents disperse it in less than 4 h. While the river input located in the middle of the bay and the sewer system plant may add more than 10 times this amount during a rainy day or during the tourist season. Finally, the coastal line obtained seasonally with a drone suggests that the southern part of the bay has not been modified by the construction of the new port located in the northern part of the bay, owing to the two subsystem division of the bay.

Keywords: Acoustic Doppler current profiler, time series, port construction, construction around coral reefs, sediment transport monitoring.

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Authors: Chen Wu, Lijuan Wang

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Relation between tolerance class and indispensable attribute and knowledge dependency in rough set model with tolerance relation is explored. After giving definitions and concepts of knowledge dependency and knowledge dependency degree for incomplete information system in tolerance rough set model by distinguishing decision attribute containing missing attribute value or not, the result of maintaining reflectivity, transitivity, augmentation, decomposition law and merge law for complete knowledge dependency is proved. Knowledge dependency degrees (not complete knowledge dependency degrees) only satisfy some laws after transitivity, augmentation and decomposition operations. An algorithm to solve attribute reduction in an incomplete decision table is designed. The correctness is checked by an example.

Keywords: Incomplete information system, rough set, tolerance relation, knowledge dependence, attribute reduction.

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Authors: Nikhil, Ari Visa, Chin-Chao Chen, Chiu-Yue Lin, Jaakko A. Puhakka, Olli Yli-Harja

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A predictive clustering hybrid regression (pCHR) approach was developed and evaluated using dataset from H2- producing sucrose-based bioreactor operated for 15 months. The aim was to model and predict the H2-production rate using information available about envirome and metabolome of the bioprocess. Selforganizing maps (SOM) and Sammon map were used to visualize the dataset and to identify main metabolic patterns and clusters in bioprocess data. Three metabolic clusters: acetate coupled with other metabolites, butyrate only, and transition phases were detected. The developed pCHR model combines principles of k-means clustering, kNN classification and regression techniques. The model performed well in modeling and predicting the H2-production rate with mean square error values of 0.0014 and 0.0032, respectively.

Keywords: Biohydrogen, bioprocess modeling, clusteringhybrid regression.

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Authors: Irina Mikajlo, Jaroslav Záhora, Helena Dvořáčková, Jaroslav Hynšt, Jakub Elbl

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Strong anthropogenic impact has uncontrolled consequences on the nature of the soil. Hence, up-to-date sustainable methods of soil state improvement are essential. Investigators provide the evidence that biochar can positively effects physical, chemical, and biological soil properties and the abundance of mycorrhizal fungi which are in the focus of this study. The main aim of the present investigation is to demonstrate the effect of two types of plant growth promoting bacteria (PGPB) inoculums along with the beech wood biochar and mineral N additives on mycorrhizal colonization. Experiment has been set up in laboratory conditions with containers filled with arable soil from the protection zone of the main water source “Brezova nad Svitavou”. Lactuca sativa (lettuce) has been selected as a model plant. Based on the obtained data, it can be concluded that mycorrhizal colonization increased as the result of combined influence of biochar and PGPB inoculums amendment. In addition, correlation analyses showed that the numbers of main groups of cultivated bacteria were dependent on the degree of mycorrhizal colonization.

Keywords: Arbuscular mycorrhiza, biochar, PGPB inoculum, soil microorganisms.

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Authors: I. V. Pinto, M. R. Sooriyarachchi

Abstract:

It can be frequently observed that the data arising in our environment have a hierarchical or a nested structure attached with the data. Multilevel modelling is a modern approach to handle this kind of data. When multilevel modelling is combined with a binary response, the estimation methods get complex in nature and the usual techniques are derived from quasi-likelihood method. The estimation methods which are compared in this study are, marginal quasi-likelihood (order 1 & order 2) (MQL1, MQL2) and penalized quasi-likelihood (order 1 & order 2) (PQL1, PQL2). A statistical model is of no use if it does not reflect the given dataset. Therefore, checking the adequacy of the fitted model through a goodness-of-fit (GOF) test is an essential stage in any modelling procedure. However, prior to usage, it is also equally important to confirm that the GOF test performs well and is suitable for the given model. This study assesses the suitability of the GOF test developed for binary response multilevel models with respect to the method used in model estimation. An extensive set of simulations was conducted using MLwiN (v 2.19) with varying number of clusters, cluster sizes and intra cluster correlations. The test maintained the desirable Type-I error for models estimated using PQL2 and it failed for almost all the combinations of MQL. Power of the test was adequate for most of the combinations in all estimation methods except MQL1. Moreover, models were fitted using the four methods to a real-life dataset and performance of the test was compared for each model.

Keywords: Goodness-of-fit test, marginal quasi-likelihood, multilevel modelling, type-I error, penalized quasi-likelihood, power, quasi-likelihood.

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Authors: Tonislav Ivanov, Oleksii Nedashkivskyi, Denis Babeshko, Vadim Pinskiy, Matthew Putman

Abstract:

Training a machine learning model for object detection that generalizes well is known to benefit from a training dataset with diverse examples. However, training datasets usually contain many repeats of common examples of a class and lack rarely seen examples. This is due to the process commonly used during human annotation where a person would proceed sequentially through a list of images labeling a sufficiently high total number of examples. Instead, the method presented involves an active process where, after the initial labeling of several images is completed, the next subset of images for labeling is selected by an algorithm. This process of algorithmic image selection and manual labeling continues in an iterative fashion. The algorithm used for the image selection is a deep learning algorithm, based on the U-shaped architecture, which quantifies the presence of unseen data in each image in order to find images that contain the most novel examples. Moreover, the location of the unseen data in each image is highlighted, aiding the labeler in spotting these examples. Experiments performed using semiconductor wafer data show that labeling a subset of the data, curated by this algorithm, resulted in a model with a better performance than a model produced from sequentially labeling the same amount of data. Also, similar performance is achieved compared to a model trained on exhaustive labeling of the whole dataset. Overall, the proposed approach results in a dataset that has a diverse set of examples per class as well as more balanced classes, which proves beneficial when training a deep learning model.

Keywords: Computer vision, deep learning, object detection, semiconductor.

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