Search results for: hybrid frequent subgraph mining

Authors: Abrar Hussain Qureshi

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Vocabulary has been highlighted as a key indicator in any foreign language learning program, especially English as a foreign language (EFL). It is often considered a potential tool in foreign language curriculum, and its deficiency impedes successful communication in the target language. The knowledge of the lexicon is very significant in getting communicative competence and performance. Nouns constitute a considerable bulk of English vocabulary. Rather, they are the bones of the English language and are the main semantic carrier in spoken and written discourse. As nouns dominate the bulk of the English lexicon, their role becomes all the more potential. The undertaken research is a systematic effort in this regard to work out a list of highly frequent list of Pakistani English nouns for the EFL learners at the secondary level. It will encourage autonomy for the EFL learners as well as will save their time. The corpus used for the research has been developed locally from leading English newspapers of Pakistan. Wordsmith Tools has been used to process the research data and to retrieve word list of frequent Pakistani English nouns. The retrieved list of core Pakistani English nouns is supposed to be useful for English language learners at the secondary level as it covers a wide range of speech events.

Keywords: corpus, EFL, frequency list, nouns

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Authors: Suelane Garcia Fontes, Silvio Luiz Stanzani, Pedro L. Pizzigatti Corrła Ronaldo G. Morato

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Environmental changes and major natural disasters are most prevalent in the world due to the damage that humanity has caused to nature and these damages directly affect the lives of animals. Thus, the study of animal behavior and their interactions with the environment can provide knowledge that guides researchers and public agencies in preservation and conservation actions. Exploratory analysis of animal movement can determine the patterns of animal behavior and with technological advances the ability of animals to be tracked and, consequently, behavioral studies have been expanded. There is a lot of research on animal movement and behavior, but we note that a proposal that combines resources and allows for exploratory analysis of animal movement and provide statistical measures on individual animal behavior and its interaction with the environment is missing. The contribution of this paper is to present the framework AniMoveMineR, a unified solution that aggregates trajectory analysis and data mining techniques to explore animal movement data and provide a first step in responding questions about the animal individual behavior and their interactions with other animals over time and space. We evaluated the framework through the use of monitored jaguar data in the city of Miranda Pantanal, Brazil, in order to verify if the use of AniMoveMineR allows to identify the interaction level between these jaguars. The results were positive and provided indications about the individual behavior of jaguars and about which jaguars have the highest or lowest correlation.

Keywords: data mining, data science, trajectory, animal behavior

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Authors: Eva Nolan, Timothy Mcnichols

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Gamers spend over 3 billion hours collectively playing video games a week, which is arguably not nearly enough time to indulge in the many benefits gaming has to offer. Much of the previous research on video gaming is centered on the effects of playing violent video games and the negative impacts they have on the individual. However, there is a dearth of research in the area of non-violent video games, specifically the emotional and cognitive benefits playing non-violent games can offer individuals. Current research in the area of video game play suggests there are many benefits to playing for an individual, such as decreasing symptoms of depression, decreasing stress, increasing positive emotions, inducing relaxation, decreasing anxiety, and particularly improving mood. One suggestion as to why video games may offer such benefits is that they possess ideal characteristics to create and maintain flow experiences, which in turn, is the subjective experience where an individual obtains a heightened and improved state of mind while they are engaged in a task where a balance of challenge and skill is found. Many video games offer a platform for collaborative gameplay, which can enhance the emotional experience of gaming through the feeling of social support and social inclusion. The present study was designed to examine the effects of collaborative gameplay and flow experience on participants’ perceived mood. To investigate this phenomenon, an in-between subjects design involving forty participants were randomly divided into two groups where they engaged in solo or collaborative gameplay. Each group represented an even number of frequent gamers and non-frequent gamers. Each participant played ‘The Lego Movie Videogame’ on the Playstation 4 console. The participant’s levels of flow experience and perceived mood were measured by the Flow State Scale (FSS) and the Positive and Negative Affect Schedule (PANAS). The following research hypotheses were investigated: (i.) participants in the collaborative gameplay condition will experience higher levels of flow experience and higher levels of mood than those in the solo gameplay condition; (ii.) participants who are frequent gamers will experience higher levels of flow experience and higher levels of mood than non-frequent gamers; and (iii.) there will be a significant positive relationship between flow experience and mood. If the estimated findings are supported, this suggests that engaging in collaborative gameplay can be beneficial for an individual’s mood and that experiencing a state of flow can also enhance an individual’s mood. Hence, collaborative gaming can be beneficial to promote positive emotions (higher levels of mood) through engaging an individual’s flow state.

Keywords: collaborative gameplay, flow experience, mood, games, positive emotions

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Authors: Hati̇ce Bi̇rtane, Asli Beyler Çi̇ği̇l, Memet Vezi̇r Kahraman

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Polyimide (PI) is one of the most important super-engineering materials because of its mechanical properties and its thermal stability. Electronic industry is the typical extensive applications of polyimides including interlayer insulation films, buffer coating, films, alpha-ray shielding films, and alignment films for liquid crystal displays. The mechanical and thermal properties of polymers are generally improved by the addition of inorganic additives. The challenges in this area of high-performance organic/inorganic hybrid materials are to obtain significant improvements in the interfacial adhesion between the polymer matrix and the reinforcing material since the organic matrix is relatively incompatible with the inorganic phase. In this study, modified nanodiamond was prepared from the reaction of nanodiamond and (3-Mercaptopropyl)trimethoxysilane. Poly(amic acid) was prepared from the reaction of 3,3',4,4'-Benzophenonetetracarboxylic dianhydride (BTDA) and 4,4'-Oxydianiline (ODA). Polyimide/modified nanodiamond hybrids were prepared by blending of poly(amic acid) and organically modified nanodiamond. The morphology of the Polyimide/ modified nanodiamond hybrids was characterized by scanning electron microscopy (SEM). Chemical structure of polyimide and Polyimide/modified nanodiamond hybrids was characterized by FTIR. FTIR results showed that the Polyimide/modified nanodiamond hybrids were successfully prepared. A thermal property of the Polyimide/modified nanodiamond hybrids was characterized by thermogravimetric analysis (TGA).

Keywords: hybrid materials, nanodiamond, polyimide, polymer

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Authors: Shilpa Balan

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Radio Frequency Identification, also popularly known as RFID is used to automatically identify and track tags attached to items. This study focuses on the application of RFID in healthcare. The adoption of RFID in healthcare is a crucial technology to patient safety and inventory management. Data from RFID tags are used to identify the locations of patients and inventory in real time. Medical errors are thought to be a prominent cause of loss of life and injury. The major advantage of RFID application in healthcare industry is the reduction of medical errors. The healthcare industry has generated huge amounts of data. By discovering patterns and trends within the data, big data analytics can help improve patient care and lower healthcare costs. The number of increasing research publications leading to innovations in RFID applications shows the importance of this technology. This study explores the current state of research of RFID in healthcare using a text mining approach. No study has been performed yet on examining the current state of RFID research in healthcare using a data mining approach. In this study, related articles were collected on RFID from healthcare journal and news articles. Articles collected were from the year 2000 to 2015. Significant keywords on the topic of focus are identified and analyzed using open source data analytics software such as Rapid Miner. These analytical tools help extract pertinent information from massive volumes of data. It is seen that the main benefits of adopting RFID technology in healthcare include tracking medicines and equipment, upholding patient safety, and security improvement. The real-time tracking features of RFID allows for enhanced supply chain management. By productively using big data, healthcare organizations can gain significant benefits. Big data analytics in healthcare enables improved decisions by extracting insights from large volumes of data.

Keywords: RFID, data mining, data analysis, healthcare

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Authors: F. Debbat, F. T. Bendimerad

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The efficient frequency assignment for radio communications becomes more and more crucial when developing new information technologies and their applications. It is consists in defining an assignment of frequencies to radio links, to be established between base stations and mobile transmitters. Separation of the frequencies assigned is necessary to avoid interference. However, unnecessary separation causes an excess requirement for spectrum, the cost of which may be very high. This problem is NP-hard problem which cannot be solved by conventional optimization algorithms. It is therefore necessary to use metaheuristic methods to solve it. This paper proposes Hybrid approach based on simulated annealing (SA) and Tabu Search (TS) methods to solve this problem. Computational results, obtained on a number of standard problem instances, testify the effectiveness of the proposed approach.

Keywords: cellular mobile communication, frequency assignment problem, optimization, tabu search, simulated annealing

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Authors: Hamza Arif, Haroon Javaid

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Based on hybrid energy concept of smart grid to synchronize and monitor power being generated at the user end. The ATMEGA328p controller of arduino is used as a processor unit that sends wireless data between user and power utility through NRF24L01 wireless modules. Current and potential transformer circuit are designed to sense the voltage and current at the utility and power being generated at the user end through solar panel. They are designed to interface with the arduino. The approach is used to demonstrate the concept of smart grid and to facilitate for further advancements in the field of smart grid technology. A PWM (Pulse Width Modulation) technique is used to synchronize the user output power with the utility supplier.

Keywords: smart grid, hybrid energy, grid tied inverter, PWM

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Authors: Giovana Perini-Loureiro

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The purpose of this research was to identify whether the nominalizations terminating in -TION in the academic discourse of native English speakers contain the arguments required by their input verbs. In the perspective of functional linguistics, ideational metaphors, with nominalization as their most pervasive realization, are lexically dense, and therefore frequent in formal texts. Ideational metaphors allow the academic genre to instantiate objectification, de-personalization, and the ability to construct a chain of arguments. The valence of those nouns present in nominalizations tends to maintain the same elements of the valence from its original verbs, but these arguments are not always expressed. The initial hypothesis was that these arguments would also be present alongside the nominalizations, through anaphora or cataphora. In this study, a qualitative analysis of the occurrences of the five more frequent nominalized terminations in -TION in academic texts was accomplished, and thus a verification of the occurrences of the arguments required by the original verbs. The assembling of the concordance lines was done through COCA (Corpus of Contemporary American English). After identifying the five most frequent nominalizations (attention, action, participation, instruction, intervention), the concordance lines were selected at random to be analyzed, assuring the representativeness and reliability of the sample. It was possible to verify, in all the analyzed instances, the presence of arguments. In most instances, the arguments were not expressed, but recoverable, either in the context or in the shared knowledge among the interactants. It was concluded that the realizations of the arguments which were not expressed alongside the nominalizations are part of a continuum, starting from the immediate context with anaphora and cataphora; up to a knowledge shared outside the text, such as specific area knowledge. The study also has implications for the teaching of academic writing, especially with regards to the impact of nominalizations on the thematic and informational flow of the text. Grammatical metaphors are essential to academic writing, hence acknowledging the occurrence of its arguments is paramount to achieve linguistic awareness and the writing prestige required by the academy.

Keywords: corpus, functional linguistics, grammatical metaphors, nominalizations, academic English

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Authors: A. Kouloumpis, P. Zygouri, G. Potsi, K. Spyrou, D. Gournis

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Much of the research effort on graphene focuses on its use as building block for the development of new hybrid nanostructures with well-defined dimensions and behavior suitable for applications among else in gas storage, heterogeneous catalysis, gas/liquid separations, nanosensing and biology. Towards this aim, here we describe a new bottom-up approach, which combines the self-assembly with the Langmuir Schaefer technique, for the production of fullerene-intercalated graphene hybrid materials. This new method uses graphene nanosheets as a template for the grafting of various fullerene C60 molecules (pure C60, bromo-fullerenes, C60Br24, and fullerols, C60(OH)24) in a bi-dimensional array, and allows for perfect layer-by-layer growth with control at the molecular level. Our film preparation approach involves a bottom-up layer-by-layer process that includes the formation of a hybrid organo-graphene Langmuir film hosting fullerene molecules within its interlayer spacing. A dilute water solution of chemically oxidized graphene (GO) was used as subphase on the Langmuir-Blodgett deposition system while an appropriate amino surfactant (that binds covalently with the GO) was applied for the formation of hybridized organo-GO. After the horizontal lift of a hydrophobic substrate, a surface modification of the GO platelets was performed by bringing the surface of the transferred Langmuir film in contact with a second amino surfactant solution (capable to interact strongly with the fullerene derivatives). In the final step, the hybrid organo-graphene film was lowered in the solution of the appropriate fullerene derivative. Multilayer films were constructed by repeating this procedure. Hybrid fullerene-based thin films deposited on various hydrophobic substrates were characterized by X-ray diffraction (XRD) and X-ray reflectivity (XRR), FTIR, and Raman spectroscopies, Atomic Force Microscopy, and optical measurements. Acknowledgments. This research has been co‐financed by the European Union (European Social Fund – ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF)‐Research Funding Program: THALES. Investing in knowledge society through the European Social Fund (no. 377285).

Keywords: hybrids, graphene oxide, fullerenes, langmuir-blodgett, intercalated structures

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Authors: L. Chevez, A. Apaza, J. Rodriguez, R. Puga, H. Loro, Juan Z. Davalos

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Virtual Dimension (VD) procedure is used to analyze Hyperspectral Image (HIS) treatment-data in order to estimate the abundance of mineral components of a mining sample. Hyperspectral images coming from reflectance spectra (NIR region) are pre-treated using Standard Normal Variance (SNV) and Minimum Noise Fraction (MNF) methodologies. The endmember components are identified by the Simplex Growing Algorithm (SVG) and after adjusted to the reflectance spectra of reference-databases using Simulated Annealing (SA) methodology. The obtained abundance of minerals of the sample studied is very near to the ones obtained using XRD with a total relative error of 2%.

Keywords: hyperspectral imaging, minimum noise fraction, MNF, simplex growing algorithm, SGA, standard normal variance, SNV, virtual dimension, XRD

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Authors: Joshua N. Edokpayi, John O. Odiyo, Patience P. Shikwambana

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Water is a very rare natural resource in South Africa. Ga-Selati River is used for both domestic and industrial purposes. This study was carried out in order to assess the quality of Ga-Selati River in a mining area of Limpopo Province-Phalaborwa. The pH, Electrical Conductivity (EC) and Total Dissolved Solids (TDS) were determined using a Crinson multimeter while turbidity was measured using a Labcon Turbidimeter. The concentrations of Al, Ca, Cd, Cr, Fe, K, Mg, Mn, Na and Pb were analysed in triplicate using a Varian 520 flame atomic absorption spectrometer (AAS) supplied by PerkinElmer, after acid digestion with nitric acid in a fume cupboard. The average pH of the river from eight different sampling sites was 8.00 and 9.38 in wet and dry season respectively. Higher EC values were determined in the dry season (138.7 mS/m) than in the wet season (96.93 mS/m). Similarly, TDS values were higher in dry (929.29 mg/L) than in the wet season (640.72 mg/L) season. These values exceeded the recommended guideline of South Africa Department of Water Affairs and Forestry (DWAF) for domestic water use (70 mS/m) and that of the World Health Organization (WHO) (600 mS/m), respectively. Turbidity varied between 1.78-5.20 and 0.95-2.37 NTU in both wet and dry seasons. Total hardness of 312.50 mg/L and 297.75 mg/L as the concentration of CaCO3 was computed for the river in both the wet and the dry seasons and the river water was categorised as very hard. Mean concentration of the metals studied in both the wet and the dry seasons are: Na (94.06 mg/L and 196.3 mg/L), K (11.79 mg/L and 13.62 mg/L), Ca (45.60 mg/L and 41.30 mg/L), Mg (48.41 mg/L and 44.71 mg/L), Al (0.31 mg/L and 0.38 mg/L), Cd (0.01 mg/L and 0.01 mg/L), Cr (0.02 mg/L and 0.09 mg/L), Pb (0.05 mg/L and 0.06 mg/L), Mn (0.31 mg/L and 0.11 mg/L) and Fe (0.76 mg/L and 0.69 mg/L). Results from this study reveal that most of the metals were present in concentrations higher than the recommended guidelines of DWAF and WHO for domestic use and the protection of aquatic life.

Keywords: contamination, mining activities, surface water, trace metals

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Authors: Thuvanan Borvornvitchotikarn, Werasak Kurutach

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Mutual information (MI) is widely used in medical image registration. In the different medical images analysis, it is difficult to choose an optimal bins size number for calculating the probability distributions in MI. As the result, this paper presents a new adaptive bins number selection approach that named a hybrid EMPCA-Scott approach. This work combines an expectation maximization principal component analysis (EMPCA) and the modified Scott’s rule. The proposed approach solves the binning problem from the various intensity values in medical images. Experimental results of this work show the lower registration errors compared to other adaptive binning approaches.

Keywords: mutual information, EMPCA, Scott, probability distributions

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Authors: Grienggrai Rajchakit

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This paper is concerned with the exponential stability of switched linear systems with interval time-varying delays. The time delay is any continuous function belonging to a given interval, in which the lower bound of delay is not restricted to zero. By constructing a suitable augmented Lyapunov-Krasovskii functional combined with Leibniz-Newton's formula, a switching rule for the exponential stability of switched linear systems with interval time-varying delays and new delay-dependent sufficient conditions for the exponential stability of the systems are first established in terms of LMIs. Finally, some examples are exploited to illustrate the effectiveness of the proposed schemes.

Keywords: exponential stability, hybrid systems, time-varying delays, lyapunov-krasovskii functional, leibniz-newton's formula

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Authors: Paul R Armstrong

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Doubled haploids (DHs) have become an important breeding tool for creating maize inbred lines, although several bottlenecks in the DH production process limit wider development, application, and adoption of the technique. DH kernels are typically sorted manually and represent about 10% of the seeds in a much larger pool where the remaining 90% are hybrid siblings. This introduces time constraints on DH production and manual sorting is often not accurate. Automated sorting based on the chemical composition of the kernel can be effective, but devices, namely NMR, have not achieved the sorting speed to be a cost-effective replacement to manual sorting. This study evaluated a single kernel near-infrared reflectance spectroscopy (skNIR) platform to accurately identify DH kernels based on oil content. The skNIR platform is a higher-throughput device, approximately 3 seeds/s, that uses spectra to predict oil content of each kernel from maize crosses intentionally developed to create larger than normal oil differences, 1.5%-2%, between DH and hybrid kernels. Spectra from the skNIR were used to construct a partial least squares regression (PLS) model for oil and for a categorical reference model of 1 (DH kernel) or 2 (hybrid kernel) and then used to sort several crosses to evaluate performance. Two approaches were used for sorting. The first used a general PLS model developed from all crosses to predict oil content and then used for sorting each induction cross, the second was the development of a specific model from a single induction cross where approximately fifty DH and one hundred hybrid kernels used. This second approach used a categorical reference value of 1 and 2, instead of oil content, for the PLS model and kernels selected for the calibration set were manually referenced based on traditional commercial methods using coloration of the tip cap and germ areas. The generalized PLS oil model statistics were R2 = 0.94 and RMSE = .93% for kernels spanning an oil content of 2.7% to 19.3%. Sorting by this model resulted in extracting 55% to 85% of haploid kernels from the four induction crosses. Using the second method of generating a model for each cross yielded model statistics ranging from R2s = 0.96 to 0.98 and RMSEs from 0.08 to 0.10. Sorting in this case resulted in 100% correct classification but required models that were cross. In summary, the first generalized model oil method could be used to sort a significant number of kernels from a kernel pool but was not close to the accuracy of developing a sorting model from a single cross. The penalty for the second method is that a PLS model would need to be developed for each individual cross. In conclusion both methods could find useful application in the sorting of DH from hybrid kernels.

Keywords: NIR, haploids, maize, sorting

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Authors: Ahmad Awajan, Sadam Al Wadi

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In this study, a hybrid method based on Empirical Mode Decomposition and Holt-Winter (EMD-HW) is used to forecast Amman stock market data. First, the data are decomposed by EMD method into Intrinsic Mode Functions (IMFs) and residual components. Then, all components are forecasted by HW technique. Finally, forecasting values are aggregated together to get the forecasting value of stock market data. Empirical results showed that the EMD- HW outperform individual forecasting models. The strength of this EMD-HW lies in its ability to forecast non-stationary and non- linear time series without a need to use any transformation method. Moreover, EMD-HW has a relatively high accuracy comparing with eight existing forecasting methods based on the five forecast error measures.

Keywords: Holt-Winter method, empirical mode decomposition, forecasting, time series

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Authors: Aprajeeta Jha, Punyadarshini P. Tripathy

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Drying technologies for various food processing operations shares an inevitable linkage with energy, cost and environmental sustainability. Hence, solar drying of food grains has become imperative choice to combat duo challenges of meeting high energy demand for drying and to address climate change scenario. But performance and reliability of solar dryers depend hugely on sunshine period, climatic conditions, therefore, offer a limited control over drying conditions and have lower efficiencies. Solar drying technology, supported by Photovoltaic (PV) power plant and hybrid type solar air collector can potentially overpower the disadvantages of solar dryers. For development of such robust hybrid dryers; to ensure quality and shelf-life of paddy grains the optimization of process parameter becomes extremely critical. Investigation of the moisture distribution profile within the grains becomes necessary in order to avoid over drying or under drying of food grains in hybrid solar dryer. Computational simulations based on finite element modeling can serve as potential tool in providing a better insight of moisture migration during drying process. Hence, present work aims at optimizing the process parameters and to develop a 3-dimensional (3D) finite element model (FEM) for predicting moisture profile in paddy during solar drying. COMSOL Multiphysics was employed to develop a 3D finite element model for predicting moisture profile. Furthermore, optimization of process parameters (power level, air velocity and moisture content) was done using response surface methodology in design expert software. 3D finite element model (FEM) for predicting moisture migration in single kernel for every time step has been developed and validated with experimental data. The mean absolute error (MAE), mean relative error (MRE) and standard error (SE) were found to be 0.003, 0.0531 and 0.0007, respectively, indicating close agreement of model with experimental results. Furthermore, optimized process parameters for drying paddy were found to be 700 W, 2.75 m/s at 13% (wb) with optimum temperature, milling yield and drying time of 42˚C, 62%, 86 min respectively, having desirability of 0.905. Above optimized conditions can be successfully used to dry paddy in PV integrated solar dryer in order to attain maximum uniformity, quality and yield of product. PV-integrated hybrid solar dryers can be employed as potential and cutting edge drying technology alternative for sustainable energy and food security.

Keywords: finite element modeling, moisture migration, paddy grain, process optimization, PV integrated hybrid solar dryer

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Authors: Chayaporn Kaensar

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Recently, Job Recommender Systems have gained much attention in industries since they solve the problem of information overload on the recruiting website. Therefore, we proposed Extended Personalized Job System that has the capability of providing the appropriate jobs for job seeker and recommending some suitable information for them using Data Mining Techniques and Dynamic User Profile. On the other hands, company can also interact to the system for publishing and updating job information. This system have emerged and supported various platforms such as web application and android mobile application. In this paper, User profiles, Implicit User Action, User Feedback, and Clustering Techniques in WEKA libraries have gained attention and implemented for this application. In additions, open source tools like Yii Web Application Framework, Bootstrap Front End Framework and Android Mobile Technology were also applied.

Keywords: recommendation, user profile, data mining, web and mobile technology

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Authors: Daya Rani

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Non-metal-based compounds have emerged as promising electrodes in recent years to replace scarce and expensive transition-metals for energy storage applications. Herein, a simple electro-spinning technique followed by carbonization is used to create tetraphosphorus triselenide(P4Se3)nano-flakes encapsulated in carbon nanofiber (P4Se3@CNF) to obtain a binder-free, metal-free and flexible hybrid electrode with high electrical conductivity and cyclic stability. A remarkable capacitive performance (5.5-folds@P4Se3) of 810Fg-1/[email protected] has been obtained using P4Se3@CNF electrode with an excellent rate capability compared to pristine(P4Se3) which is further supported by theoretical calculations via intercalating graphene within bare P4Se3 flakes inducing partial charge redistribution in hetero-structure. A flexible pouch-type hybrid-supercapacitor followed by coin-cell has been manufactured offering exceptional energy-density without sacrificing power density and ultra-long durability over 35000 and 100000-cycles with capacitance-retention of 99.77% and 100%, respectively. It has been demonstrated that as-fabricated device has practical usefulness towards renewable energy harvesting and storage via integrating commercial solar cell module with supercapattery array that can enlighten the blue LED approximately for 31minutes, rotate the homemade windmill device, power Arduino and glow “INST” against 2minutes of charging. This work demonstrates a facile route towards the development of metal-free electrochemical renewable energy storage/transfer devices offering an inevitable adoption in industrial platforms.

Keywords: metal free, carbon nano-fiber, pouch-type hybrid super-capacitor, nano-flakes

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Authors: Y. A. Yahaya, Ahmad Tijjani Asabe

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This paper focuses on the formulation of 3-step hybrid Adams type method for the solution of first order differential equation (ODE). The methods which was derived on both grid and off grid points using multistep collocation schemes and also evaluated at some points to produced Block Adams type method and Adams moulton method respectively. The method with the highest order was selected to serve as the corrector. The convergence was valid and efficient. The numerical experiments were carried out and reveal that hybrid Adams type methods performed better than the conventional Adams moulton method.

Keywords: adam-moulton type (amt), corrector method, off-grid, block method, convergence analysis

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Authors: Ahmed Moulay, Mariia Zhuldybina, Mirko Torres, Mike Rozel, Ngoc Duc Trinh, Chloé Bois

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Hybrid printed electronics technology (HPE) provides innovative opportunities to enhance conventional electronics applications, which are often based on printed circuit boards (PCB). By combining the best of both performance from conventional electronic components and the flexibility from printed circuits makes it possible to manufacture HPE at high volumes using roll-to-roll printing processes. However, several challenges must be overcome in order to accurately integrate an electronic component on a printed circuit. In this presentation, we will demonstrate the integration process of electronic components from the lab scale to the industrialization. Both the printing quality and the integration technique must be studied to define the optimal conditions. To cover the parameters that influence the print quality of the printed circuit, different printing processes, flexible substrates, and conductive inks will be used to determine the optimized printing process/ink/substrate system. After the systems is selected, an electronic component of 2.5 mm2 chip size will be integrated to validate the functionality of the printed, electronic circuit. Critical information such as the conductive adhesive, the curing conditions, and the chip encapsulation will be determined. Thanks to these preliminary results, we are able to demonstrate the chip integration on a printed circuit using industrial equipment, showing the potential of industrialization, compatible using roll-to-roll printing and integrating processes.

Keywords: flat bed screen-printing, hybrid printed electronics, integration, large-scale production, roll-to-roll printing, rotary screen printing

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Authors: A. E. Kobryn

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We present both descriptive and predictive modeling of structural properties of blends of PCBM or organic-inorganic hybrid perovskites of the type CH3NH3PbX3 (X=Cl, Br, I) with P3HT, P3BT or squaraine SQ2 dye sensitizer, including adsorption on TiO2 clusters having rutile (110) surface. In our study, we use a methodology that allows computing the microscopic structure of blends on the nanometer scale and getting insight on miscibility of its components at various thermodynamic conditions. The methodology is based on the integral equation theory of molecular liquids in the reference interaction site representation/model (RISM) and uses the universal force field. Input parameters for RISM, such as optimized molecular geometries and charge distribution of interaction sites, are derived with the use of the density functional theory methods. To compare the diffusivity of the PCBM in binary blends with P3HT and P3BT, respectively, the study is complemented with MD simulation. A very good agreement with experiment and the reports of alternative modeling or simulation is observed for PCBM in P3HT system. The performance of P3BT with perovskites, however, seems as expected. The calculated nanoscale morphologies of blends of P3HT, P3BT or SQ2 with perovskites, including adsorption on TiO2, are all new and serve as an instrument in rational design of organic/hybrid photovoltaics. They are used in collaboration with experts who actually make prototypes or devices for practical applications.

Keywords: multiscale theory and modeling, nanoscale morphology, organic-inorganic halide perovskites, three dimensional distribution

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Authors: M. Caramihai, Irina Severin

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This paper deals with the design of an intelligent control structure for a bioprocess of Hansenula polymorpha yeast cultivation. The objective of the process control is to produce biomass in a desired physiological state. The work demonstrates that the designed Hybrid Control Techniques (HCT) are able to recognize specific evolution bioprocess trajectories using neural networks trained specifically for this purpose, in order to estimate the model parameters and to adjust the overall bioprocess evolution through an expert system and a fuzzy structure. The design of the control algorithm as well as its tuning through realistic simulations is presented. Taking into consideration the synergism of different paradigms like fuzzy logic, neural network, and symbolic artificial intelligence (AI), in this paper we present a real and fulfilled intelligent control architecture with application in bioprocess control.

Keywords: bioprocess, intelligent control, neural nets, fuzzy structure, hybrid techniques

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Authors: Deng Zengming, Wang Mingjiang

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As 3D video is explored as a hot research topic in the last few decades, free-viewpoint TV (FTV) is no doubt a promising field for its better visual experience and incomparable interactivity. View synthesis is obviously a crucial technology for FTV; it enables to render images in unlimited numbers of virtual viewpoints with the information from limited numbers of reference view. In this paper, a novel hybrid synthesis framework is proposed and blending priority is explored. In contrast to the commonly used View Synthesis Reference Software (VSRS), the presented synthesis process is driven in consideration of the temporal correlation of image sequences. The temporal correlations will be exploited to produce fine synthesis results even near the foreground boundaries. As for the blending priority, this scheme proposed that one of the two reference views is selected to be the main reference view based on the distance between the reference views and virtual view, another view is chosen as the auxiliary viewpoint, just assist to fill the hole pixel with the help of background information. Significant improvement of the proposed approach over the state-of –the-art pixel-based virtual view synthesis method is presented, the results of the experiments show that subjective gains can be observed, and objective PSNR average gains range from 0.5 to 1.3 dB, while SSIM average gains range from 0.01 to 0.05.

Keywords: fusion method, Gaussian mixture model, hybrid framework, view synthesis

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Authors: Kyung Bae Park, Sung Ho Ha

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Online user-generated contents (UGC) significantly change the way customers behave (e.g., shop, travel), and a pressing need to handle the overwhelmingly plethora amount of various UGC is one of the paramount issues for management. However, a current approach (e.g., sentiment analysis) is often ineffective for leveraging textual information to detect the problems or issues that a certain management suffers from. In this paper, we employ text mining of Latent Dirichlet Allocation (LDA) on a popular online review site dedicated to complaint from users. We find that the employed LDA efficiently detects customer complaints, and a further inspection with the visualization technique is effective to categorize the problems or issues. As such, management can identify the issues at stake and prioritize them accordingly in a timely manner given the limited amount of resources. The findings provide managerial insights into how analytics on social media can help maintain and improve their reputation management. Our interdisciplinary approach also highlights several insights by applying machine learning techniques in marketing research domain. On a broader technical note, this paper illustrates the details of how to implement LDA in R program from a beginning (data collection in R) to an end (LDA analysis in R) since the instruction is still largely undocumented. In this regard, it will help lower the boundary for interdisciplinary researcher to conduct related research.

Keywords: latent dirichlet allocation, R program, text mining, topic model, user generated contents, visualization

Procedia PDF Downloads 186

Authors: Suchitra Sivakumar, Hajime Shingyouchi, Toshinori Okajima, Kyohei Yamaguchi, Jin Kusaka

Abstract:

The progressing need for powertrain electrification calls for more accurate and reliable simulation models. A battery pack serves as the most vital component for energy storage in an electrified powertrain. Hybrid electric vehicles (HEV) do not behave the same way as they age, and there are several environmental factors that account for the degradation of the battery on a system level. Therefore, in this work, a battery model was proposed to study the state of charge (SOC) variation and the internal dynamic changes that contribute to aging and performance degradation in HEV batteries. An equivalent circuit battery model (ECM) is built using MATLAB Simulink to investigate the output characteristics of the lithium-ion battery. The ECM comprises of circuit elements like a voltage source, a series resistor and a parallel RC network connected in series. A parameter estimation study is conducted on the ECM to study the dependencies of the circuit elements with the state of charge (SOC) and the terminal voltage of the battery. The battery model is extended to simulate the temperature dependence of the individual battery cell and the battery pack with the environment. The temperature dependence model accounts for the heat loss due to internal resistance build up in the battery pack during charging, discharging, and due to atmospheric temperature. The model was validated for a lithium-ion battery pack with an independent drive cycle showing a voltage accuracy of 4% and SOC accuracy of about 2%.

Keywords: battery model, hybrid electric vehicle, lithium-ion battery, thermal model

Procedia PDF Downloads 296

Authors: Ahmed Khalil

Abstract:

Increased energy demands around the world have led to the raise in power production which has resulted with more greenhouse gas emissions through fossil sources. These fossil sources and emissions cause deterioration in echo-system. Therefore, renewable energy sources come to the scene as echo-friendly and clean energy sourcing, whereas the electrical devices and energy needs decrease in the timeline. Each of these renewable energy sources contribute to the reduction of greenhouse gases and mitigate environmental deterioration. However, there are also some general and source-specific challenges, which influence the choice of the investors. The most prominent general challenge that effects end-users’ comfort and reliability is usually determined as the intermittence which derives from the diversions of source conditions, due to nature dynamics and uncontrolled periodic changes. Research and development professionals strive to mitigate intermittence challenge through material improvement for each renewable source whereas hybrid source mix stand as a solution. This solution prevails well, when single renewable technologies are upgraded further. On the other hand, integration of energy efficient devices and systems, raise the affirmative effect of such solution in means of less energy requirement in sustainability composition or scenario. This paper provides a glimpse on the advantages of composing renewable source mix versus single usage, with contribution of sampled e-efficient systems and devices. Accordingly it demonstrates the extended benefits, through planning and predictive estimation stages of Ahmadi Town Projects in Kuwait.

Keywords: e-efficient systems, hybrid source, intermittence challenge, renewable energy

Procedia PDF Downloads 134

Authors: Habtemariam Alemu

Abstract:

It has been known that the performance of position and force control is highly affected by both robot dynamic and environment stiffness uncertainties. In this paper, joint space hybrid force and position control strategy with self-selecting matrix using artificial neural network compensator is proposed. The objective of the work is to improve controller robustness by applying a neural network technique in order to compensate the effect of uncertainties in the robot model. Simulation results for a 6 degree of freedom (6-DoF) manipulator and different types of environments showed the effectiveness of the suggested approach. 6-DoF Puma 560 family robot manipulator is chosen as industrial robot and its efficient dynamic model is designed using Matlab/SimMechanics library.

Keywords: robot manipulator, force/position control, artificial neural network, Matlab/Simulink

Procedia PDF Downloads 512

Authors: Kisik Song, Kyuwoong Kim, Sungjoo Lee

Abstract:

Many global firms and corporations derive new technology and opportunity by identifying vacant technology from patent analysis. However, previous studies failed to focus on technologies that promised continuous growth in industrial fields. Most studies that derive new technology opportunities do not test practical effectiveness. Since previous studies depended on expert judgment, it became costly and time-consuming to evaluate new technologies based on patent analysis. Therefore, research suggests a quantitative and systematic approach to technology evaluation indicators by using patent data to and from customer communities. The first step involves collecting two types of data. The data is used to construct evaluation indicators and apply these indicators to the evaluation of new technologies. This type of data mining allows a new method of technology evaluation and better predictor of how new technologies are adopted.

Keywords: data mining, evaluating new technology, technology opportunity, patent analysis

Procedia PDF Downloads 374

Authors: Michael Ricardo Lang, AdriéLle Costa, Ivana Iesbik, Karine Haag, Leonardo Trindade Buffara, Oscar Reimann Junior, Chelin Auswaldt Steclan

Abstract:

Stroke affects not only the individual, but has significant impacts on the social and family context. Therefore, it is necessary to know the peculiarities of each region, in order to contribute to regional public health policies effectively. Thus, the present study discusses biomarkers in a post-stroke population, admitted to a stroke unit (U-stroke) of reference in the southern region of Brazil. Biomarkers were analyzed, such as age, length of stay, mortality rate, survival time, risk factors and family history of stroke in patients after ischemic stroke. In this studied population, comparing men and women, it was identified that men were more affected than women, and the average age of women affected was higher, as they also had the highest mortality rate and the shortest hospital stay. The risk factors identified here were according to the global scenario; with SAH being the most frequent and those associated with sedentary lifestyle in women the most frequent (dyspilipidemia, heart disease and obesity). In view of this, the importance of studies that characterize populations regionally is evident, strengthening the strategic planning of policies in favor of health care.

Keywords: biomarkers, sex, stroke, stroke unit, population

Procedia PDF Downloads 264

Authors: P. Bharathiraja

Abstract:

Computer Aided decision making system is used to enhance diagnosis and prognosis of diseases and also to assist clinicians and junior doctors in clinical decision making. Medical Data used for decision making should be definite and consistent. Data Mining and soft computing techniques are used for cleaning the data and for incorporating human reasoning in decision making systems. Fuzzy rule based inference technique can be used for classification in order to incorporate human reasoning in the decision making process. In this work, missing values are imputed using the mean or mode of the attribute. The data are normalized using min-ma normalization to improve the design and efficiency of the fuzzy inference system. The fuzzy inference system is used to handle the uncertainties that exist in the medical data. Equal-width-partitioning is used to partition the attribute values into appropriate fuzzy intervals. Fuzzy rules are generated using Class Based Associative rule mining algorithm. The system is trained and tested using heart disease data set from the University of California at Irvine (UCI) Machine Learning Repository. The data was split using a hold out approach into training and testing data. From the experimental results it can be inferred that classification using fuzzy inference system performs better than trivial IF-THEN rule based classification approaches. Furthermore it is observed that the use of fuzzy logic and fuzzy inference mechanism handles uncertainty and also resembles human decision making. The system can be used in the absence of a clinical expert to assist junior doctors and clinicians in clinical decision making.

Keywords: decision making, data mining, normalization, fuzzy rule, classification

Procedia PDF Downloads 517