Search results for: modified model

Authors: Jana Gutierez Chvalkovska, Michal Mejstrik, Matej Urban

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There are pending discussions over the mapping of country export potential in order to refocus export strategy of firms and its evidence-based promotion by the Export Credit Agencies (ECAs) and other permitted vehicles of governments. In this paper we develop our version of an applied model that offers “stepwise” elimination of unattractive markets. We modify and calibrate the model for the particular features of the Czech Republic and specific pilot cases where we apply an individual approach to each sector.

Keywords: export strategy, modeling export, calibration, export promotion

Procedia PDF Downloads 498

Authors: Makduma Zahan Badhan, M. Abdul Matin

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A study has been undertaken to develop a fluvial stage-discharge rating curve for Jamuna River. Past Cross-sectional survey of Jamuna River reach within Sirajgonj and Tangail has been analyzed. The analysis includes the estimation of discharge carrying capacity, possible maximum scour depth and sediment transport capacity of the selected reaches. To predict the discharge and sediment carrying capacity, stream flow data which include cross-sectional area, top width, water surface slope and median diameter of the bed material of selected stations have been collected and some are calculated from reduced level data. A well-known resistance equation has been adopted and modified to a simple form in order to be used in the present analysis. The modified resistance equation has been used to calculate the mean velocity through the channel sections. In addition, a sediment transport equation has been applied for the prediction of transport capacity of the various sections. Results show that the existing drainage sections of Jamuna channel reach under study have adequate carrying capacity under existing bank-full conditions, but these reaches are subject to bed erosion even in low flow situations. Regarding sediment transport rate, it can be estimated that the channel flow has a relatively high range of bed material concentration. Finally, stage­ discharge curves for various sections have been developed. Based on stage-discharge rating data of various sections, water surface profile and sediment-rating curve of Jamuna River have been developed and also the flooding conditions have been analyzed from predicted water surface profile.

Keywords: discharge rating, flow profile, fluvial, sediment rating

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Authors: Enrique Barbieri

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The basic disease-spread model described by three states denoting the susceptible (S), infectious (I), and removed (recovered and deceased) (R) sub-groups of the total population N, or SIR model, has been considered. Heuristic mitigating action profiles of the pharmaceutical and non-pharmaceutical types may be developed in a control design setting for the purpose of reducing the transmission rate or improving the recovery rate parameters in the model. Even though the transmission and recovery rates are not control inputs in the traditional sense, a linear observer and feedback controller can be tuned to generate an asymptotic estimate of the transmission rate for a linearized, discrete-time version of the SIR model. Then, a set of mitigating actions is suggested to steer the basic reproduction number toward unity, in which case the disease does not spread, and the infected population state does not suffer from multiple waves. The special case of piecewise constant transmission rate is described and applied to a seventh-order SEIQRDP model, which segments the population into four additional states. The offline simulations in discrete time may be used to produce heuristic policies implemented by public health and government organizations.

Keywords: control of SIR, observer, SEIQRDP, disease spread

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Authors: Anastasis D. Petrou

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Innovation and strategy discussions do highlight open innovation as a new paradigm in business. Yet, a number of stumbling blocks in the form of closed innovation principles weaved into the fabric of a traditional business model stand in the way of the new paradigm’s momentum to increase value in various business contexts. The paper argues that businesses considering an engagement with the open innovation paradigm would need to take steps to improve their multiplicative, absorptive and relational capabilities, respectively. The needed improvements would amount to a business model evolutionary transformation and eventually bring about a paradigm overhaul in business. The transformation is worth staging over time to ensure that open innovation is developed across interconnected and partnered areas of strategic importance. This article develops an open innovation strategy (OIS) capabilities model, and employs examples from different industries to briefly discuss OIS’s potential to augment business value in a number of suggested areas for future research.

Keywords: close innovation, open innovation paradigm, open innovation strategy (OIS) paradigm, OIS capabilities model, multiplicative capability, absorptive capability, relational capability

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Authors: Xian Li, Qing-Guo Wang, Jiangshuai Huang, Jidong Liu, Ming Yu, Tan Kok Poh

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In power industry, accurate electricity demand forecasting for a certain leading time is important for system operation and control, etc. In this paper, we investigate the modeling and forecasting of Singapore’s electricity demand. Several standard models, such as HWT exponential smoothing model, the ARMA model and the ANNs model have been proposed based on historical demand data. We applied them to Singapore electricity market and proposed three refinements based on simulation to improve the modeling accuracy. Compared with existing models, our refined model can produce better forecasting accuracy. It is demonstrated in the simulation that by adding forecasting error into the forecasting equation, the modeling accuracy could be improved greatly.

Keywords: power industry, electricity demand, modeling, forecasting

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Authors: B. Van Kessel, P. T. Kockelkorn, T. R. Speelman, T. C. Wierikx, C. Mai Van, T. A. Bogaard

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Saltwater intrusion is a common problem in estuaries around the world, as it could hinder the freshwater supply of coastal zones. This problem is likely to grow due to climate change and sea-level rise. The influence of these factors on the saltwater intrusion was investigated for the Cai River in the Khanh Hoa province in Vietnam. In addition, the Cai River has high seasonal fluctuations in discharge, leading to increased saltwater intrusion during the dry season. Sea level rise, river discharge changes, river mouth widening and a proposed saltwater intrusion prevention dam can have influences on the saltwater intrusion but have not been quantified for the Cai River estuary. This research used both an analytical and numerical model to investigate the effect of the aforementioned factors. The analytical model was based on a model proposed by Savenije and was calibrated using limited in situ data. The numerical model was a 3D hydrodynamic model made using the Delft3D4 software. The analytical model and numerical model agreed with in situ data, mostly for tidally average data. Both models indicated a roughly similar dependence on discharge, also agreeing that this parameter had the most severe influence on the modeled saltwater intrusion. Especially for discharges below 10 m/s3, the saltwater was predicted to reach further than 10 km. In the models, both sea-level rise and river widening mainly resulted in salinity increments up to 3 kg/m3 in the middle part of the river. The predicted sea-level rise in 2070 was simulated to lead to an increase of 0.5 km in saltwater intrusion length. Furthermore, the effect of the saltwater intrusion dam seemed significant in the model used, but only for the highest position of the gate.

Keywords: Cai River, hydraulic models, river discharge, saltwater intrusion, tidal barriers

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Authors: Omar Alzeley, Sergey Utev

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Minimizing a constrained multivariate function is the fundamental of Machine learning, and these algorithms are at the core of data mining and data visualization techniques. The decision function that maps input points to output points is based on the result of optimization. This optimization is the central of learning theory. One approach to complex systems where the dynamics of the system is inferred by a statistical analysis of the fluctuations in time of some associated observable is time series analysis. The purpose of this paper is a mathematical transition from the autoregressive model of classical time series to the matrix formalization of quantum theory. Firstly, we have proposed a quantum time series model (QTS). Although Hamiltonian technique becomes an established tool to detect a deterministic chaos, other approaches emerge. The quantum probabilistic technique is used to motivate the construction of our QTS model. The QTS model resembles the quantum dynamic model which was applied to financial data. Secondly, various statistical methods, including machine learning algorithms such as the Kalman filter algorithm, are applied to estimate and analyses the unknown parameters of the model. Finally, simulation techniques such as Markov chain Monte Carlo have been used to support our investigations. The proposed model has been examined by using real and simulated data. We establish the relation between quantum statistical machine and quantum time series via random matrix theory. It is interesting to note that the primary focus of the application of QTS in the field of quantum chaos was to find a model that explain chaotic behaviour. Maybe this model will reveal another insight into quantum chaos.

Keywords: machine learning, simulation techniques, quantum probability, tensor product, time series

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Authors: Lely A. Luengas, Pedro R. Vizcaya, Giovanni Sánchez

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In this paper, a methodology is presented to obtain the Static Alignment Model for any transtibial amputee person. The proposed methodology starts from experimental data collected on the Hospital Militar Central, Bogotá, Colombia. The effects of transtibial prosthesis malalignment on amputees were measured in terms of joint angles, center of pressure (COP) and weight distribution. Some statistical tools are used to obtain the model parameters. Mathematical predictive models of prosthetic alignment were created. The proposed models are validated in amputees and finding promising results for the prosthesis Static Alignment. Static alignment process is unique to each subject; nevertheless the proposed methodology can be used in each transtibial amputee.

Keywords: information theory, prediction model, prosthetic alignment, transtibial prosthesis

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Authors: Zhilin Wang, Zhihao Zheng, Linxin Liu

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This study proposes a low-code model-building approach that aims to simplify the development and deployment of artificial intelligence (AI) models. With an intuitive way to drag and drop and connect components, users can easily build complex models and integrate multiple algorithms for training. After the training is completed, the system automatically generates a callable model service API. This method not only lowers the technical threshold of AI development and improves development efficiency but also enhances the flexibility of algorithm integration and simplifies the deployment process of models. The core strength of this method lies in its ease of use and efficiency. Users do not need to have a deep programming background and can complete the design and implementation of complex models with a simple drag-and-drop operation. This feature greatly expands the scope of AI technology, allowing more non-technical people to participate in the development of AI models. At the same time, the method performs well in algorithm integration, supporting many different types of algorithms to work together, which further improves the performance and applicability of the model. In the experimental part, we performed several performance tests on the method. The results show that compared with traditional model construction methods, this method can make more efficient use, save computing resources, and greatly shorten the model training time. In addition, the system-generated model service interface has been optimized for high availability and scalability, which can adapt to the needs of different application scenarios.

Keywords: low-code, model building, artificial intelligence, algorithm integration, model deployment

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Authors: Olubunmi S. Shittu, Olufemi J. Ayodele, Augustus O. A. Ilori, Abidemi O. Filani, Adetola T. Afuye

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Indiscriminate refuse dumping in and around Ado-Ekiti combined with improper management of few available dumpsites, such as Ilokun dumpsite, posed the threat of heavy metals pollution in the surrounding soils and underground water that needs assessment using pollution indices. Surface soils (0-15 cm) were taken from the centre of Ilokun dumpsite (0 m) and environs at different directions and distances during the dry and wet seasons, as well as a background sample at 1000 m away, adjacent to the dumpsite at Ilokun, Ado-Ekiti, Nigeria. The concentration of heavy metals used to calculate the pollution indices for the soils were determined using Atomic Adsorption Spectrophotometer. The soils recorded high concentrations of all the heavy metals above the background concentrations irrespective of the season with highest concentrations at the 0 m except Ni and Fe at 50 m during the dry and wet season, respectively. The heavy metals concentration were in the order of Ni > Mn > Pb > Cr > Cu > Cd > Fe during the dry season, and Fe > Cr > Cu > Pb > Ni > Cd > Mn during the wet season. Using the Contamination Factor (CF), the soils were classified to be moderately contaminated with Cd and Fe to very high contamination with other metals during the dry season and low Cd contamination (0.87), moderate contamination with Fe, Pb, Mn and Ni and very high contamination with Cr and Cu during the wet season. At both seasons, the Pollution Load Index (PLI) indicates the soils to be generally polluted with heavy metals and the Geoaccumulation Index (I_geo) calculated shown the soils to be in unpolluted to moderately polluted levels. Enrichment Factor (EF) implied the soils to be deficiently enriched with all the heavy metals except Cr (7.90) and Cu (6.42) that were at significantly enrichment levels during the wet season. Modified Degree of Contamination (mC_d) recorded, indicated the soils to be of very high to extremely high degree of contamination during the dry season and moderate degree of contamination during the wet season except 0 m with high degree of contamination. The concentration of heavy metals in the soils combined with some of the pollution indices indicated the soils in and around the Ilokun Dumpsite are being polluted with heavy metals from anthropogenic sources constituted by the indiscriminate refuse dumping.

Keywords: contamination factor, enrichment factor, geoaccumulation index, modified degree of contamination, pollution load index

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Authors: Christopher Deekia Nwimae, Nigel Simms, Liyun Lao

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Erosion in pipe bends caused by particles is a major obstacle in the oil and gas fields and might cause the breakdown of production equipment. This work studied the effects imposed by flow velocity and impact of solid particles diameter in an elbow; erosion rate was verified with experimental data using the computational fluid dynamics (CFD) approach. Two-way coupled Euler-Lagrange and discrete phase model was employed to calculate the air/solid particle flow in an elbow. One erosion model and three-particle rebound models were used to predict the erosion rate on the 90° elbows. The generic erosion model was used in the CFD-based erosion model, and after comparing it with experimental data, results showed agreement with the CFD-based predictions as observed.

Keywords: erosion, prediction, elbow, computational fluid dynamics

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Authors: Yoshimoto Kurihara, Tad Gonsalves

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Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%.

Keywords: 6D posture estimation, image recognition, deep learning, AlexNet

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Authors: Yaser Al-Anii, Abdulmajeed Almaneea, Jonathan L. Summers, Harvey M. Thompson, Nikil Kapur

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The natural convection cooling system of a fully immersed server in dielectric liquid is studied numerically. In present case study, the dielectric liquid represents working fluid and it is in contact with server inside capsule. The capsule includes electronic component and fluid, which can be modelled as saturated porous media. This medium follow Darcy flow regime and assumed to be in balance between its components. The study focus is on role of spatial parameters on thermal behavior of convective heat transfer. Based on server known unit, which is 1U, two parameters Ly and S are changed to test their effect. Meanwhile, wide range of modified Rayleigh number, which is 0.5 to 300, are covered to better understand thermal performance. Navier-Stokes equations are used to model physical domain. Furthermore, successive over relaxation and time marching techniques are used to solve momentum and energy equation. From obtained correlation, the in-between distance S is more effective on Nusselt number than distance to edge Ly by approximately 14%. In addition, as S increase, the average Nusselt number of the upper unit is increased sharply, whereas the lower one keeps on same level.

Keywords: convective cooling of server, darcy flow, liquid-immersed server, porous media

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Authors: Mohammad Y. Badiee, Saeed Golestani, Mir Saman Pishvaee

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In recent years consumers and governments have been pushing companies to design their activities in such a way as to reduce negative environmental impacts by producing renewable product or threat free disposal policy more and more. It is therefore important to focus more accurate to the optimization of various aspect of total supply chain. Modeling a supply chain can be a challenging process due to the fact that there are a large number of factors that need to be considered in the model. The use of multi-objective optimization can lead to overcome those problems since more information is used when designing the model. Uncertainty is inevitable in real world. Considering uncertainty on parameters in addition to use multi-objectives are ways to give more flexibility to the decision making process since the process can take into account much more constraints and requirements. In this paper we demonstrate a stochastic scenario based robust model to cope with uncertainty in a closed-loop multi-objective supply chain. By applying the proposed model in a real world case, the power of proposed model in handling data uncertainty is shown.

Keywords: supply chain management, closed-loop supply chain, multi-objective optimization, goal programming, uncertainty, robust optimization

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Authors: Selin Guney, Andres Riquelme

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The traditional bio-economic method for fisheries modeling uses some estimate of the growth parameters and the system carrying capacity from a biological model for the population dynamics (usually a logistic population growth model) which is then analyzed as a traditional production function. The stock dynamic is transformed into a revenue function and then compared with the extraction costs to estimate the maximum economic yield. In this paper, the logistic population growth model for the population is combined with a forecast of the abundance and location of the stock by using a generalized additive model approach. The paper focuses on the Chilean hake population. This method allows for the incorporation of climatic variables and the interaction with other marine species, which in turn will increase the reliability of the estimates and generate better extraction paths for different conservation objectives, such as the maximum biological yield or the maximum economic yield.

Keywords: bio-economic, fisheries, GAM, production

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Authors: Georgia M. Michailidou, Nina-Maria S. Ainali, Eleftheria C. Xanthopoulou, Dimitrios N. Bikiaris

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Polysaccharides are polymeric materials derived from nature. They are extensively used in pharmaceutical technology due to their low cost, their ready availability and their low toxicity. Chitosan is the product derived from the deacetylation of chitin usually obtained from arthropods. It is a linear polysaccharide which is composed of repeated units of N-deacetylated amino groups and some N-acetylated groups residues. Due to its excellent biological properties, it is an attractive natural polymer. It is biocompatible with low toxicity and complete biodegradability. Although it has excellent properties, the chemical modification of its structure results in new derivatives with ameliorated and more improved properties compared to the initial polymer. This is the exact purpose of the present study in which chitosan was modified with three different monomers, namely trans-aconitic acid, succinic anhydride and 2-hydroxyethyl acrylate. In chitosan’s modification with trans aconitic acid, EDC was utilized as an activator of the carboxylic groups of the monomer, and then a coupling reaction with the amino groups took place. Succinic anhydride reacted with chitosan through a ring opening reaction while 2-hydroxyethyl acrylate reacted through the addition of chitosan’s amino group to the double bond of the monomer. Through FTIR and NMR measurements the success of each reaction was confirmed, and the new structures of the derivatives were verified. X-ray diffraction was utilized in order to examine the effect of the modifications in chitosan’s crystallinity. Finally, swelling tests were conducted in order to assess the improved ability of the new polymeric materials to absorb water. Our results support the successful modification of chitosan’s macromolecular chains in all three reactions. Furthermore, the new derivatives appear to be amorphous concerning their crystallinity and have great ability in absorbing water.

Keywords: chitosan, derivatives, modification, polysaccharide

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Authors: S. Sonkham, U. Pinsopon, W. Chatlatanagulchai

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This paper presents a method of sliding mode control (SMC) designing and developing for the servo system in a dual-stage actuator (DSA) hard disk drive. Mathematical modelling of hard disk drive actuators is obtained, extracted from measuring frequency response of the voice-coil motor (VCM) and PZT micro-actuator separately. Matlab software tools are used for mathematical model estimation and also for controller design and simulation. A model-reference approach for tracking requirement is selected as a proposed technique. The simulation results show that performance of a model-reference SMC controller design in DSA servo control can be satisfied in the tracking error, as well as keeping the positioning of the head within the boundary of +/-5% of track width under the presence of internal and external disturbance. The overall results of model-reference SMC design in DSA are met per requirement specifications and significant reduction in %off track is found when compared to the single-state actuator (SSA).

Keywords: hard disk drive, dual-stage actuator, track following, hdd servo control, sliding mode control, model-reference, tracking control

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Authors: Shahrokh Barati

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In this paper, we introduced AIDS disease at first, then proposed dynamic model illustrate its progress, after expression of a short history of nonlinear modeling by polynomial phasing systems, we considered the stability conditions of the systems, which contained a huge amount of researches in order to modeling and control of AIDS in dynamic nonlinear form, in this approach using a frame work of control any polynomial phasing modeling system which have been generalized by part of phasing model of T-S, in order to control the system in better way, the stability conditions were achieved based on polynomial functions, then we focused to design the appropriate controller, firstly we considered the equilibrium points of system and their conditions and in order to examine changes in the parameters, we presented polynomial phase model that was the generalized approach rather than previous Takagi Sugeno models, then with using case we evaluated the equations in both open loop and close loop and with helping the controlling feedback, the close loop equations of system were calculated, to simulate nonlinear model of AIDS disease, we used polynomial phasing controller output that was capable to make the parameters of a nonlinear system to follow a sustainable reference model properly.

Keywords: polynomial fuzzy, AIDS, nonlinear AIDS model, fuzzy control systems

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Authors: Guru Prakash, Revanth Dugalam

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A road’s condition often deteriorates due to harsh loading such as overload due to trucks, and severe environmental conditions such as heavy rain, snow load, and cyclic loading. In absence of proper maintenance planning, this results in potholes, wide cracks, bumps, and increased roughness of roads. In this paper, a data-driven model will be developed to detect these damages using vibration and image signals. The key idea of the proposed methodology is that the road anomaly manifests in these signals, which can be detected by training a machine learning algorithm. The use of various machine learning techniques such as the support vector machine and Radom Forest method will be investigated. The proposed model will first be trained and tested with artificially simulated data, and the model architecture will be finalized by comparing the accuracies of various models. Once a model is fixed, the field study will be performed, and data will be collected. The field data will be used to validate the proposed model and to predict the future road’s health condition. The proposed will help to automate the road condition monitoring process, repair cost estimation, and maintenance planning process.

Keywords: SVM, data-driven, road health monitoring, pot-hole

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Authors: Babek Erdebilli

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The aim of this study is to develop and describe a new methodology for the Multi-Criteria Decision-Making (MCDM) problem using IFE (Elimination Et Choix Traduisant La Realite (ELECTRE) model. The proposed models enable Decision-Makers (DMs) on the assessment and use Intuitionistic Fuzzy Numbers (IFN). A numerical example is provided to demonstrate and clarify the proposed analysis procedure. Also, an empirical experiment is conducted to validation the effectiveness.

Keywords: multi-criteria decision-making, IFE, DM’s, fuzzy electre model

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Authors: Sangwoo Han, Saeed Khaleghi Rahimian, Ying Liu

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Vehicle electrification is gaining momentum, and many car manufacturers promise to deliver more electric vehicle (EV) models to consumers in the coming years. In controlling the battery pack, the battery management system (BMS) must maintain optimal battery performance while ensuring the safety of a battery pack. Tasks related to battery performance include determining state-of-charge (SOC), state-of-power (SOP), state-of-health (SOH), cell balancing, and battery charging. Safety related functions include making sure cells operate within specified, static and dynamic voltage window and temperature range, derating power, detecting faulty cells, and warning the user if necessary. The BMS often utilizes an RC circuit model to model a Li-ion cell because of its robustness and low computation cost among other benefits. Because an equivalent circuit model such as the RC model is not a physics-based model, it can never be a prognostic model to predict battery state-of-health and avoid any safety risk even before it occurs. A physics-based Li-ion cell model, on the other hand, is more capable at the expense of computation cost. To avoid the high computation cost associated with a full-order model, many researchers have demonstrated the use of a single particle model (SPM) for BMS applications. One drawback associated with the single particle modeling approach is that it forces to use the average current density in the calculation. The SPM would be appropriate for simulating drive cycles where there is insufficient time to develop a significant current distribution within an electrode. However, under a continuous or high-pulse electrical load, the model may fail to predict cell voltage or Li⁺ plating potential. To overcome this issue, a multi-particle reduced-order model is proposed here. The use of multiple particles combined with either linear or nonlinear charge-transfer reaction kinetics enables to capture current density distribution within an electrode under any type of electrical load. To maintain computational complexity like that of an SPM, governing equations are solved sequentially to minimize iterative solving processes. Furthermore, the model is validated against a full-order model implemented in COMSOL Multiphysics.

Keywords: battery management system, physics-based li-ion cell model, reduced-order model, single-particle and multi-particle model

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Authors: Chong Zhang, Mu-Xuan Tao

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In several earthquakes, numerous reinforced concrete (RC) frames subjected to seismic excitation demonstrated a collapse pattern characterized by column hinges, though designed according to the Strong-Column-Weak-Beam (S-C-W-B) criteria. The effect of biaxial seismic excitation on the disparity between design and actual performance is carefully investigated in this article. First, a modified load contour method is proposed to derive a closed-form equation of biaxial bending moment strength, which is verified by numerical and experimental tests. Afterwards, a group of time history analyses of a simple frame modeled by fiber beam-column elements subjected to biaxial seismic excitation are conducted to verify that the current S-C-W-B criteria are not adequate to prevent the occurrence of column hinges. A biaxial over-strength factor is developed based on the proposed equation, and the reinforcement of columns is appropriately amplified with this factor to prevent the occurrence of column hinges under biaxial excitation, which is proved to be effective by another group of time history analyses.

Keywords: biaxial bending moment capacity, biaxial seismic excitation, fiber beam model, load contour method, strong-column-weak-beam

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Authors: W. H. Wan Zakiyatussariroh, A. A. Nasuhar, W. Y. Wan Fairos, Z. A. Nazatul Shahreen

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Forecasting dengue incidence in a population can provide useful information to facilitate the planning of the public health intervention. Many studies on dengue cases in Malaysia were conducted but are limited in modeling the outbreak and forecasting incidence. This article attempts to propose the most appropriate time series model to explain the behavior of dengue incidence in Malaysia for the purpose of forecasting future dengue outbreaks. Several seasonal auto-regressive integrated moving average (SARIMA) models were developed to model Malaysia’s number of dengue incidence on weekly data collected from January 2001 to December 2011. SARIMA (2,1,1)(1,1,1)52 model was found to be the most suitable model for Malaysia’s dengue incidence with the least value of Akaike information criteria (AIC) and Bayesian information criteria (BIC) for in-sample fitting. The models further evaluate out-sample forecast accuracy using four different accuracy measures. The results indicate that SARIMA (2,1,1)(1,1,1)52 performed well for both in-sample fitting and out-sample evaluation.

Keywords: time series modeling, Box-Jenkins, SARIMA, forecasting

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Authors: S. K. Balashanmugam, Padmavati Manchikanti, S. R. Subramanian

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The question of legal liability over injury arising out of the import and the introduction of GM food emerges as a crucial issue confronting to promote GM food and its derivatives. There is a greater possibility of commercialized GM food from the exporting country to enter importing country where status of approval shall not be same. This necessitates the importance of fixing a liability mechanism to discuss the damage, if any, occurs at the level of transboundary movement or at the market. There was a widespread consensus to develop the Cartagena Protocol on Biosafety and to give for a dedicated regime on liability and redress in the form of Nagoya Kuala Lumpur Supplementary Protocol on the Liability and Redress (‘N-KL Protocol’) at the international context. The national legal frameworks based on this protocol are not adequately established in the prevailing food legislations of the developing countries. The developing economy like India is willing to import GM food and its derivatives after the successful commercialization of Bt Cotton in 2002. As a party to the N-KL Protocol, it is indispensable for India to formulate a legal framework and to discuss safety, liability, and regulatory issues surrounding GM foods in conformity to the provisions of the Protocol. The liability mechanism is also important in the case where the risk assessment and risk management is still in implementing stage. Moreover, the country is facing GM infiltration issues with its neighbors Bangladesh. As a precautionary approach, there is a need to formulate rules and procedure of legal liability to discuss any kind of damage occurs at transboundary trade. In this context, the proposed work will attempt to analyze the liability regime in the existing Food Safety and Standards Act, 2006 from the applicability and domestic compliance and to suggest legal and policy options for regulatory authorities.

Keywords: commercialization, food safety, FSSAI, genetically modified foods, India, liability

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

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

Keywords: Acoustic Doppler Current Profiler, construction around coral reefs, dredging, port construction, sediment transport monitoring,

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Authors: Andrés I. Ávila, Patricia Aros, César San Martín, Elizabeth Kehr, Yovana Leal

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Planning models for fresh products is a very useful tool for improving the net profits. To get an efficient supply chain model, several functions should be considered to get a complete simulation of several operational units. We consider a linear programming model to help farmers to decide if it is convenient to choose what area should be planted for three kinds of export fruits considering their future investment. We consider area, investment, water, productivity minimal unit, and harvest restrictions to develop a monthly based model to compute the average income in five years. Also, conditions on the field as area, water availability, and initial investment are required. Using the Chilean costs and dollar-peso exchange rate, we can simulate several scenarios to understand the possible risks associated to this market. Also, this tool help to support decisions for government and individual farmers.

Keywords: mixed integer problem, fresh fruit production, support decision model, agricultural and biosystems engineering

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Authors: Okyu Kwon, Dongho Kang, Byungsik Kim, Seungkwon Jung

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We propose a technique to measure the impact of loss of traffic function in a particular area to surrounding areas. The proposed method is applied to the city of Seoul, which is the capital of South Korea, with a population of about ten million. Based on the actual road network in Seoul, we construct an abstract road network between 1kmx1km grid cells. The link weight of the abstract road network is re-adjusted considering traffic volume measured at several survey points. On the modified abstract road network, we evaluate the traffic vulnerability by calculating a network measure of betweenness centrality (BC) for every single grid cells. This study analyzes traffic impacts caused by road dysfunction due to heavy rainfall in urban areas. We could see the change of the BC value in all other grid cells by calculating the BC value once again when the specific grid cell lost its traffic function, that is, when the node disappeared on the grid-based road network. The results show that it is appropriate to use the sum of the BC variation of other cells as the influence index of each lattice cell on traffic. This research was supported by a grant (2017-MOIS31-004) from Fundamental Technology Development Program for Extreme Disaster Response funded by Korean Ministry of Interior and Safety (MOIS).

Keywords: vulnerability, road network, beweenness centrality, heavy rainfall, road impact

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Authors: Edamana Krishnan, Khalil Al-Ghafri

Abstract:

In this paper, modified K(n,n) and K(n+1,n+1) equations have been solved using mapping methods which give a variety of solutions in terms of Jacobi elliptic functions. The solutions when m approaches 0 and 1, with m as the modulus of the JEFs have also been deduced. The role of constraint conditions has been discussed.

Keywords: travelling wave solutions, solitary wave solutions, compactons, Jacobi elliptic functions, mapping methods

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Authors: Sujit K. Basak

Abstract:

The aim of this paper is to analyze the impact of literature review software on researchers. The aim of this study was achieved by analyzing models in terms of perceived usefulness, perceived ease of use, and acceptance level. Collected data was analyzed using WarpPLS 4.0 software. This study used two theoretical frameworks namely Technology Acceptance Model and the Training Needs Assessment Model. The study was experimental and was conducted at a public university in South Africa. The results of the study showed that acceptance level has a high impact on research workload and productivity followed by perceived usefulness and perceived ease of use.

Keywords: technology acceptance model, training needs assessment model, literature review software, research productivity

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Authors: Yin-Yee Leong, Jack C. Yue, Hsin-Chung Wang

Abstract:

Human longevity has been experiencing its largest increase since the end of World War II, and modeling the mortality rates is therefore often the focus of many studies. Among all mortality models, the Lee–Carter model is the most popular approach since it is fairly easy to use and has good accuracy in predicting mortality rates (e.g., for Japan and the USA). However, empirical studies from several countries have shown that the age parameters of the Lee–Carter model are not constant in time. Many modifications of the Lee–Carter model have been proposed to deal with this problem, including adding an extra cohort effect and adding another period effect. In this study, we propose a spatial modification and use clusters to explain why the age parameters of the Lee–Carter model are not constant. In spatial analysis, clusters are areas with unusually high or low mortality rates than their neighbors, where the “location” of mortality rates is measured by age and time, that is, a 2-dimensional coordinate. We use a popular cluster detection method—Spatial scan statistics, a local statistical test based on the likelihood ratio test to evaluate where there are locations with mortality rates that cannot be described well by the Lee–Carter model. We first use computer simulation to demonstrate that the cluster effect is a possible source causing the problem of the age parameters not being constant. Next, we show that adding the cluster effect can solve the non-constant problem. We also apply the proposed approach to mortality data from Japan, France, the USA, and Taiwan. The empirical results show that our approach has better-fitting results and smaller mean absolute percentage errors than the Lee–Carter model.

Keywords: mortality improvement, Lee–Carter model, spatial statistics, cluster detection

Procedia PDF Downloads 171