Search results for: Numerical Model

Authors: Nieves M. C. Talavera-Prieto, Abel G. M. Ferreira, António T. G. Portugal, Rui J. Moreira, Jaime B. Santos

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The knowledge of biodiesel density over large ranges of temperature and pressure is important for predicting the behavior of fuel injection and combustion systems in diesel engines, and for the optimization of such systems. In this study, cottonseed oil was transesterified into biodiesel and its density was measured at temperatures between 288 K and 358 K and pressures between 0.1 MPa and 30 MPa, with expanded uncertainty estimated as ±1.6 kg⋅m- 3. Experimental pressure-volume-temperature (pVT) cottonseed data was used along with literature data relative to other 18 biodiesels, in order to build a database used to test the correlation of density with temperarure and pressure using the Goharshadi–Morsali–Abbaspour equation of state (GMA EoS). To our knowledge, this is the first that density measurements are presented for cottonseed biodiesel under such high pressures, and the GMA EoS used to model biodiesel density. The new tested EoS allowed correlations within 0.2 kg·m-3 corresponding to average relative deviations within 0.02%. The built database was used to develop and test a new full predictive model derived from the observed linear relation between density and degree of unsaturation (DU), which depended from biodiesel FAMEs profile. The average density deviation of this method was only about 3 kg.m-3 within the temperature and pressure limits of application. These results represent appreciable improvements in the context of density prediction at high pressure when compared with other equations of state.

Keywords: Biodiesel, Correlation, Density, Equation of state, Prediction.

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Authors: Ahmed Shinkafi, Craig Lawson

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There is a great advancement towards the All-Electric Aircraft (AEA) technology. The AEA concept assumes that all aircraft systems will be integrated into one electrical power source in the future. The principle of the electro-thermal system is to transfer the energy required for anti/de-icing to the protected areas in electrical form. However, powering a large aircraft anti-icing system electrically could be quite excessive in cost and system weight. Hence, maximising the anti/de-icing efficiency of the electro-thermal system in order to minimise its power demand has become crucial to electro-thermal de-icing system sizing. In this work, an enhanced methodology has been developed for conceptual sizing of aircraft electro-thermal de-icing System. The work factored those critical terms overlooked in previous studies which were critical to de-icing energy consumption. A case study of a typical large aircraft wing de-icing was used to test and validate the model. The model was used to optimise the system performance by a trade-off between the de-icing peak power and system energy consumption. The optimum melting surface temperatures and energy flux predicted enabled the reduction in the power required for de-icing. The weight penalty associated with electro-thermal anti-icing/de-icing method could be eliminated using this method without under estimating the de-icing power requirement.

Keywords: Aircraft de-icing system, electro-thermal, in-flight icing.

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Authors: Ruchika Malhotra, Megha Khanna

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The development of change prediction models can help the software practitioners in planning testing and inspection resources at early phases of software development. However, a major challenge faced during the training process of any classification model is the imbalanced nature of the software quality data. A data with very few minority outcome categories leads to inefficient learning process and a classification model developed from the imbalanced data generally does not predict these minority categories correctly. Thus, for a given dataset, a minority of classes may be change prone whereas a majority of classes may be non-change prone. This study explores various alternatives for adeptly handling the imbalanced software quality data using different sampling methods and effective MetaCost learners. The study also analyzes and justifies the use of different performance metrics while dealing with the imbalanced data. In order to empirically validate different alternatives, the study uses change data from three application packages of open-source Android data set and evaluates the performance of six different machine learning techniques. The results of the study indicate extensive improvement in the performance of the classification models when using resampling method and robust performance measures.

Keywords: Change proneness, empirical validation, imbalanced learning, machine learning techniques, object-oriented metrics.

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Authors: Shaukat Wasi, Zubair A. Shaikh, Sajid Qasmi, Hussain Sachwani, Rehman Lalani, Aamir Chagani

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Automatic Extraction of Event information from social text stream (emails, social network sites, blogs etc) is a vital requirement for many applications like Event Planning and Management systems and security applications. The key information components needed from Event related text are Event title, location, participants, date and time. Emails have very unique distinctions over other social text streams from the perspective of layout and format and conversation style and are the most commonly used communication channel for broadcasting and planning events. Therefore we have chosen emails as our dataset. In our work, we have employed two statistical NLP methods, named as Finite State Machines (FSM) and Hidden Markov Model (HMM) for the extraction of event related contextual information. An application has been developed providing a comparison among the two methods over the event extraction task. It comprises of two modules, one for each method, and works for both bulk as well as direct user input. The results are evaluated using Precision, Recall and F-Score. Experiments show that both methods produce high performance and accuracy, however HMM was good enough over Title extraction and FSM proved to be better for Venue, Date, and time.

Keywords: Emails, Event Extraction, Event Detection, Finite state machines, Hidden Markov Model.

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Authors: Eakong Atiptamvaree, Twittie Senivongse

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A new paradigm for software design and development models software by its business process, translates the model into a process execution language, and has it run by a supporting execution engine. This process-oriented paradigm promotes modeling of software by less technical users or business analysts as well as rapid development. Since business process models may be shared by different organizations and sometimes even by different business domains, it is interesting to apply a technique used in traditional software component technology to design reusable business processes. This paper discusses an approach to apply a technique for software component fabrication to the design of process-oriented software units, called process components. These process components result from decomposing a business process of a particular application domain into subprocesses with an aim that the process components can be reusable in different process-based software models. The approach is quantitative because the quality of process component design is measured from technical features of the process components. The approach is also strategic because the measured quality is determined against business-oriented component management goals. A software tool has been developed to measure how good a process component design is, according to the required managerial goals and comparing to other designs. We also discuss how we benefit from reusable process components.

Keywords: Business process model, process component, component management goals, measurement

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Authors: Mehdi Modabberifar, Milad Roodi, Ehsan Souri

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In recent years, honeycomb fiber reinforced plastic (FRP) sandwich panels have been increasingly used in various industries. Low weight, low price and high mechanical strength are the benefits of these structures. However, their mechanical properties and behavior have not been fully explored. The objective of this study is to conduct a combined numerical-statistical investigation of honeycomb FRP sandwich beams subject to torsion load. In this paper, the effect of geometric parameters of sandwich panel on maximum shear strain in both face and core and angle of torsion in a honeycomb FRP sandwich structures in torsion is investigated. The effect of Parameters including core thickness, face skin thickness, cell shape, cell size, and cell thickness on mechanical behavior of the structure were numerically investigated. Main effects of factors were considered in this paper and regression equations were derived. Taguchi method was employed as experimental design and an optimum parameter combination for the maximum structure stiffness has been obtained. The results showed that cell size and face skin thickness have the most significant impacts on torsion angle, maximum shear strain in face and core.

Keywords: Finite element, honeycomb FRP sandwich panel, torsion, civil engineering.

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Authors: Norazah Mohd Suki, Norbayah Mohd Suki

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This study proposes a conceptual model and empirically tests the relationships between customers and librarians (i.e. tangibles, responsiveness, assurance, reliability and empathy) with a dependent variable (customer satisfaction) regarding library services. The SERVQUAL instrument was administered to 100 respondents which comprises of staff and students at a public higher learning institution in the Federal Territory of Labuan, Malaysia. They were public university library users. Results revealed that all service quality dimensions tested were significant and influenced customer satisfaction of visitors to a public university library. Assurance is the most important factor that influences customer satisfaction with the services rendered by the librarian. It is imperative for the library management to take note that the top five service attributes that gained greatest attention from library visitors- perspective includes employee willingness to help customers, availability of customer representatives online for response to queries, library staff actively and promptly provide services, signs in the building are clear and library staff are friendly and courteous. This study provides valuable results concerning the determinants of the service quality and customer satisfaction of public university library services from the users' perspective.

Keywords: Service Quality, Customer Satisfaction, SERVQUAL Model, Multiple Regression Analysis

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Authors: Yessine Hadj Kacem, Adel Mahfoudhi, Hedi Tmar, Mohamed Abid

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Unified Modeling Language (UML) extensions for real time embedded systems (RTES) co-design, are taking a growing interest by a great number of industrial and research communities. The extension mechanism is provided by UML profiles for RTES. It aims at improving an easily-understood method of system design for non-experts. On the other hand, one of the key items of the co- design methods is the Hardware/Software partitioning and scheduling tasks. Indeed, it is mandatory to define where and when tasks are implemented and run. Unfortunately the main goals of co-design are not included in the usual practice of UML profiles. So, there exists a need for mapping used models to an execution platform for both schedulability test and HW/SW partitioning. In the present work, test schedulability and design space exploration are performed at an early stage. The proposed approach adopts Model Driven Engineering MDE. It starts from UML specification annotated with the recent profile for the Modeling and Analysis of Real Time Embedded systems MARTE. Following refinement strategy, transformation rules allow to find a feasible schedule that satisfies timing constraints and to define where tasks will be implemented. The overall approach is experimented for the design of a football player robot application.

Keywords: MDE, UML profile, scheduling analysis, HW/SW partitioning.

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Authors: Kisik Song, Sungjoo Lee

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Recent years have seen an increasing number of patent disputes due to excessive competition in the global market and a reduced technology life-cycle; this has increased the risk of investment in technology development. While many global companies have started developing a methodology to identify promising technologies and assess for decisions, the existing methodology still has some limitations. Post hoc assessments of the new technology are not being performed, especially to determine whether the suggested technologies turned out to be promising. For example, in existing quantitative patent analysis, a patent’s citation information has served as an important metric for quality assessment, but this analysis cannot be applied to recently registered patents because such information accumulates over time. Therefore, we propose a new technology assessment model that can replace citation information and positively affect technological development based on post hoc analysis of the patents for promising technologies. Additionally, we collect customer reviews on a target technology to extract keywords that show the customers’ needs, and we determine how many keywords are covered in the new technology. Finally, we construct a portfolio (based on a technology assessment from patent information) and a customer-based marketability assessment (based on review data), and we use them to visualize the characteristics of the new technologies.

Keywords: Technology assessment, patents, citation information, opinion mining.

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Authors: Riccardo Fratini, Riccardo Santini, Jacopo Serafini, Massimo Gennaretti, Stefano Panzieri

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This paper presents a nonlinear differential model, for a three-bladed horizontal axis wind turbine (HAWT) suited for control applications. It is based on a 8-dofs, lumped parameters structural dynamics coupled with a quasi-steady sectional aerodynamics. In particular, using the Euler-Lagrange Equation (Energetic Variation approach), the authors derive, and successively validate, such model. For the derivation of the aerodynamic model, the Greenbergs theory, an extension of the theory proposed by Theodorsen to the case of thin airfoils undergoing pulsating flows, is used. Specifically, in this work, the authors restricted that theory under the hypothesis of low perturbation reduced frequency k, which causes the lift deficiency function C(k) to be real and equal to 1. Furthermore, the expressions of the aerodynamic loads are obtained using the quasi-steady strip theory (Hodges and Ormiston), as a function of the chordwise and normal components of relative velocity between flow and airfoil Ut, Up, their derivatives, and section angular velocity ε˙. For the validation of the proposed model, the authors carried out open and closed-loop simulations of a 5 MW HAWT, characterized by radius R =61.5 m and by mean chord c = 3 m, with a nominal angular velocity Ωn = 1.266rad/sec. The first analysis performed is the steady state solution, where a uniform wind Vw = 11.4 m/s is considered and a collective pitch angle θ = 0.88◦ is imposed. During this step, the authors noticed that the proposed model is intrinsically periodic due to the effect of the wind and of the gravitational force. In order to reject this periodic trend in the model dynamics, the authors propose a collective repetitive control algorithm coupled with a PD controller. In particular, when the reference command to be tracked and/or the disturbance to be rejected are periodic signals with a fixed period, the repetitive control strategies can be applied due to their high precision, simple implementation and little performance dependency on system parameters. The functional scheme of a repetitive controller is quite simple and, given a periodic reference command, is composed of a control block Crc(s) usually added to an existing feedback control system. The control block contains and a free time-delay system eτs in a positive feedback loop, and a low-pass filter q(s). It should be noticed that, while the time delay term reduces the stability margin, on the other hand the low pass filter is added to ensure stability. It is worth noting that, in this work, the authors propose a phase shifting for the controller and the delay system has been modified as e^(−(T−γk)), where T is the period of the signal and γk is a phase shifting of k samples of the same periodic signal. It should be noticed that, the phase shifting technique is particularly useful in non-minimum phase systems, such as flexible structures. In fact, using the phase shifting, the iterative algorithm could reach the convergence also at high frequencies. Notice that, in our case study, the shifting of k samples depends both on the rotor angular velocity Ω and on the rotor azimuth angle Ψ: we refer to this controller as a spatial repetitive controller. The collective repetitive controller has also been coupled with a C(s) = PD(s), in order to dampen oscillations of the blades. The performance of the spatial repetitive controller is compared with an industrial PI controller. In particular, starting from wind speed velocity Vw = 11.4 m/s the controller is asked to maintain the nominal angular velocity Ωn = 1.266rad/s after an instantaneous increase of wind speed (Vw = 15 m/s). Then, a purely periodic external disturbance is introduced in order to stress the capabilities of the repetitive controller. The results of the simulations show that, contrary to a simple PI controller, the spatial repetitive-PD controller has the capability to reject both external disturbances and periodic trend in the model dynamics. Finally, the nominal value of the angular velocity is reached, in accordance with results obtained with commercial software for a turbine of the same type.

Keywords: Wind turbines, aeroelasticity, repetitive control, periodic systems.

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Authors: Qing Dai, Zhengkui Lin, Jiajia Zhang, Yi Qu

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Based on activity method, this paper focuses on morning commuting behavior when commuters travel with autonomous vehicles (AVs). Firstly, a net utility function of commuters is constructed by the activity utility of commuters at home, in car and at workplace, and the disutility of travel time cost and that of schedule delay cost. Then, this net utility function is applied to build an equilibrium model. Finally, under the assumption of constant marginal activity utility, the properties of equilibrium are analyzed. The results show that, in autonomous driving, the starting and ending time of morning peak and the number of commuters who arrive early and late at workplace are the same as those in manual driving. In automatic driving, however, the departure rate of arriving early at workplace is higher than that of manual driving, while the departure rate of arriving late is just the opposite. In addition, compared with manual driving, the departure time of arriving at workplace on time is earlier and the number of people queuing at the bottleneck is larger in automatic driving. However, the net utility of commuters and the total net utility of system in automatic driving are greater than those in manual driving.

Keywords: Autonomous cars, bottleneck model, activity utility, user equilibrium.

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Authors: S. Chiali, Z.Eberrichi, M.Malki

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The number of framework conceived for e-learning constantly increase, unfortunately the creators of learning materials and educational institutions engaged in e-formation adopt a “proprietor" approach, where the developed products (courses, activities, exercises, etc.) can be exploited only in the framework where they were conceived, their uses in the other learning environments requires a greedy adaptation in terms of time and effort. Each one proposes courses whose organization, contents, modes of interaction and presentations are unique for all learners, unfortunately the latter are heterogeneous and are not interested by the same information, but only by services or documents adapted to their needs. Currently the new tendency for the framework conceived for e-learning, is the interoperability of learning materials, several standards exist (DCMI (Dublin Core Metadata Initiative)[2], LOM (Learning Objects Meta data)[1], SCORM (Shareable Content Object Reference Model)[6][7][8], ARIADNE (Alliance of Remote Instructional Authoring and Distribution Networks for Europe)[9], CANCORE (Canadian Core Learning Resource Metadata Application Profiles)[3]), they converge all to the idea of learning objects. They are also interested in the adaptation of the learning materials according to the learners- profile. This article proposes an approach for the composition of courses adapted to the various profiles (knowledge, preferences, objectives) of learners, based on two ontologies (domain to teach and educational) and the learning objects.

Keywords: Adaptive educational hypermedia systems (AEHS), E-learning, Learner's model, Learning objects, Metadata, Ontology.

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Authors: Mohanad Alata, Mohammad Molhim, Abdullah Ramini

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Fuzzy C-means Clustering algorithm (FCM) is a method that is frequently used in pattern recognition. It has the advantage of giving good modeling results in many cases, although, it is not capable of specifying the number of clusters by itself. In FCM algorithm most researchers fix weighting exponent (m) to a conventional value of 2 which might not be the appropriate for all applications. Consequently, the main objective of this paper is to use the subtractive clustering algorithm to provide the optimal number of clusters needed by FCM algorithm by optimizing the parameters of the subtractive clustering algorithm by an iterative search approach and then to find an optimal weighting exponent (m) for the FCM algorithm. In order to get an optimal number of clusters, the iterative search approach is used to find the optimal single-output Sugenotype Fuzzy Inference System (FIS) model by optimizing the parameters of the subtractive clustering algorithm that give minimum least square error between the actual data and the Sugeno fuzzy model. Once the number of clusters is optimized, then two approaches are proposed to optimize the weighting exponent (m) in the FCM algorithm, namely, the iterative search approach and the genetic algorithms. The above mentioned approach is tested on the generated data from the original function and optimal fuzzy models are obtained with minimum error between the real data and the obtained fuzzy models.

Keywords: Fuzzy clustering, Fuzzy C-Means, Genetic Algorithm, Sugeno fuzzy systems.

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Authors: Srikanth Pandurangi, Basheer Mohammed, Nezar Al Sayegh

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In order to meet the increasing demand for housing care for Kuwaiti citizens, the government authorities in Kuwait are undertaking a series of projects in the form of new large cities, outside the current urban area. Al Mutlaa City located to the north-west of the Kuwait Metropolitan Area is one such project out of the 15 planned new cities. The city accommodates a wide variety of residential developments, employment opportunities, commercial, recreational, health care and institutional uses. This paper examines the application of comprehensive transportation demand modeling works undertaken in VISUM platform to understand the future intracity and intercity travel distribution patterns in Kuwait. The scope of models developed varied in levels of detail: strategic model update, sub-area models representing future demand of Al Mutlaa City, sub-area models built to estimate the demand in the residential neighborhoods of the city. This paper aims at offering model update framework that facilitates easy integration between sub-area models and strategic national models for unified traffic forecasts. This paper presents the transportation demand modeling results utilized in informing the planning of multi-modal transportation system for Al Mutlaa City. This paper also presents the household survey data collection efforts undertaken using GPS devices (first time in Kuwait) and notebook computer based digital survey forms for interviewing representative sample of citizens and residents. The survey results formed the basis of estimating trip generation rates and trip distribution coefficients used in the strategic base year model calibration and validation process.

Keywords: GPS based household surveys, transportation infrastructure, origin-destination trip matrices, traffic forecasts, transportation demand modeling, travel behavior patterns.

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Authors: A Fouda, Z. Melikyan

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Air conditioning systems of houses consume large quantity of electricity. To reducing energy consumption for air conditioning purposes it is becoming attractive the use of evaporative cooling air conditioning which is less energy consuming compared to air chillers. But, it is obvious that higher energy efficiency of evaporative cooling is not enough to judge whether evaporative cooling economically is competitive with other types of cooling systems. To proving the higher energy efficiency and cost effectiveness of the evaporative cooling competitive analysis of various types of cooling system should be accomplished. For noted purpose optimization mathematical model for each system should be composed based on system approach analysis. In this paper different types of evaporative cooling-heating systems are discussed and methods for increasing their energy efficiency and as well as determining of their design parameters are developed. The optimization mathematical models for each of them are composed with help of which least specific costs for each of them are reviled. The comparison of specific costs proved that the most efficient and cost effective is considered the “direct evaporating" system if it is applicable for given climatic conditions. Next more universal and applicable for many climatic conditions system providing least cost of heating and cooling is considered the “direct evaporating" system.

Keywords: air, conditioning, system, evaporative cooling, mathematical model, optimization, thermoeconomic.

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Authors: Y. Chiang, J. R. Wang, J. H. Yang, Y. S. Tseng, C. Shih, S. W. Chen

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Kuosheng nuclear power plant (NPP) is a BWR/6 plant in Taiwan. There is more concern for the safety of Spent Fuel Pools (SFPs) in Taiwan after Fukushima event. In order to estimate the safety of Kuosheng NPP SFP, by using MELCOR2.1 and SNAP, the safety analysis of Kuosheng NPP SFP was performed combined with the mitigation strategy of NEI 06-12 report. There were several steps in this research. First, the Kuosheng NPP SFP models were established by MELCOR2.1/SNAP. Second, the Station Blackout (SBO) analysis of Kuosheng SFP was done by TRACE and MELCOR under the cooling system failure condition. The results showed that the calculations of MELCOR and TRACE were very similar in this case. Second, the mitigation strategy analysis was done with the MELCOR model by following the NEI 06-12 report. The results showed the effectiveness of NEI 06-12 strategy in Kuosheng NPP SFP. Finally, a sensitivity study of SFP quenching was done to check the differences of different water injection time and the phenomena during the quenching. The results showed that if the cladding temperature was over 1600 K, the water injection may have chance to cause the accident more severe with more hydrogen generation. It was because of the oxidation heat and the “Breakaway” effect of the zirconium-water reaction. An animation model built by SNAP was also shown in this study.

Keywords: MELCOR, SNAP, spent fuel pool, quenching.

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Authors: Doaa M. Atia, Faten H. Fahmy, Ninet M. Ahmed, Hassen T. Dorrah

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In this paper, the optimum design for renewable energy system powered an aquaculture pond was determined. Hybrid Optimization Model for Electric Renewable (HOMER) software program, which is developed by U.S National Renewable Energy Laboratory (NREL), is used for analyzing the feasibility of the stand alone and hybrid system in this study. HOMER program determines whether renewable energy resources satisfy hourly electric demand or not. The program calculates energy balance for every 8760 hours in a year to simulate operation of the system. This optimization compares the demand for the electrical energy for each hour of the year with the energy supplied by the system for that hour and calculates the relevant energy flow for each component in the model. The essential principle is to minimize the total system cost while HOMER ensures control of the system. Moreover the feasibility analysis of the energy system is also studied. Wind speed, solar irradiance, interest rate and capacity shortage are the parameters which are taken into consideration. The simulation results indicate that the hybrid system is the best choice in this study, yielding lower net present cost. Thus, it provides higher system performance than PV or wind stand alone systems.

Keywords: Wind stand-alone system, Photovoltaic stand-alone system, Hybrid system, Optimum system sizing, feasibility, Cost analysis.

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Authors: Hsin-Chang Yang, Chung-Hong Lee, Kuo-Lung Ke

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The self-organizing map (SOM) model is a well-known neural network model with wide spread of applications. The main characteristics of SOM are two-fold, namely dimension reduction and topology preservation. Using SOM, a high-dimensional data space will be mapped to some low-dimensional space. Meanwhile, the topological relations among data will be preserved. With such characteristics, the SOM was usually applied on data clustering and visualization tasks. However, the SOM has main disadvantage of the need to know the number and structure of neurons prior to training, which are difficult to be determined. Several schemes have been proposed to tackle such deficiency. Examples are growing/expandable SOM, hierarchical SOM, and growing hierarchical SOM. These schemes could dynamically expand the map, even generate hierarchical maps, during training. Encouraging results were reported. Basically, these schemes adapt the size and structure of the map according to the distribution of training data. That is, they are data-driven or dataoriented SOM schemes. In this work, a topic-oriented SOM scheme which is suitable for document clustering and organization will be developed. The proposed SOM will automatically adapt the number as well as the structure of the map according to identified topics. Unlike other data-oriented SOMs, our approach expands the map and generates the hierarchies both according to the topics and their characteristics of the neurons. The preliminary experiments give promising result and demonstrate the plausibility of the method.

Keywords: Self-organizing map, topic identification, learning algorithm, text clustering.

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Authors: George Yungchih Wang

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Real options theory suggests that managerial flexibility embedded within irreversible investments can account for a significant value in project valuation. Although the argument has become the dominant focus of capital investment theory over decades, yet recent survey literature in capital budgeting indicates that corporate practitioners still do not explicitly apply real options in investment decisions. In this paper, we explore how real options decision criteria can be transformed into equivalent capital budgeting criteria under the consideration of uncertainty, assuming that underlying stochastic process follows a geometric Brownian motion (GBM), a mixed diffusion-jump (MX), or a mean-reverting process (MR). These equivalent valuation techniques can be readily decomposed into conventional investment rules and “option impacts", the latter of which describe the impacts on optimal investment rules with the option value considered. Based on numerical analysis and Monte Carlo simulation, three major findings are derived. First, it is shown that real options could be successfully integrated into the mindset of conventional capital budgeting. Second, the inclusion of option impacts tends to delay investment. It is indicated that the delay effect is the most significant under a GBM process and the least significant under a MR process. Third, it is optimal to adopt the new capital budgeting criteria in investment decision-making and adopting a suboptimal investment rule without considering real options could lead to a substantial loss in value.

Keywords: real options, capital budgeting, geometric Brownianmotion, mixed diffusion-jump, mean-reverting process

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Authors: Wenhsuan Lee, Yachi Chang, Yingyih Shih

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In clinical practices, it is common that while facing the unknown progress of their disease, palliative care patients may easily feel anxious and depressed. These types of reactions are a cause of psychosomatic diseases and may also influence treatment results. However, the purpose of palliative care is to provide relief from all kinds of pains. Therefore, how to make patients more comfortable is an issue worth studying. This study adopted the “bio-psycho-social model” proposed by Engel and applied spontaneous breathing training, in the hope of seeing patients’ psychological state changes caused by their physiological state changes, improvements in their anxious conditions, corresponding adjustments of their cognitive functions, and further enhancement of their social functions and the social support system. This study will be a one-year study. Palliative care outpatients will be recruited and assigned to the experimental group or the control group for six outpatient visits (once a month), with 80 patients in each group. The patients of both groups agreed that this study can collect their physiological quantitative data using an HRV device before the first outpatient visit. They also agreed to answer the “Beck Anxiety Inventory (BAI)”, the “Taiwanese version of the WHOQOL-BREF questionnaire” before the first outpatient visit, to fill a self-report questionnaire after each outpatient visit, and to answer the “Beck Anxiety Inventory (BAI)”, the “Taiwanese version of the WHOQOL-BREF questionnaire” after the last outpatient visit. The patients of the experimental group agreed to receive the breathing training under HRV monitoring during the first outpatient visit of this study. Before each of the following three outpatient visits, they were required to fill a self-report questionnaire regarding their breathing practices after going home. After the outpatient visits, they were taught how to practice breathing through an HRV device and asked to practice it after going home. Later, based on the results from the HRV data analyses and the pre-tests and post-tests of the “Beck Anxiety Inventory (BAI)”, the “Taiwanese version of the WHOQOL-BREF questionnaire”, the influence of the breathing training in the bio, psycho, and social aspects were evaluated. The data collected through the self-report questionnaires of the patients of both groups were used to explore the possible interfering factors among the bio, psycho, and social changes. It is expected that this study will support the “bio-psycho-social model” proposed by Engel, meaning that bio, psycho, and social supports are closely related, and that breathing training helps to transform palliative care patients’ psychological feelings of anxiety and depression, to facilitate their positive interactions with others, and to improve the quality medical care for them.

Keywords: Palliative care, breathing training, bio-psycho-social Model, heart rate variability.

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Authors: B.Rajendra Prasath, P.Tamilporai, Mohd.F.Shabir

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An effort has been taken to simulate the combustion and performance characteristics of biodiesel fuel in direct injection (D.I) low heat rejection (LHR) diesel engine. Comprehensive analyses on combustion characteristics such as cylinder pressure, peak cylinder pressure, heat release and performance characteristics such as specific fuel consumption and brake thermal efficiency are carried out. Compression ignition (C.I) engine cycle simulation was developed and modified in to LHR engine for both diesel and biodiesel fuel. On the basis of first law of thermodynamics the properties at each degree crank angle was calculated. Preparation and reaction rate model was used to calculate the instantaneous heat release rate. A gas-wall heat transfer calculations are based on the ANNAND-s combined heat transfer model with instantaneous wall temperature to analyze the effect of coating on heat transfer. The simulated results are validated by conducting the experiments on the test engine under identical operating condition on a turbocharged D.I diesel engine. In this analysis 20% of biodiesel (derived from Jatropha oil) blended with diesel and used in both conventional and LHR engine. The simulated combustion and performance characteristics results are found satisfactory with the experimental value.

Keywords: Biodiesel, Direct injection, Low heat rejection, Turbocharged engine

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Authors: Walaa Khalaf, Calogero Pace, Manlio Gaudioso

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We present an Electronic Nose (ENose), which is aimed at identifying the presence of one out of two gases, possibly detecting the presence of a mixture of the two. Estimation of the concentrations of the components is also performed for a volatile organic compound (VOC) constituted by methanol and acetone, for the ranges 40-400 and 22-220 ppm (parts-per-million), respectively. Our system contains 8 sensors, 5 of them being gas sensors (of the class TGS from FIGARO USA, INC., whose sensing element is a tin dioxide (SnO2) semiconductor), the remaining being a temperature sensor (LM35 from National Semiconductor Corporation), a humidity sensor (HIH–3610 from Honeywell), and a pressure sensor (XFAM from Fujikura Ltd.). Our integrated hardware–software system uses some machine learning principles and least square regression principle to identify at first a new gas sample, or a mixture, and then to estimate the concentrations. In particular we adopt a training model using the Support Vector Machine (SVM) approach with linear kernel to teach the system how discriminate among different gases. Then we apply another training model using the least square regression, to predict the concentrations. The experimental results demonstrate that the proposed multiclassification and regression scheme is effective in the identification of the tested VOCs of methanol and acetone with 96.61% correctness. The concentration prediction is obtained with 0.979 and 0.964 correlation coefficient for the predicted versus real concentrations of methanol and acetone, respectively.

Keywords: Electronic nose, Least square regression, Mixture ofgases, Support Vector Machine.

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Authors: Alper Akın, İbrahim Aydoğdu

Abstract:

This study presents a hybrid metaheuristic algorithm to obtain optimum designs for steel space buildings. The optimum design problem of three-dimensional steel frames is mathematically formulated according to provisions of LRFD-AISC (Load and Resistance factor design of American Institute of Steel Construction). Design constraints such as the strength requirements of structural members, the displacement limitations, the inter-story drift and the other structural constraints are derived from LRFD-AISC specification. In this study, a hybrid algorithm by using teachinglearning based optimization (TLBO) and harmony search (HS) algorithms is employed to solve the stated optimum design problem. These algorithms are two of the recent additions to metaheuristic techniques of numerical optimization and have been an efficient tool for solving discrete programming problems. Using these two algorithms in collaboration creates a more powerful tool and mitigates each other’s weaknesses. To demonstrate the powerful performance of presented hybrid algorithm, the optimum design of a large scale steel building is presented and the results are compared to the previously obtained results available in the literature.

Keywords: Optimum structural design, hybrid techniques, teaching-learning based optimization, harmony search algorithm, minimum weight, steel space frame.

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Authors: Altab Hossain, Ataur Rahman, A. K. M. Mohiuddin, Yulfian Aminanda

Abstract:

Fuzzy logic system (FLS) is used in this study to predict the tractive performance in terms of traction force, and motion resistance for an intelligent air cushion track vehicle while it operates in the swamp peat. The system is effective to control the intelligent air –cushion system with measuring the vehicle traction force (TF), motion resistance (MR), cushion clearance height (CH) and cushion pressure (CP). Ultrasonic displacement sensor, pull-in solenoid electromagnetic switch, pressure control sensor, micro controller, and battery pH sensor are incorporated with the Fuzzy logic system to investigate experimentally the TF, MR, CH, and CP. In this study, a comparison for tractive performance of an intelligent air cushion track vehicle has been performed with the results obtained from the predicted values of FLS and experimental actual values. The mean relative error of actual and predicted values from the FLS model on traction force, and total motion resistance are found as 5.58 %, and 6.78 % respectively. For all parameters, the relative error of predicted values are found to be less than the acceptable limits. The goodness of fit of the prediction values from the FLS model on TF, and MR are found as 0.90, and 0.98 respectively.

Keywords: Cushion pressure, Fuzzy logic, Motion resistance, Traction force.

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Authors: Vasudha Hegde, Narendra Chaulagain, H. M. Ravikumar, Sonu Mishra, Siva Yellampalli

Abstract:

Acoustic sensors are extensively used in recent days not only for sensing and condition monitoring applications but also for small scale energy harvesting applications to power wireless sensor networks (WSN) due to their inherent advantages. The natural frequency of the structure plays a major role in energy harvesting applications since the sensor key element has to operate at resonant frequency. In this paper, circular diaphragm based MEMS acoustic sensor is modelled by Lumped Element Model (LEM) and the natural frequency is compared with the simulated model using Finite Element Method (FEM) tool COMSOL Multiphysics. The sensor has the circular diaphragm of 3000 µm radius and thickness of 30 µm to withstand the high SPL (Sound Pressure Level) and also to withstand the various fabrication steps. A Piezoelectric ZnO layer of thickness of 1 µm sandwiched between two aluminium electrodes of thickness 0.5 µm and is coated on the diaphragm. Further, a channel with radius 3000 µm radius and length 270 µm is connected at the bottom of the diaphragm. The natural frequency of the structure by LEM method is approximately 16.6 kHz which is closely matching with that of simulated structure with suitable approximations.

Keywords: Acoustic sensor, diaphragm based, lumped element modeling, natural frequency, piezoelectric.

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Authors: Diana Fajardo, Sebastián Ortiz, Oscar Herrera, Angélica Santis

Abstract:

Currently, in Colombia is arising a problem related to collecting used lubricant oils which are generated by the increment of the vehicle fleet. This situation does not allow a proper disposal of this type of waste, which in turn results in a negative impact on the environment. Therefore, through the comparative analysis of various heuristics, the best solution to the VRP (Vehicle Routing Problem) was selected by comparing costs and times for the collection of used lubricant oils in the city of Pereira, Colombia; since there is no presence of management companies engaged in the direct administration of the collection of this pollutant. To achieve this aim, six proposals of through methods of solution of two phases were discussed. First, the assignment of the group of generator points of the residue was made (previously identified). Proposals one and four of through methods are based on the closeness of points. The proposals two and five are using the scanning method and the proposals three and six are considering the restriction of the capacity of collection vehicle. Subsequently, the routes were developed - in the first three proposals by the Clarke and Wright's savings algorithm and in the following proposals by the Traveling Salesman optimization mathematical model. After applying techniques, a comparative analysis of the results was performed and it was determined which of the proposals presented the most optimal values in terms of the distance, cost and travel time.

Keywords: Heuristics, optimization model, savings algorithm used vehicular oil, VRP.

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Authors: Ali Heidari

Abstract:

This paper presents the principles for improving flood mitigation operation in multipurpose dams and maximizing reservoir performance during flood occurrence with a focus on the real-time operation of gated spillways. The criteria of operation include the safety of dams during flood management, minimizing the downstream flood risk by decreasing the flood hazard and fulfilling water supply and other purposes of the dam operation in mid and long terms horizons. The parameters deemed to be important include flood inflow, outlet capacity restrictions, downstream flood inundation damages, economic revenue of dam operation, and environmental and sedimentation restrictions. A simulation model was used to determine the real-time release of the Dez Dam located in the Dez Rivers in southwest Iran, considering the gate regulation curves for the gated spillway. The results of the simulation model show that there is a possibility to improve the current procedures used in the real-time operation of the dams, particularly using gate regulation curves and early flood forecasting system results. The Dez Dam operation data show that in one of the best flood control records, 17% of the total active volume and flood control pool of the reservoir have not been used in decreasing the downstream flood hazard despite the availability of a flood forecasting system.

Keywords: Dam operation, flood control criteria, Dez Dam, Iran.

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Authors: Badr Alsulami, Ahmed S. Elamary

Abstract:

This paper summarizes and presents main results of an in-depth numerical analysis dealing with the shear buckling resistance of aluminum plate girders. The studies conducted have permitted the development of a simple design expression to determine the critical shear buckling stress in aluminum web panels. This expression takes into account the effects of reduction of strength in aluminum alloys due to welding process. Ultimate shear resistance (USR) of plate girders can be obtained theoretically using Cardiff theory or Hӧglund’s theory. USR of aluminum alloy plate girders predicted theoretically using BS8118 appear inconsistent when compared with test data. Theoretical predictions based on Hӧglund’s theory, are more realistic. Cardiff theory proposed to predict the USR of steel plate girders only. Welded aluminum alloy plate girders studied experimentally by others; the USR resulted from tests are reviewed. Comparison between the test results with the values obtained from Hӧglund’s theory, BS8118 design method and Cardiff theory performed theoretically. Finally, a new equation based on Cardiff tension-field theory, proposed to predict theoretically the USR of aluminum plate girders.

Keywords: Shear resistance, Aluminum, Cardiff theory, Hӧglund's theory, Plate girder.

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Authors: C. Hemasundara Rao, M. Madhavi Latha

Abstract:

Image compression can improve the performance of the digital systems by reducing time and cost in image storage and transmission without significant reduction of the image quality. Furthermore, the discrete cosine transform has emerged as the new state-of-the art standard for image compression. In this paper, a hybrid image compression technique based on reversible blockade transform coding is proposed. The technique, implemented over regions of interest (ROIs), is based on selection of the coefficients that belong to different transforms, depending on the coefficients is proposed. This method allows: (1) codification of multiple kernals at various degrees of interest, (2) arbitrary shaped spectrum,and (3) flexible adjustment of the compression quality of the image and the background. No standard modification for JPEG2000 decoder was required. The method was applied over different types of images. Results show a better performance for the selected regions, when image coding methods were employed for the whole set of images. We believe that this method is an excellent tool for future image compression research, mainly on images where image coding can be of interest, such as the medical imaging modalities and several multimedia applications. Finally VLSI implementation of proposed method is shown. It is also shown that the kernal of Hartley and Cosine transform gives the better performance than any other model.

Keywords: VLSI, Discrete Cosine Transform, JPEG, Hartleytransform, Radon Transform

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Authors: Nipun Manirajan, Heeralal Gargama, Sushil Guhe, Manoj Prabhakaran

Abstract:

In railway industry, train sets are designed based on contractual requirements (mission profile), where reliability targets are measured in terms of mean distance between failures (MDBF). However, during the beginning of revenue services, trains do not achieve the designed mission profile distance (mileage) within the timeframe due to infrastructure constraints, scarcity of commuters or other operational challenges thereby not respecting the original design inputs. Since trains do not run sufficiently and do not achieve the designed mileage within the specified time, car builder has a risk of not achieving the contractual MDBF target. This paper proposes a constant failure rate based model to deal with the situations where mileage accumulation is not a part of the design mission profile. The model provides appropriate MDBF target to be demonstrated based on actual accumulated mileage. A case study of rolling stock running in the field is undertaken to analyze the failure data and MDBF target demonstration during low mileage accumulation. The results of case study prove that with the proposed method, reliability targets are achieved under low mileage accumulation.

Keywords: Mean distance between failures, mileage based reliability, reliability target normalization, rolling stock reliability.

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