Search results for: artificial writers
264 A Neuro-Automata Decision Support System for the Control of Late Blight in Tomato Crops
Authors: Gizelle K. Vianna, Gustavo S. Oliveira, Gabriel V. Cunha
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The use of decision support systems in agriculture may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. In our work, we designed and implemented a decision support system for small tomatoes producers. This work investigates ways to recognize the late blight disease from the analysis of digital images of tomatoes, using a pair of multilayer perceptron neural networks. The networks outputs are used to generate repainted tomato images in which the injuries on the plant are highlighted, and to calculate the damage level of each plant. Those levels are then used to construct a situation map of a farm where a cellular automata simulates the outbreak evolution over the fields. The simulator can test different pesticides actions, helping in the decision on when to start the spraying and in the analysis of losses and gains of each choice of action.
Keywords: Artificial neural networks, cellular automata, decision support system, pattern recognition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1043263 A Multi-Objective Evolutionary Algorithm of Neural Network for Medical Diseases Problems
Authors: Sultan Noman Qasem
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This paper presents an evolutionary algorithm for solving multi-objective optimization problems-based artificial neural network (ANN). The multi-objective evolutionary algorithm used in this study is genetic algorithm while ANN used is radial basis function network (RBFN). The proposed algorithm named memetic elitist Pareto non-dominated sorting genetic algorithm-based RBFN (MEPGAN). The proposed algorithm is implemented on medical diseases problems. The experimental results indicate that the proposed algorithm is viable, and provides an effective means to design multi-objective RBFNs with good generalization capability and compact network structure. This study shows that MEPGAN generates RBFNs coming with an appropriate balance between accuracy and simplicity, comparing to the other algorithms found in literature.
Keywords: Radial basis function network, Hybrid learning, Multi-objective optimization, Genetic algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2243262 Word Stemming Algorithms and Retrieval Effectiveness in Malay and Arabic Documents Retrieval Systems
Authors: Tengku Mohd T. Sembok
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Documents retrieval in Information Retrieval Systems (IRS) is generally about understanding of information in the documents concern. The more the system able to understand the contents of documents the more effective will be the retrieval outcomes. But understanding of the contents is a very complex task. Conventional IRS apply algorithms that can only approximate the meaning of document contents through keywords approach using vector space model. Keywords may be unstemmed or stemmed. When keywords are stemmed and conflated in retrieving process, we are a step forwards in applying semantic technology in IRS. Word stemming is a process in morphological analysis under natural language processing, before syntactic and semantic analysis. We have developed algorithms for Malay and Arabic and incorporated stemming in our experimental systems in order to measure retrieval effectiveness. The results have shown that the retrieval effectiveness has increased when stemming is used in the systems.Keywords: Information Retrieval, Natural Language Processing, Artificial Intelligence.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2250261 Congo Red Photocatalytic Decolourization using Modified Titanium
Authors: A. López–Vásquez, D. Santamaría, M. Tibatá, C. Gómez
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A study concerning the photocatalytic decolourization of Congo red (CR) dye, over artificial UV irradiation is presented. Photocatalysts based on a commercial titanium dioxide (TiO2) modified with transition metals (Ni, Cu and Zn) were used. The dopage method used was wet impregnation. A TiO2 sample without salt was subjected to the same hydrothermal treatment to be used as reference. Congo red solutions to several pH conditions (natural and basic) were used to evaluate photocatalytic performance of each doped catalysts. Photodecolourization percentage was measured spectrofotrometically after 3 h of treatment to 499 nm as response variable. Kinetics investigations of photodegradation indicated that reactions obey to Langmuir-Hinshelwood model and pseudo–first order law. The rate constant studies of photocatalytic decolourization reactions for Zn–TiO2 and Cu–TiO2 photocatalysts indicated that in all cases the rate constant of the reaction was higher than that of TiO2 undoped. These results show that nature of the metal modifying the TiO2 influence on the efficiency of the photocatalyst evaluated in process. Ni does not present an additional effect compared with TiO2, while Zn enhances the photoactivity due to its electronic properties.Keywords: Congo red, Dopage, Photodecolourization, Titanium dioxide.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2154260 An Intelligent System Framework for Generating Activity List of a Project Using WBS Mind map and Semantic Network
Authors: H. Iranmanesh, M. Madadi
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Work Breakdown Structure (WBS) is one of the most vital planning processes of the project management since it is considered to be the fundamental of other processes like scheduling, controlling, assigning responsibilities, etc. In fact WBS or activity list is the heart of a project and omission of a simple task can lead to an irrecoverable result. There are some tools in order to generate a project WBS. One of the most powerful tools is mind mapping which is the basis of this article. Mind map is a method for thinking together and helps a project manager to stimulate the mind of project team members to generate project WBS. Here we try to generate a WBS of a sample project involving with the building construction using the aid of mind map and the artificial intelligence (AI) programming language. Since mind map structure can not represent data in a computerized way, we convert it to a semantic network which can be used by the computer and then extract the final WBS from the semantic network by the prolog programming language. This method will result a comprehensive WBS and decrease the probability of omitting project tasks.Keywords: Expert System, Mind map, Semantic network, Work breakdown structure,
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2591259 Optimal Placement and Sizing of Distributed Generation in Microgrid for Power Loss Reduction and Voltage Profile Improvement
Authors: Ferinar Moaidi, Mahdi Moaidi
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Environmental issues and the ever-increasing in demand of electrical energy make it necessary to have distributed generation (DG) resources in the power system. In this research, in order to realize the goals of reducing losses and improving the voltage profile in a microgrid, the allocation and sizing of DGs have been used. The proposed Genetic Algorithm (GA) is described from the array of artificial intelligence methods for solving the problem. The algorithm is implemented on the IEEE 33 buses network. This study is presented in two scenarios, primarily to illustrate the effect of location and determination of DGs has been done to reduce losses and improve the voltage profile. On the other hand, decisions made with the one-level assumptions of load are not universally accepted for all levels of load. Therefore, in this study, load modelling is performed and the results are presented for multi-levels load state.Keywords: Distributed generation, genetic algorithm, microgrid, load modelling, loss reduction, voltage improvement.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1042258 Energy Loss at Drops using Neuro Solutions
Authors: Farzin Salmasi
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Energy dissipation in drops has been investigated by physical models. After determination of effective parameters on the phenomenon, three drops with different heights have been constructed from Plexiglas. They have been installed in two existing flumes in the hydraulic laboratory. Several runs of physical models have been undertaken to measured required parameters for determination of the energy dissipation. Results showed that the energy dissipation in drops depend on the drop height and discharge. Predicted relative energy dissipations varied from 10.0% to 94.3%. This work has also indicated that the energy loss at drop is mainly due to the mixing of the jet with the pool behind the jet that causes air bubble entrainment in the flow. Statistical model has been developed to predict the energy dissipation in vertical drops denotes nonlinear correlation between effective parameters. Further an artificial neural networks (ANNs) approach was used in this paper to develop an explicit procedure for calculating energy loss at drops using NeuroSolutions. Trained network was able to predict the response with R2 and RMSE 0.977 and 0.0085 respectively. The performance of ANN was found effective when compared to regression equations in predicting the energy loss.Keywords: Air bubble, drop, energy loss, hydraulic jump, NeuroSolutions
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1632257 Callusing in Stevia rebaudiana (Natural Sweetener) for Steviol Glycoside Production
Authors: Pratibha Gupta, Satyawati Sharma, Sanjay Saxena
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Stevia rebaudiana Bertoni (natural sweetener) belongs to Asteraceae family and can be used as substitute of artificial sweeteners for diabetic patients. Conventionally, it is cultivated by seeds or stem cutting, but seed viability rate is poor. A protocol for callus induction and multiplication was developed to produce large no. of calli in short period. Surface sterilized nodal, leaf and root explants were cultured on Murashige and Skoog (MS) medium with different concentrations of plant hormone like, IBA, kinetin, NAA, 2,4-D, and NAA in combination with 2,4-D. 100% callusing was observed from leaf explants cultured on combination of NAA and 2,4-D after three weeks while with 2,4-D, only 10% callusing was observed. Calli obtained from leaf and root explants were shiny green while with nodal explants it was hard and brown. The present findings deal with induction of callusing in Stevia to achieve the rapid callus multiplication for study of steviol glycosides in callus culture.Keywords: 2, 4-D, Callusing, NAA, Stevia, Steviol glycosides
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3240256 Study of the Effectiveness of Solar Heat Gain and Day Light Factors on Minimizing Electricity Use in High Rise Buildings
Authors: Mansour Nikpour, Mohd Zin kandar, Mohsen Ghasemi, Hossein Fallah
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Over half of the total electricity consumption is used in buildings. Air-conditioning and electric lighting are the two main resources of electricity consumption in high rise buildings. One way to reduce electricity consumption would be to limit heat gain into buildings, therefore reduce the demand for air-conditioning during hot summer months especially in hot regions. On the other hand natural daylight can be used to reduce the use of electricity for artificial lighting. In this paper effective factors on minimizing heat gain and achieving required day light were reviewed .As daylight always accompanied by solar heat gain. Also interactions between heat gain and daylight were discussed through previous studies and equations which are related to heat gain and day lighting especially in high rise buildings. As a result importance of building-s form and its component on energy consumption in buildings were clarified.
Keywords: High rise buildings, energy demand, day lighting, heat gain.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2886255 Column Size for R.C. Frames with High Drift
Authors: Sunil S. Mayengbam, S. Choudhury
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A method to predict the column size for displacement based design of reinforced concrete frame buildings with higher target inter storey drift is reported here. The column depth derived from empirical relation as a function of given beam section, target inter-story drift, building plan features and common displacement based design parameters is used. Regarding the high drift requirement, a minimum column-beam moment capacity ratio is maintained during capacity design. The method is used in designing four, eight and twelve story frame buildings with displacement based design for three percent target inter storey drift. Non linear time history analysis of the designed buildings are performed under five artificial ground motions to show that the columns are found elastic enough to avoid column sway mechanism assuring that for the design the column size can be used with or without minor changes.
Keywords: Column size, point of contra flexure, displacement based design, capacity design.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 27305254 Evaluating Hurst Parameters and Fractal Dimensions of Surveyed Dataset of Tailings Dam Embankment
Authors: I. Yakubu, Y. Y. Ziggah, C. Yeboah
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In the mining environment, tailings dam embankment is among the hazards and risk areas. The tailings dam embankment could fail and result to damages to facilities, human injuries or even fatalities. Periodic monitoring of the dam embankment is needed to help assess the safety of the tailings dam embankment. Artificial intelligence techniques such as fractals can be used to analyse the stability of the monitored dataset from survey measurement techniques. In this paper, the fractal dimension (D) was determined using D = 2-H. The Hurst parameters (H) of each monitored prism were determined by using a time domain of rescaled range programming in MATLAB software. The fractal dimensions of each monitored prism were determined based on the values of H. The results reveal that the values of the determined H were all within the threshold of 0 ≤ H ≤ 1 m. The smaller the H, the bigger the fractal dimension is. Fractal dimension values ranging from 1.359 x 10-4 m to 1.8843 x 10-3 m were obtained from the monitored prisms on the based on the tailing dam embankment dataset used. The ranges of values obtained indicate that the tailings dam embankment is stable.Keywords: Hurst parameter, fractal dimension, tailings dam embankment, surveyed dataset.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 741253 Cognitive Radio Spectrum Management
Authors: Swapnil Singhal, Santosh Kumar Singh
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The emerging Cognitive Radio is combo of both the technologies i.e. Radio dynamics and software technology. It involve wireless system with efficient coding, designing, and making them artificial intelligent to take the decision according to the surrounding environment and adopt themselves accordingly, so as to deliver the best QoS. This is the breakthrough from fixed hardware and fixed utilization of the spectrum. This software-defined approach of research is centralized at user-definition and application driven model, various software method are used for the optimization of the wireless communication. This paper focused on the Spectrum allocation technique using genetic algorithm GA to evolve radio, represented by chromosomes. The chromosomes gene represents the adjustable parameters in given radio and by using GA, evolving over the generations, the optimized set of parameters are evolved, as per the requirement of user and availability of the spectrum, in our prototype the gene consist of 6 different parameters, and the best set of parameters are evolved according to the application need and availability of the spectrum holes and thus maintaining best QoS for user, simultaneously maintaining licensed user rights. The analyzing tool Matlab is used for the performance of the prototype.
Keywords: ASDR, Cognitive Radio, QoS, Spectrum.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2046252 The Use of Dynamically Optimised High Frequency Moving Average Strategies for Intraday Trading
Authors: Abdalla Kablan, Joseph Falzon
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This paper is motivated by the aspect of uncertainty in financial decision making, and how artificial intelligence and soft computing, with its uncertainty reducing aspects can be used for algorithmic trading applications that trade in high frequency. This paper presents an optimized high frequency trading system that has been combined with various moving averages to produce a hybrid system that outperforms trading systems that rely solely on moving averages. The paper optimizes an adaptive neuro-fuzzy inference system that takes both the price and its moving average as input, learns to predict price movements from training data consisting of intraday data, dynamically switches between the best performing moving averages, and performs decision making of when to buy or sell a certain currency in high frequency.Keywords: Financial decision making, High frequency trading, Adaprive neuro-fuzzy systems, moving average strategy.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 5053251 An Intelligent Human-Computer Interaction System for Decision Support
Authors: Chee Siong Teh, Chee Peng Lim
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This paper proposes a novel architecture for developing decision support systems. Unlike conventional decision support systems, the proposed architecture endeavors to reveal the decision-making process such that humans' subjectivity can be incorporated into a computerized system and, at the same time, to preserve the capability of the computerized system in processing information objectively. A number of techniques used in developing the decision support system are elaborated to make the decisionmarking process transparent. These include procedures for high dimensional data visualization, pattern classification, prediction, and evolutionary computational search. An artificial data set is first employed to compare the proposed approach with other methods. A simulated handwritten data set and a real data set on liver disease diagnosis are then employed to evaluate the efficacy of the proposed approach. The results are analyzed and discussed. The potentials of the proposed architecture as a useful decision support system are demonstrated.
Keywords: Interactive evolutionary computation, multivariate data projection, pattern classification, topographic map.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1443250 Effect of Humic Acid on Physical and Engineering Properties of Lime-Treated Organic Clay
Authors: N. Z. Mohd Yunus, D. Wanatowski, L. R. Stace
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The present work deals with the stabilisation of organic clay using hydrated lime. Artificial organic clays were prepared by adding kaolin and different humic acid contents. Results given by physical testing show that the presence of humic acid has a drawback effect on the untreated organic clay. The decrease in specific gravity value was accompanied by a decrease in dry density and plasticity of clay at higher humic acid contents. Significant increase in shear strength at 7 days of curing period is observed in the lime-treated samples up to 5% lime content. However shear strength of lime-treated organic clay decreases at longer curing periods. The results given by laboratory testing is further verified by microstructure analysis. Based on the results obtained in this study, it can be concluded that the presence of more than 1.5% humic acid reduces significantly the efficiency of lime stabilization in organic clays.Keywords: Humic acid, kaolin, lime, organic clay
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2729249 Modelling of Powered Roof Supports Work
Authors: Marcin Michalak
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Due to the increasing efforts on saving our natural environment a change in the structure of energy resources can be observed - an increasing fraction of a renewable energy sources. In many countries traditional underground coal mining loses its significance but there are still countries, like Poland or Germany, in which the coal based technologies have the greatest fraction in a total energy production. This necessitates to make an effort to limit the costs and negative effects of underground coal mining. The longwall complex is as essential part of the underground coal mining. The safety and the effectiveness of the work is strongly dependent of the diagnostic state of powered roof supports. The building of a useful and reliable diagnostic system requires a lot of data. As the acquisition of a data of any possible operating conditions it is important to have a possibility to generate a demanded artificial working characteristics. In this paper a new approach of modelling a leg pressure in the single unit of powered roof support. The model is a result of the analysis of a typical working cycles.Keywords: Machine modelling, underground mining, coal mining.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1914248 Performance Comparison of ADTree and Naive Bayes Algorithms for Spam Filtering
Authors: Thanh Nguyen, Andrei Doncescu, Pierre Siegel
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Classification is an important data mining technique and could be used as data filtering in artificial intelligence. The broad application of classification for all kind of data leads to be used in nearly every field of our modern life. Classification helps us to put together different items according to the feature items decided as interesting and useful. In this paper, we compare two classification methods Naïve Bayes and ADTree use to detect spam e-mail. This choice is motivated by the fact that Naive Bayes algorithm is based on probability calculus while ADTree algorithm is based on decision tree. The parameter settings of the above classifiers use the maximization of true positive rate and minimization of false positive rate. The experiment results present classification accuracy and cost analysis in view of optimal classifier choice for Spam Detection. It is point out the number of attributes to obtain a tradeoff between number of them and the classification accuracy.Keywords: Classification, data mining, spam filtering, naive Bayes, decision tree.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1485247 Development of NOx Emission Model for a Tangentially Fired Acid Incinerator
Authors: Elangeshwaran Pathmanathan, Rosdiazli Ibrahim, Vijanth Sagayan Asirvadam
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This paper aims to develop a NOx emission model of an acid gas incinerator using Nelder-Mead least squares support vector regression (LS-SVR). Malaysia DOE is actively imposing the Clean Air Regulation to mandate the installation of analytical instrumentation known as Continuous Emission Monitoring System (CEMS) to report emission level online to DOE . As a hardware based analyzer, CEMS is expensive, maintenance intensive and often unreliable. Therefore, software predictive technique is often preferred and considered as a feasible alternative to replace the CEMS for regulatory compliance. The LS-SVR model is built based on the emissions from an acid gas incinerator that operates in a LNG Complex. Simulated Annealing (SA) is first used to determine the initial hyperparameters which are then further optimized based on the performance of the model using Nelder-Mead simplex algorithm. The LS-SVR model is shown to outperform a benchmark model based on backpropagation neural networks (BPNN) in both training and testing data.Keywords: artificial neural networks, industrial pollution, predictive algorithms, support vector machines
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1965246 Application of Neuro-Fuzzy Dynamic Programming to Improve the Reactive Power and Voltage Profile of a Distribution Substation
Authors: M. Tarafdar Haque, S. Najafi
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Improving the reactive power and voltage profile of a distribution substation is investigated in this paper. The purpose is to properly determination of the shunt capacitors on/off status and suitable tap changer (TC) position of a substation transformer. In addition, the limitation of secondary bus voltage, the maximum allowable number of switching operation in a day for on load tap changer and on/off status of capacitors are taken into account. To achieve these goals, an artificial neural network (ANN) is designed to provide preliminary scheduling. Input of ANN is active and reactive powers of transformer and its primary and secondary bus voltages. The output of ANN is capacitors on/off status and TC position. The preliminary schedule is further refined by fuzzy dynamic programming in order to reach the final schedule. The operation of proposed method in Q/V improving is compared with the results obtained by operator operation in a distribution substation.Keywords: Neuro-fuzzy, Dynamic programming, Reactive power, Voltage profile.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1617245 Prediction of Unsteady Forced Convection over Square Cylinder in the Presence of Nanofluid by Using ANN
Authors: Ajoy Kumar Das, Prasenjit Dey
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Heat transfer due to forced convection of copper water based nanofluid has been predicted by Artificial Neural network (ANN). The present nanofluid is formed by mixing copper nanoparticles in water and the volume fractions are considered here are 0% to 15% and the Reynolds number are kept constant at 100. The back propagation algorithm is used to train the network. The present ANN is trained by the input and output data which has been obtained from the numerical simulation, performed in finite volume based Computational Fluid Dynamics (CFD) commercial software Ansys Fluent. The numerical simulation based results are compared with the back propagation based ANN results. It is found that the forced convection heat transfer of water based nanofluid can be predicted correctly by ANN. It is also observed that the back propagation ANN can predict the heat transfer characteristics of nanofluid very quickly compared to standard CFD method.Keywords: Forced convection, Square cylinder, nanofluid, neural network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2353244 Neural Network Ensemble-based Solar Power Generation Short-Term Forecasting
Authors: A. Chaouachi, R.M. Kamel, R. Ichikawa, H. Hayashi, K. Nagasaka
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This paper presents the applicability of artificial neural networks for 24 hour ahead solar power generation forecasting of a 20 kW photovoltaic system, the developed forecasting is suitable for a reliable Microgrid energy management. In total four neural networks were proposed, namely: multi-layred perceptron, radial basis function, recurrent and a neural network ensemble consisting in ensemble of bagged networks. Forecasting reliability of the proposed neural networks was carried out in terms forecasting error performance basing on statistical and graphical methods. The experimental results showed that all the proposed networks achieved an acceptable forecasting accuracy. In term of comparison the neural network ensemble gives the highest precision forecasting comparing to the conventional networks. In fact, each network of the ensemble over-fits to some extent and leads to a diversity which enhances the noise tolerance and the forecasting generalization performance comparing to the conventional networks.Keywords: Neural network ensemble, Solar power generation, 24 hour forecasting, Comparative study
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3264243 ORPP with MAIEP Based Technique for Loadability Enhancement
Authors: Norziana Aminudin, Titik Khawa Abdul Rahman, Ismail Musirin
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One of the factors to maintain system survivability is the adequate reactive power support to the system. Lack of reactive power support may cause undesirable voltage decay leading to total system instability. Thus, appropriate reactive power support scheme should be arranged in order to maintain system stability. The strength of a system capacity is normally denoted as system loadability. This paper presents the enhancement of system loadability through optimal reactive power planning technique using a newly developed optimization technique, termed as Multiagent Immune Evolutionary Programming (MAIEP). The concept of MAIEP is developed based on the combination of Multiagent System (MAS), Artificial Immune System (AIS) and Evolutionary Programming (EP). In realizing the effectiveness of the proposed technique, validation is conducted on the IEEE-26-Bus Reliability Test System. The results obtained from pre-optimization and post-optimization process were compared which eventually revealed the merit of MAIEP.Keywords: Load margin, MAIEP, Maximum loading point, ORPP.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1487242 Simulating Laboratory Short Term Aging to Suit Malaysian Field Conditions
Authors: Meor O. Hamzah, Seyed R. Omranian, Ali Jamshidi, Mohd R M. Hasan
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This paper characterizes the effects of artificial short term aging in the laboratory on the rheological properties of virgin 80/100 penetration grade asphalt binder. After several years in service, asphalt mixture started to deteriorate due to aging. Aging is a complex physico-chemical phenomenon that influences asphalt binder rheological properties causing a deterioration in asphalt mixture performance. To ascertain asphalt binder aging effects, the virgin, artificially aged and extracted asphalt binder were tested via the Rolling Thin film Oven (RTFO), Dynamic Shear Rheometer (DSR) and Rotational Viscometer (RV). A comparative study between laboratory and field aging conditions were also carried out. The results showed that the specimens conditioned for 85 minutes inside the RTFO was insufficient to simulate the actual short term aging caused that took place in the field under Malaysian field conditionsKeywords: Asphalt binder, Short term aging, Rheological properties, Viscosity, Temperature susceptibility.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2511241 Neuro-fuzzy Classification System for Wireless-Capsule Endoscopic Images
Authors: Vassilis S. Kodogiannis, John N. Lygouras
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In this research study, an intelligent detection system to support medical diagnosis and detection of abnormal lesions by processing endoscopic images is presented. The images used in this study have been obtained using the M2A Swallowable Imaging Capsule - a patented, video color-imaging disposable capsule. Schemes have been developed to extract texture features from the fuzzy texture spectra in the chromatic and achromatic domains for a selected region of interest from each color component histogram of endoscopic images. The implementation of an advanced fuzzy inference neural network which combines fuzzy systems and artificial neural networks and the concept of fusion of multiple classifiers dedicated to specific feature parameters have been also adopted in this paper. The achieved high detection accuracy of the proposed system has provided thus an indication that such intelligent schemes could be used as a supplementary diagnostic tool in endoscopy.Keywords: Medical imaging, Computer aided diagnosis, Endoscopy, Neuro-fuzzy networks, Fuzzy integral.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1740240 Predictive Models for Compressive Strength of High Performance Fly Ash Cement Concrete for Pavements
Authors: S. M. Gupta, Vanita Aggarwal, Som Nath Sachdeva
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The work reported through this paper is an experimental work conducted on High Performance Concrete (HPC) with super plasticizer with the aim to develop some models suitable for prediction of compressive strength of HPC mixes. In this study, the effect of varying proportions of fly ash (0% to 50% @ 10% increment) on compressive strength of high performance concrete has been evaluated. The mix designs studied were M30, M40 and M50 to compare the effect of fly ash addition on the properties of these concrete mixes. In all eighteen concrete mixes that have been designed, three were conventional concretes for three grades under discussion and fifteen were HPC with fly ash with varying percentages of fly ash. The concrete mix designing has been done in accordance with Indian standard recommended guidelines. All the concrete mixes have been studied in terms of compressive strength at 7 days, 28 days, 90 days, and 365 days. All the materials used have been kept same throughout the study to get a perfect comparison of values of results. The models for compressive strength prediction have been developed using Linear Regression method (LR), Artificial Neural Network (ANN) and Leave-One-Out Validation (LOOV) methods.
Keywords: ANN, concrete mixes, compressive strength, fly ash, high performance concrete, linear regression, strength prediction models.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2068239 Antimicrobial Agents Produced by Yeasts
Authors: T. Buyuksirit, H. Kuleasan
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Natural antimicrobials are used to preserve foods that can be found in plants, animals, and microorganisms. Antimicrobial substances are natural or artificial agents that produced by microorganisms or obtained semi/total chemical synthesis are used at low concentrations to inhibit the growth of other microorganisms. Food borne pathogens and spoilage microorganisms are inactivated by the use of antagonistic microorganisms and their metabolites. Yeasts can produce toxic proteins or glycoproteins (toxins) that cause inhibition of sensitive bacteria and yeast species. Antimicrobial substance producing phenotypes belonging different yeast genus were isolated from different sources. Toxins secreted by many yeast strains inhibiting the growth of other yeast strains. These strains show antimicrobial activity, inhibiting the growth of mold and bacteria. The effect of antimicrobial agents produced by yeasts can be extremely fast, and therefore may be used in various treatment procedures. Rapid inhibition of microorganisms is possibly caused by microbial cell membrane lipopolysaccharide binding and in activation (neutralization) effect. Antimicrobial agents inhibit the target cells via different mechanisms of action.
Keywords: Antimicrobial agents, Glycoprotein, Toxic protein, Yeast.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4537238 Electric Field Effect on the Rise of Single Bubbles during Boiling
Authors: N. Masoudnia, M. Fatahi
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An experimental study of saturated pool boiling on a single artificial nucleation site without and with the application of an electric field on the boiling surface has been conducted. N-pentane is boiling on a copper surface and is recorded with a high speed camera providing high quality pictures and movies. The accuracy of the visualization allowed establishing an experimental bubble growth law from a large number of experiments. This law shows that the evaporation rate is decreasing during the bubble growth, and underlines the importance of liquid motion induced by the preceding bubble. Bubble rise is therefore studied: once detached, bubbles accelerate vertically until reaching a maximum velocity in good agreement with a correlation from literature. The bubbles then turn to another direction. The effect of applying an electric field on the boiling surface in finally studied. In addition to changes of the bubble shape, changes are also shown in the liquid plume and the convective structures above the surface. Lower maximum rising velocities were measured in the presence of electric fields, especially with a negative polarity.
Keywords: Single bubbles, electric field, boiling, effect.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1191237 AI Tutor: A Computer Science Domain Knowledge Graph-Based QA System on JADE platform
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 process, knowledge graph, agent, QA system.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 879236 Artificial Generation of Visual Evoked Potential to Enhance Visual Ability
Authors: A. Vani, M. N. Mamatha
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Visual signal processing in human beings occurs in the occipital lobe of the brain. The signals that are generated in the brain are universal for all the human beings and they are called Visual Evoked Potential (VEP). Generally, the visually impaired people lose sight because of severe damage to only the eyes natural photo sensors, but the occipital lobe will still be functioning. In this paper, a technique of artificially generating VEP is proposed to enhance the visual ability of the subject. The system uses the electrical photoreceptors to capture image, process the image, to detect and recognize the subject or object. This voltage is further processed and can transmit wirelessly to a BIOMEMS implanted into occipital lobe of the patient’s brain. The proposed BIOMEMS consists of array of electrodes that generate the neuron potential which is similar to VEP of normal people. Thus, the neurons get the visual data from the BioMEMS which helps in generating partial vision or sight for the visually challenged patient.Keywords: Visual evoked potential, OpenViBe, BioMEMS, Neuro prosthesis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1452235 RASPE – Risk Advisory Smart System for Pipeline Projects in Egypt
Authors: Nael Y. Zabel, Maged E. Georgy, Moheeb E. Ibrahim
Abstract:
A knowledge-based expert system with the acronym RASPE is developed as an application tool to help decision makers in construction companies make informed decisions about managing risks in pipeline construction projects. Choosing to use expert systems from all available artificial intelligence techniques is due to the fact that an expert system is more suited to representing a domain’s knowledge and the reasoning behind domain-specific decisions. The knowledge-based expert system can capture the knowledge in the form of conditional rules which represent various project scenarios and potential risk mitigation/response actions. The built knowledge in RASPE is utilized through the underlying inference engine that allows the firing of rules relevant to a project scenario into consideration. Paper provides an overview of the knowledge acquisition process and goes about describing the knowledge structure which is divided up into four major modules. The paper shows one module in full detail for illustration purposes and concludes with insightful remarks.
Keywords: Expert System, Knowledge Management, Pipeline Projects, Risk Mismanagement.
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