Search results for: inverse filtering on graphs

Authors: Gordon Newlove Asamoah, Wendy Ama Oti

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This study focused on evaluating access to finance for local oil field companies in Ghana. The study adopted a census survey design in evaluating access to finance for local oil field companies in Ghana. The respondents of this study were 30 management members of three oil field companies in Ghana. The data collected was analysed using Statistical Package for Social Scientists (SPSS) to generate tables and graphs for interpretation. The results show that most companies use equity financing in combination with other forms of financing to finance their business activities. This research has shown the various challenges bordering on the financing of local oil and gas projects, with emphasis on the challenges of raising funds by indigenous oil companies. Financing of the projects by indigenous oil field companies in Ghana is preferably achieved through equity finance mainly because it is the easiest to get compared to all the other forms of financing available. Other sources of financing available are debt financing, joint venture, and retained earnings from the profits generated from their operations. The study made recommendations to local oil field companies as to how they can make good use of the capital market to raise financing.

Keywords: access, financing, oil fields, Ghana

Procedia PDF Downloads 110

Authors: Ali Ateş, Ansar B. Mwimbo, Ali H. Abdulkarim

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General transport equation has a wide range of application in Fluid Mechanics and Heat Transfer problems. In this equation, generally when φ variable which represents a flow property is used to represent fluid velocity component, general transport equation turns into momentum equations or with its well known name Navier-Stokes equations. In these non-linear differential equations instead of seeking for analytic solutions, preferring numerical solutions is a more frequently used procedure. Finite difference method is a commonly used numerical solution method. In these equations using velocity and pressure gradients instead of stress tensors decreases the number of unknowns. Also, continuity equation, by integrating the system, number of equations is obtained as number of unknowns. In this situation, velocity and pressure components emerge as two important parameters. In the solution of differential equation system, velocities and pressures must be solved together. However, in the considered grid system, when pressure and velocity values are jointly solved for the same nodal points some problems confront us. To overcome this problem, using staggered grid system is a referred solution method. For the computerized solutions of the staggered grid system various algorithms were developed. From these, two most commonly used are SIMPLE and SIMPLER algorithms. In this study Navier-Stokes equations were numerically solved for Newtonian flow, whose mass or gravitational forces were neglected, for incompressible and laminar fluid, as a hydro dynamically fully developed region and in two dimensional cartesian coordinate system. Finite difference method was chosen as the solution method. This is a parametric study in which varying values of velocity components, pressure and Reynolds numbers were used. Differential equations were discritized using central difference and hybrid scheme. The discritized equation system was solved by Gauss-Siedel iteration method. SIMPLE and SIMPLER were used as solution algorithms. The obtained results, were compared for central difference and hybrid as discritization methods. Also, as solution algorithm, SIMPLE algorithm and SIMPLER algorithm were compared to each other. As a result, it was observed that hybrid discritization method gave better results over a larger area. Furthermore, as computer solution algorithm, besides some disadvantages, it can be said that SIMPLER algorithm is more practical and gave result in short time. For this study, a code was developed in DELPHI programming language. The values obtained in a computer program were converted into graphs and discussed. During sketching, the quality of the graph was increased by adding intermediate values to the obtained result values using Lagrange interpolation formula. For the solution of the system, number of grid and node was found as an estimated. At the same time, to indicate that the obtained results are satisfactory enough, by doing independent analysis from the grid (GCI analysis) for coarse, medium and fine grid system solution domain was obtained. It was observed that when graphs and program outputs were compared with similar studies highly satisfactory results were achieved.

Keywords: finite difference method, GCI analysis, numerical solution of the Navier-Stokes equations, SIMPLE and SIMPLER algoritms

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Authors: Phuoc Si Nguyen

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Frequency transformation with Pascal matrix equations is a method for transforming an electronic filter (analogue or digital) into another filter. The technique is based on frequency transformation in the s-domain, bilinear z-transform with pre-warping frequency, inverse bilinear transformation and a very useful application of the Pascal’s triangle that simplifies computing and enables calculation by hand when transforming from one filter to another. This paper will introduce two methods to transform a filter into a digital filter: frequency transformation from the s-domain into the z-domain; and frequency transformation in the z-domain. Further, two Pascal matrix equations are derived: an analogue to digital filter Pascal matrix equation and a digital to digital filter Pascal matrix equation. These are used to design a desired digital filter from a given filter.

Keywords: frequency transformation, bilinear z-transformation, pre-warping frequency, digital filters, analog filters, pascal’s triangle

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Authors: Vrushali Guhe, Shailza Singh

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Cutaneous leishmaniasis is neglected tropical disease present worldwide caused by the protozoan parasite Leishmania major, the therapeutic armamentarium for leishmaniasis are showing several limitations as drugs are showing toxic effects with increasing resistance by a parasite. Thus identification of novel therapeutic targets is of paramount importance. Previous studies have shown that autophagy, a cellular process, can either facilitate infection or aid in the elimination of the parasite, depending on the specific parasite species and host background in leishmaniasis. In the present study, our objective was to target the essential autophagy protein ATG8, which plays a crucial role in the survival, infection dynamics, and differentiation of the Leishmania parasite. ATG8 in Leishmania major and its homologue, LC3, in Homo sapiens, act as autophagic markers. Present study manifested the crucial role of ATG8 protein as a potential target for combating Leishmania major infection. Through bioinformatics analysis, we identified non-conserved motifs within the ATG8 protein of Leishmania major, which are not present in LC3 of Homo sapiens. Against these two non-conserved motifs, we generated a peptide library of 60 peptides on the basis of physicochemical properties. These peptides underwent a filtering process based on various parameters, including feasibility of synthesis and purification, compatibility with Selective Reaction Monitoring (SRM)/Multiple reaction monitoring (MRM), hydrophobicity, hydropathy index, average molecular weight (Mw average), monoisotopic molecular weight (Mw monoisotopic), theoretical isoelectric point (pI), and half-life. Further filtering criterion shortlisted three peptides by using molecular docking and molecular dynamics simulations. The direct interaction between ATG8 and the shortlisted peptides was confirmed through Surface Plasmon Resonance (SPR) experiments. Notably, these peptides exhibited the remarkable ability to penetrate the parasite membrane and exert profound effects on Leishmania major. The treatment with these peptides significantly impacted parasite survival, leading to alterations in the cell cycle and morphology. Furthermore, the peptides were found to modulate autophagosome formation, particularly under starved conditions, suggesting their involvement in disrupting the regulation of autophagy within Leishmania major. In vitro, studies demonstrated that the selected peptides effectively reduced the parasite load within infected host cells. Encouragingly, these findings were corroborated by in vivo experiments, which showed a reduction in parasite burden upon peptide administration. Additionally, the peptides were observed to affect the levels of LC3II within host cells. In conclusion, our findings highlight the efficacy of these novel peptides in targeting Leishmania major’s ATG8 and disrupting parasite survival. These results provide valuable insights into the development of innovative therapeutic strategies against leishmaniasis via targeting autophagy protein ATG8 of Leishmania major.

Keywords: ATG8, leishmaniasis, surface plasmon resonance, MD simulation, molecular docking, peptide designing, therapeutics

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Authors: Rizwan Sultan, Hasnain Zafar

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Trauma is the leading cause of death among young population not only in USA but Pakistan as well. The high prevalence of disease should result in larger amount of data and larger number of publications resulting in exploring room for improvement in the field. We aimed to review trauma literature generated from Pakistan in journals indexed with PubMed from January 2010 to December 2014. Search using term “Trauma AND Pakistan” filtering for relevant dates and species human was done on Pubmed. The abstracts and articles were reviewed by the authors to collect data on a preformed performa. 114 articles were published from Pakistan during these 5 years. 64% articles were published in international journals. 63% articles were published in journals with impact factor less than 1. 54% articles were published from one of the four provinces of Pakistan. 64% of articles provided level 4 while 14% articles provided level 5 evidence on the topic. 55% articles discussed epidemiology in non-representative populations. Trauma literature from Pakistan is not only lacking significantly but is also of poor quality and is unable to offer conclusions on this particular subject. There is a lot of space for improvement in the upcoming years.

Keywords: trauma, literature, Pakistan, level of evidence

Procedia PDF Downloads 331

Authors: T. Godwin

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The design of a supply chain always entails the trade-off between responsiveness and efficiency. The launch of e-commerce has not only changed the way of shopping but also altered the supply chain design while trading off efficiency with responsiveness. A concept called ‘virtual responsiveness’ is introduced in the context of e-commerce supply chain. A simulation model is developed to compare actual responsiveness and virtual responsiveness to the customer in an e-commerce supply chain. The simulation is restricted to the movement of goods from the e-tailer to the customer. Customer demand follows a statistical distribution and is generated using inverse transformation technique. The two responsiveness schemes of the supply chain are compared in terms of the minimum number of inventory required at the e-tailer to fulfill the orders. Computational results show the savings achieved through virtual responsiveness. The insights gained from this study could be used to redesign e-commerce supply chain by incorporating virtual responsiveness. A part of the achieved cost savings could be passed back to the customer, thereby making the supply chain both effective and competitive.

Keywords: e-commerce, simulation modeling, supply chain, virtual responsiveness

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Authors: M. S. Khurram, S. A. Memon, S. Khan

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Bed voidage behavior among different flow regimes for Geldart A, B, and D particles (fluid catalytic cracking catalyst (FCC), particle A and glass beads) of diameter range 57-872 μm, apparent density 1470-3092 kg/m³, and bulk density range 890-1773 kg/m³ were investigated in a gas-solid circulating fluidized bed of 0.1 m-i.d. and 2.56 m-height of plexi-glass. Effects of variables (gas velocity, particle properties, and static bed height) were analyzed on bed voidage. The axial voidage profile showed a typical trend along the riser: a dense bed at the lower part followed by a transition in the splash zone and a lean phase in the freeboard. Bed expansion and dense bed voidage increased with an increase of gas velocity as usual. From experimental results, a generalized model relationship based on inverse fluidization number for dense bed voidage from bubbling to fast fluidization regimes was presented.

Keywords: axial voidage, circulating fluidized bed, splash zone, static bed

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Authors: Ramesh Rajagopalan, Adam Dahlstrom

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Noise removal techniques play a vital role in the performance of electrocardiographic (ECG) signal processing systems. ECG signals can be corrupted by various kinds of noise such as baseline wander noise, electromyographic interference, and power-line interference. One of the significant challenges in ECG signal processing is the degradation caused by additive 50 or 60 Hz power-line interference. This work investigates the removal of power line interference and suppression of transient response for filtering noise corrupted ECG signals. We demonstrate the effectiveness of Infinite Impulse Response (IIR) notch filter with time varying pole radius for improving the transient behavior. The temporary change in the pole radius of the filter diminishes the transient behavior. Simulation results show that the proposed IIR filter with time varying pole radius outperforms traditional IIR notch filters in terms of mean square error and transient suppression.

Keywords: notch filter, ECG, transient, pole radius

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Authors: A. Senthil Kumar, V. Murali Bhaskaran

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In the information technology ground, people are using various tools and software for their official use and personal reasons. Nowadays, people are worrying to choose data accessing and extraction tools at the time of buying and selling their products. In addition, worry about various quality factors such as price, durability, color, size, and availability of the product. The main purpose of the research study is to find solutions to these unsolved existing problems. The proposed algorithm is a Multidirectional Rank Prediction (MDRP) decision making algorithm in order to take an effective strategic decision at all the levels of data extraction, uses a real time textile dataset and analyzes the results. Finally, the results are obtained and compared with the existing measurement methods such as PCC, SLCF, and VSS. The result accuracy is higher than the existing rank prediction methods.

Keywords: Knowledge Discovery in Database (KDD), Multidirectional Rank Prediction (MDRP), Pearson’s Correlation Coefficient (PCC), VSS (Vector Space Similarity)

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Authors: Gholamreza Ghodrati Amiri, Mojtaba Jafarian Abyaneh, Ali Zare Hosseinzadeh

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This paper presents an effective model updating strategy for damage localization and quantification in frames by defining damage detection problem as an optimization issue. A generalized version of the Modal Residual Force (MRF) is employed for presenting a new damage-sensitive cost function. Then, Grey Wolf Optimization (GWO) algorithm is utilized for solving suggested inverse problem and the global extremums are reported as damage detection results. The applicability of the presented method is investigated by studying different damage patterns on the benchmark problem of the IASC-ASCE, as well as a planar shear frame structure. The obtained results emphasize good performance of the method not only in free-noise cases, but also when the input data are contaminated with different levels of noises.

Keywords: frame, grey wolf optimization algorithm, modal residual force, structural damage detection

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Authors: Cemil Turan, Mohammad Shukri Salman

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The convergence rate of the least-mean-square (LMS) algorithm deteriorates if the input signal to the filter is correlated. In a system identification problem, this convergence rate can be improved if the signal is white and/or if the system is sparse. We recently proposed a sparse transform domain LMS-type algorithm that uses a variable step-size for a sparse system identification. The proposed algorithm provided high performance even if the input signal is highly correlated. In this work, we investigate the performance of the proposed TD-LMS algorithm for a large number of filter tap which is also a critical issue for standard LMS algorithm. Additionally, the optimum value of the most important parameter is calculated for all experiments. Moreover, the convergence analysis of the proposed algorithm is provided. The performance of the proposed algorithm has been compared to different algorithms in a sparse system identification setting of different sparsity levels and different number of filter taps. Simulations have shown that the proposed algorithm has prominent performance compared to the other algorithms.

Keywords: adaptive filtering, sparse system identification, TD-LMS algorithm, VSSLMS algorithm

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Authors: Misrak Assefa

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The Amharic language is the second most widely spoken Semitic language in the world. There are several new overloaded on the web. Searching some useful documents from the web on a specific topic, which is written in the Amharic language, is a challenging task. Hence, document categorization is required for managing and filtering important information. In the classification of Amharic text news, there is still a gap in the domain of information that needs to be launch. This study attempts to design an automatic Amharic news classification using a supervised learning mechanism on four un-touch classes. To achieve this research, 4,182 news articles were used. Naive Bayes (NB) and Decision tree (j48) algorithms were used to classify the given Amharic dataset. In this paper, k-fold cross-validation is used to estimate the accuracy of the classifier. As a result, it shows those algorithms can be applicable in Amharic news categorization. The best average accuracy result is achieved by j48 decision tree and naïve Bayes is 95.2345 %, and 94.6245 % respectively using three categories. This research indicated that a typical decision tree algorithm is more applicable to Amharic news categorization.

Keywords: text categorization, supervised machine learning, naive Bayes, decision tree

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Authors: Zylfi Shehu, Shpetim Madani, Bashkim Delia

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Context: The study focuses on the interest and engagement of high school students in Tirana, Albania, in physical activity, sports, and foreign languages. It acknowledges the numerous physiological benefits of physical activity, such as cardiovascular health and improved mood. It also recognizes the importance of physical activity in childhood and adolescence for proper skeletal development and long-term health. Research Aim: The main purpose of the study is to investigate and analyze the preferences and interests of male and female high school students in Tirana regarding their functional development, physical activity, sports participation, and choice of foreign languages. The aim is to provide insights for the students and teachers to guide future objectives and improve the quality of physical education. Methodology: The study employed a survey-based approach, targeting both male and female students in public high schools in Tirana. A total of 410 students aged 15 to 19 years old, participated in the study. The data collected from the survey were processed using Excel and presented through tables and graphs. Findings: The results revealed that team sports were more favored by the students, with football being the preferred choice among males, while basketball and volleyball were more popular among females. Additionally, English was found to be the most preferred foreign language, selected by a higher percentage of females (38.57%) compared to males (16.90%). German followed as the second preferred language. Theoretical Importance: This study contributes to the understanding of students' interests in physical activity, sports, and foreign languages in Tirana's high schools. The findings highlight the need to focus on specific sports and languages to cater to students' preferences and guide future educational objectives. It also emphasizes the importance of physical education in promoting students' overall well-being and highlights potential areas for policy and program improvement. Data Collection and Analysis Procedures: The study collected data through surveys administered to high school students in Tirana. The survey responses were processed and analyzed using Excel, and the findings were presented through tables and graphs. The data analysis allowed for the identification of preferences and trends among male and female students, providing valuable insights for future decision-making. Question Addressed: The study aimed to address the question of high school students' interest in physical activity, sports, and foreign languages. It sought to understand the preferences and choices made by students in Tirana and investigate factors such as gender, family income, and accessibility to extracurricular sports activities. Conclusion: The study revealed that high school students in Tirana show a preference for team sports, with football being the most favored among males and basketball and volleyball among females. English was found to be the most preferred foreign language. The findings provide important insights for educators and policymakers to enhance physical education programs and consider students' preferences and interests to foster a more effective learning environment. The study also emphasizes the importance of physical activity and sports in promoting students' physical and mental well-being.

Keywords: female, male, foreign languages, sports, physical education, high school students

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Authors: Yihao Kuang, Bowen Ding

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With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graphs and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improved strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain a better and more efficient inference effect by introducing PPO into knowledge inference technology.

Keywords: reinforcement learning, PPO, knowledge inference

Procedia PDF Downloads 244

Authors: Mohammed Affanuddin H. Siddique, Jayesh S. Shukla, Chetan B. Meshram

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The neural networks are one of the power tools of machine learning. After the invention of perceptron in early 1980's, the neural networks and its application have grown rapidly. Neural networks are a technique originally developed for pattern investigation. The structure of a neural network consists of neurons connected through synapse. Here, we have investigated the different algorithms and cost function reduction techniques for optimization of vertical axis wind turbine (VAWT) rotor blades. The aerodynamic force coefficients corresponding to the airfoils are stored in a database along with the airfoil coordinates. A forward propagation neural network is created with the input as aerodynamic coefficients and output as the airfoil co-ordinates. In the proposed algorithm, the hidden layer is incorporated into cost function having linear and non-linear error terms. In this article, it is observed that the ANNs (Artificial Neural Network) can be used for the VAWT’s optimization.

Keywords: VAWT, ANN, optimization, inverse design

Procedia PDF Downloads 325

Authors: Vahid Khadjeh Anvary, Hamideh Karimi Yazdi

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Generally, the tool of comparison between performance of each stage in the life of a project, is the number of project progress during that period, which in most cases is only determined as one-dimensional with referring to one of three factors (physical, time, and financial). In many projects in developing countries there are controversies on accuracy and the way of analyzing progress report of projects that hinders getting definitive and engineering conclusions on the status of project.Identifying weakness points of this kind of one-dimensional look on project and determining a reliable and engineering approach for multi-dimensional decoding information receivable from project is of great importance in project management.This can be a tool to help identification of hidden diseases of project before appearing irreversible symptoms that are usually delays or increased costs of execution. The method used in this paper is defining and evaluating a hypothetical project as an example analyzing different scenarios and numerical comparison of them along with related graphs and tables. Finally, by analyzing different possible scenarios in the project, possibility or impossibility of predicting their occurrence is examine through the evidence.

Keywords: physical progress, time progress, financial progress, delays, critical path

Procedia PDF Downloads 374

Authors: Ahmed Abdulla Ashlam, Atta Badii, Frederic Stahl

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In recent years, SQL injection attacks have been identified as being prevalent against web applications. They affect network security and user data, which leads to a considerable loss of money and data every year. This paper presents the use of classification algorithms in machine learning using a method to classify the login data filtering inputs into "SQLi" or "Non-SQLi,” thus increasing the reliability and accuracy of results in terms of deciding whether an operation is an attack or a valid operation. A method Web-App auto-generated twin data structure replication. Shielding against SQLi attacks (WebAppShield) that verifies all users and prevents attackers (SQLi attacks) from entering and or accessing the database, which the machine learning module predicts as "Non-SQLi" has been developed. A special login form has been developed with a special instance of data validation; this verification process secures the web application from its early stages. The system has been tested and validated, up to 99% of SQLi attacks have been prevented.

Keywords: SQL injection, attacks, web application, accuracy, database

Procedia PDF Downloads 153

Authors: Nima Pirhadi, Yong Bo Shao, Xusheng Wa, Jianguo Lu

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Several models based on the simplified method introduced by Seed and Idriss (1971) have been developed to assess the liquefaction potential of saturated sandy soils. The procedure includes determining the cyclic resistance of the soil as the cyclic resistance ratio (CRR) and comparing it with earthquake loads as cyclic stress ratio (CSR). Of all methods to determine CRR, the methods using shear-wave velocity (Vs) are common because of their low sensitivity to the penetration resistance reduction caused by fine content (FC). To evaluate the capability of the models, based on the Vs., the new data from Bachu-Jianshi earthquake case history collected, then the prediction results of the models are compared to the measured results; consequently, the accuracy of the models are discussed via three criteria and graphs. The evaluation demonstrates reasonable accuracy of the models in the Banchu region.

Keywords: seismic liquefaction, banchu-jiashi earthquake, shear-wave velocity, liquefaction potential evaluation

Procedia PDF Downloads 241

Authors: Mohd Zamri Osman, Mohd Aizaini Maarof, Mohd Foad Rohani

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Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969.

Keywords: color space, neural network, random forest, skin detection, statistical feature

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Authors: Hoyeon Park, Yunhwan Keon, Kyoung-Jae Kim

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Location Based Social Network services (LBSNs) is a new term that combines location based service and social network service (SNS). Unlike traditional SNS, LBSNs emphasizes empirical elements in the user's actual physical location. Point-of-Interest (POI) is the most important factor to implement LBSNs recommendation system. POI information is the most popular spot in the area. In this study, we would like to recommend POI to users in a specific area through recommendation system using collaborative filtering. The process is as follows: first, we will use different data sets based on Seoul and New York to find interesting results on human behavior. Secondly, based on the location-based activity information obtained from the personalized LBSNs, we have devised a new rating that defines the user's preference for the area. Finally, we have developed an automated rating algorithm from massive raw data using distributed systems to reduce advertising costs of LBSNs.

Keywords: location-based social network services, point-of-interest, recommender systems, business analytics

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Authors: Mohamad Kassab, Amal El Fallah Seghrouchni, Frederic Barbaresco, Raed Abu Zitar

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Detecting objects such as drones is a challenging task as their relative size and maneuvering capabilities deceive machine learning models and cause them to misclassify drones as birds or other objects. In this work, we investigate applying several deep learning techniques to benchmark real data sets of flying drones. A deep learning paradigm is proposed for the purpose of mitigating the complexity of those systems. The proposed paradigm consists of a hybrid between the AdderNet deep learning paradigm and the Single Shot Detector (SSD) paradigm. The goal was to minimize multiplication operations numbers in the filtering layers within the proposed system and, hence, reduce complexity. Some standard machine learning technique, such as SVM, is also tested and compared to other deep learning systems. The data sets used for training and testing were either complete or filtered in order to remove the images with mall objects. The types of data were RGB or IR data. Comparisons were made between all these types, and conclusions were presented.

Keywords: drones detection, deep learning, birds versus drones, precision of detection, AdderNet

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Authors: Bin Wang, Yong Xu, Honggang Qu, Rongmei Liu, Zhenji Gao

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Today's mining industry is advancing gradually toward digital and visual direction. The three dimensional visualization geological modeling of mine is the digital characterization of mineral deposit, and is one of the key technology of digital mine. The three-dimensional geological modeling is a technology that combines the geological spatial information management, geological interpretation, geological spatial analysis and prediction, geostatistical analysis, entity content analysis and graphic visualization in three-dimensional environment with computer technology, and is used in geological analysis. In this paper, the three-dimensional geological modeling of an iron mine through the use of Surpac is constructed, and the weight difference of the estimation methods between distance power inverse ratio method and ordinary kriging is studied, and the ore body volume and reserves are simulated and calculated by using these two methods. Compared with the actual mine reserves, its result is relatively accurate, so it provided scientific bases for mine resource assessment, reserve calculation, mining design and so on.

Keywords: three-dimensional geological modeling, geological database, geostatistics, block model

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Authors: S. U. Durgadsimi, S. Chouguleb, S. Belladc

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(y) Li0.5Ni0.7Zn0.05Fe2O4 + (1-y) Ba0.5Sr0.5TiO3 magnetoelectric composites with y = 0.1, 0.3 and 0.5 were prepared by a conventional standard double sintering ceramic technique. X-ray diffraction analysis confirmed the phase formation of ferrite, ferroelectric and their composites. logρdc Vs 1/T graphs reveal that the dc resistivity decreases with increasing temperature exhibiting semiconductor behavior. The plots of logσac Vs logω2 are almost linear indicating that the conductivity increases with increase in frequency i.e, conductivity in the composites is due to small polaron hopping. Dielectric constant (έ) and dielectric loss (tan δ) were studied as a function of frequency in the range 100Hz–1MHz which reveals the normal dielectric behavior except the composite with y=0.1 and as a function of temperature at four fixed frequencies (i.e. 100Hz, 1KHz, 10KHz, 100KHz). ME voltage coefficient decreases with increase in ferrite content and was observed to be maximum of about 7.495 mV/cmOe for (0.1) Li0.5Ni0.7Zn0.05Fe2O4 + (0.9) Ba0.5Sr0.5TiO3 composite.

Keywords: XRD, dielectric constant, dielectric loss, DC and AC conductivity, ME voltage coefficient

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Authors: Bikshalu Kalagadda, Srikanth Rangu

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The important object of images segmentation is to extract objects with respect to some input features. One of the important methods for image segmentation is Level set method. Generally medical images and synthetic images with low contrast of pixel profile, for such images difficult to locate interested features in images. In conventional level set function, develops irregularity during its process of evaluation of contour of objects, this destroy the stability of evolution process. For this problem a remedy is proposed, a new hybrid algorithm is Clustering Level Set Evolution. Kernel fuzzy particles swarm optimization clustering with the Distance Regularized Level Set (DRLS) and Selective Binary, and Gaussian Filtering Regularized Level Set (SBGFRLS) methods are used. The ability of identifying different regions becomes easy with improved speed. Efficiency of the modified method can be evaluated by comparing with the previous method for similar specifications. Comparison can be carried out by considering medical and synthetic images.

Keywords: segmentation, clustering, level set function, re-initialization, Kernel fuzzy, swarm optimization

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Authors: Reneiloe Seodigeng, Malwandla Hanabe, Haleden Chiririwa, Hilary Rutto, Tumisang Seodigeng

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Neutralisation of acid-mine drainage (AMD) using limestone is cost effective, and good results can be obtained. However, this process has its limitations; it cannot be used for highly acidic water which consists of Fe(III). When Fe(III) reacts with CaCO₃, it results in armoring. Armoring slows the reaction, and additional alkalinity can no longer be generated. Limestone is easily accessible, so this problem can be easily dealt with. Experiments were carried out to evaluate the effect of PVC pipe length on ferric and ferrous ions. It was found that the shorter the pipe length the more these dissolved metals precipitate. The effect of the pipe length on the hydrogen ions was also studied, and it was found that these two have an inverse relationship. Experimental data were further compared with the model prediction data to see if they behave in a similar fashion. The model was able to predict the behaviour of 1.5m and 2 m pipes in ferric and ferrous ion precipitation.

Keywords: acid mine drainage, neutralisation, limestone, mathematical modelling

Procedia PDF Downloads 365

Authors: Ather Saeed, Arif Khan, Jeffrey Gosper

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Sensor devices are prone to errors and sudden node failures, which are difficult to detect in a timely manner when deployed in real-time, hazardous, large-scale harsh environments and in medical emergencies. Therefore, the loss of data can be life-threatening when the sensed phenomenon is not disseminated due to sudden node failure, battery depletion or temporary malfunctioning. We introduce a set of partial differential equations for localizing faults, similar to Green’s and Maxwell’s equations used in Electrostatics and Electromagnetism. We introduce a node organization and clustering scheme for self-stabilizing sensor networks. Green’s theorem is applied to regions where the curve is closed and continuously differentiable to ensure network connectivity. Experimental results show that the proposed GTFD (Green’s Theorem fault-detection and Self-stabilization) protocol not only detects faulty nodes but also accurately generates network stability graphs where urgent intervention is required for dynamically self-stabilizing the network.

Keywords: Green’s Theorem, self-stabilization, fault-localization, RSSI, WSN, clustering

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Authors: Hina Iqbal, Mustansar Ali Ghazanfar, Sandor Szedmak

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Recommender systems uses artificial intelligence practices for filtering obscure information and can predict if a user likes a specified item. Kernel mapping Recommender systems have been proposed which are accurate and state-of-the-art algorithms and resolve recommender system’s design objectives such as; long tail, cold-start, and sparsity. The aim of research is to propose hybrid framework that can efficiently integrate different versions— namely item-based and user-based KMR— of KMR algorithm. We have proposed various heuristic algorithms that integrate different versions of KMR (into a unified framework) resulting in improved accuracy and elimination of problems associated with conventional recommender system. We have tested our system on publically available movies dataset and benchmark with KMR. The results (in terms of accuracy, precision, recall, F1 measure and ROC metrics) reveal that the proposed algorithm is quite accurate especially under cold-start and sparse scenarios.

Keywords: Kernel Mapping Recommender Systems, hybrid recommender systems, cold start, sparsity, long tail

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Authors: Shima Soltanzadeh, Mohammad Hosain Fazel Zarandi, Mojtaba Barzegar Astanjin

Abstract:

Diagnosis of male infertility by the laboratory tests is expensive and, sometimes it is intolerable for patients. Filling out the questionnaire and then using classification method can be the first step in decision-making process, so only in the cases with a high probability of infertility we can use the laboratory tests. In this paper, we evaluated the performance of four classification methods including naive Bayesian, neural network, logistic regression and fuzzy c-means clustering as a classification, in the diagnosis of male infertility due to environmental factors. Since the data are unbalanced, the ROC curves are most suitable method for the comparison. In this paper, we also have selected the more important features using a filtering method and examined the impact of this feature reduction on the performance of each methods; generally, most of the methods had better performance after applying the filter. We have showed that using fuzzy c-means clustering as a classification has a good performance according to the ROC curves and its performance is comparable to other classification methods like logistic regression.

Keywords: classification, fuzzy c-means, logistic regression, Naive Bayesian, neural network, ROC curve

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Authors: Redouane Tlemsani, Redouane, Belkacem Kouninef, Abdelkader Benyettou

Abstract:

In this paper, our system is a Markovien system which we can see it like a Dynamic Bayesian Networks. One of the major interests of these systems resides in the complete training of the models (topology and parameters) starting from training data. The Bayesian Networks are representing models of dubious knowledge on complex phenomena. They are a union between the theory of probability and the graph theory in order to give effective tools to represent a joined probability distribution on a set of random variables. The representation of knowledge bases on description, by graphs, relations of causality existing between the variables defining the field of study. The theory of Dynamic Bayesian Networks is a generalization of the Bayesians networks to the dynamic processes. Our objective amounts finding the better structure which represents the relationships (dependencies) between the variables of a dynamic bayesian network. In applications in pattern recognition, one will carry out the fixing of the structure which obliges us to admit some strong assumptions (for example independence between some variables).

Keywords: Arabic on line character recognition, dynamic Bayesian network, pattern recognition, networks

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Authors: Honggang Qu, Yong Xu, Rongmei Liu, Zhenji Gao, Bin Wang

Abstract:

Today's mining industry is advancing gradually toward digital and visual direction. The three-dimensional visualization geological modeling of mine is the digital characterization of mineral deposits and is one of the key technology of digital mining. Three-dimensional geological modeling is a technology that combines geological spatial information management, geological interpretation, geological spatial analysis and prediction, geostatistical analysis, entity content analysis and graphic visualization in a three-dimensional environment with computer technology and is used in geological analysis. In this paper, the three-dimensional geological modeling of an iron mine through the use of Surpac is constructed, and the weight difference of the estimation methods between the distance power inverse ratio method and ordinary kriging is studied, and the ore body volume and reserves are simulated and calculated by using these two methods. Compared with the actual mine reserves, its result is relatively accurate, so it provides scientific bases for mine resource assessment, reserve calculation, mining design and so on.

Keywords: three-dimensional geological modeling, geological database, geostatistics, block model

Procedia PDF Downloads 80