Search results for: Box and Jenkins Models

Authors: H. Jung, N. Kim, B. Kang, J. Choe

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History matching is a crucial procedure for predicting reservoir performances and making future decisions. However, it is difficult due to uncertainties of initial reservoir models. Therefore, it is important to have reliable initial models for successful history matching of highly heterogeneous reservoirs such as channel reservoirs. In this paper, we proposed a novel scheme for regenerating geological models using support vector machine (SVM) and principal component analysis (PCA). First, we perform PCA for figuring out main geological characteristics of models. Through the procedure, permeability values of each model are transformed to new parameters by principal components, which have eigenvalues of large magnitude. Secondly, the parameters are projected into two-dimensional plane by multi-dimensional scaling (MDS) based on Euclidean distances. Finally, we train an SVM classifier using 20% models which show the most similar or dissimilar well oil production rates (WOPR) with the true values (10% for each). Then, the other 80% models are classified by trained SVM. We select models on side of low WOPR errors. One hundred channel reservoir models are initially generated by single normal equation simulation. By repeating the classification process, we can select models which have similar geological trend with the true reservoir model. The average field of the selected models is utilized as a probability map for regeneration. Newly generated models can preserve correct channel features and exclude wrong geological properties maintaining suitable uncertainty ranges. History matching with the initial models cannot provide trustworthy results. It fails to find out correct geological features of the true model. However, history matching with the regenerated ensemble offers reliable characterization results by figuring out proper channel trend. Furthermore, it gives dependable prediction of future performances with reduced uncertainties. We propose a novel classification scheme which integrates PCA, MDS, and SVM for regenerating reservoir models. The scheme can easily sort out reliable models which have similar channel trend with the reference in lowered dimension space.

Keywords: history matching, principal component analysis, reservoir modelling, support vector machine

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Authors: Nurudeen Oluwasola Lasisi, Abdulkareem Afolabi Ibrahim

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Newcastle disease is an infection of domestic poultry and other bird species with virulent Newcastle disease virus (NDV). In this paper, we study the dynamics of modeling the Newcastle disease virus (NDV) using a novel quarantine-adjusted incidence. We do a comparison of Vaccination, linear incident rate, and novel quarantine adjusted incident rate in the models. The dynamics of the models yield disease free and endemic equilibrium states. The effective reproduction numbers of the models are computed in order to measure the relative impact for the individual bird or combined intervention for effective disease control. We showed the local and global stability of endemic equilibrium states of the models, and we found that stability of endemic equilibrium states of models are globally asymptotically stable if the effective reproduction numbers of the models equations are greater than a unit.

Keywords: effective reproduction number, endemic state, mathematical model, Newcastle disease virus, novel quarantine-adjusted incidence, stability analysis

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Authors: Ahmed El-Banbi

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Several PVT models exist to represent how PVT properties are handled in sub-surface and surface engineering calculations for oil and gas production. The most commonly used models include black oil, modified black oil (MBO), and compositional models. These models are used in calculations that allow engineers to optimize and forecast well and reservoir performance (e.g., reservoir simulation calculations, material balance, nodal analysis, surface facilities, etc.). The choice of which model is dependent on fluid type and the production process (e.g., depletion, water injection, gas injection, etc.). Based on close to 2,000 reservoir fluid samples collected from different basins and locations, this paper presents some conclusions on the occurrence of reservoir fluids. It also reviews the common methods used to classify reservoir fluid types. Based on new criteria related to the production behavior of different fluids and economic considerations, an updated classification of reservoir fluid types is presented in the paper. Recommendations on the use of different PVT models to simulate the behavior of different reservoir fluid types are discussed. Each PVT model requirement is highlighted. Available methods for the calculation of PVT properties from each model are also discussed. Practical recommendations and tips on how to control the calculations to achieve the most accurate results are given.

Keywords: PVT models, fluid types, PVT properties, fluids classification

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Authors: Meng-Fei Cheng, Wei-Lun Chen, Han-Chang Ma, Chi-Che Tsai

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Recent K–12 Taiwan Science Education Curriculum Guideline emphasize the essential role of modeling curriculum in science learning; however, few modeling curricula have been designed and adopted in current science teaching. Therefore, this study aims to develop modeling curriculum on electric circuits to investigate any learning difficulties students have with modeling curriculum and further enhance modeling teaching. This study was conducted with 44 10th-grade students in Central Taiwan. Data collection included a students’ understanding of models in science (SUMS) survey that explored the students' epistemology of scientific models and modeling and a complex circuit problem to investigate the students’ modeling abilities. Data analysis included the following: (1) Paired sample t-tests were used to examine the improvement of students’ modeling abilities and conceptual understanding before and after the curriculum was taught. (2) Paired sample t-tests were also utilized to determine the students’ modeling abilities before and after the modeling activities, and a Pearson correlation was used to understand the relationship between students’ modeling abilities during the activities and on the posttest. (3) ANOVA analysis was used during different stages of the modeling curriculum to investigate the differences between the students’ who developed microscopic models and macroscopic models after the modeling curriculum was taught. (4) Independent sample t-tests were employed to determine whether the students who changed their models had significantly different understandings of scientific models than the students who did not change their models. The results revealed the following: (1) After the modeling curriculum was taught, the students had made significant progress in both their understanding of the science concept and their modeling abilities. In terms of science concepts, this modeling curriculum helped the students overcome the misconception that electric currents reduce after flowing through light bulbs. In terms of modeling abilities, this modeling curriculum helped students employ macroscopic or microscopic models to explain their observed phenomena. (2) Encouraging the students to explain scientific phenomena in different context prompts during the modeling process allowed them to convert their models to microscopic models, but it did not help them continuously employ microscopic models throughout the whole curriculum. The students finally consistently employed microscopic models when they had help visualizing the microscopic models. (3) During the modeling process, the students who revised their own models better understood that models can be changed than the students who did not revise their own models. Also, the students who revised their models to explain different scientific phenomena tended to regard models as explanatory tools. In short, this study explored different strategies to facilitate students’ modeling processes as well as their difficulties with the modeling process. The findings can be used to design and teach modeling curricula and help students enhance their modeling abilities.

Keywords: electric circuits, modeling curriculum, science learning, scientific model

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Authors: R. Hellmuth

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The method of factory planning has changed a lot, especially when it is about planning the factory building itself. Factory planning has the task of designing products, plants, processes, organization, areas, and the building of a factory. Regular restructuring is becoming more important in order to maintain the competitiveness of a factory. Restrictions in new areas, shorter life cycles of product and production technology as well as a VUCA world (Volatility, Uncertainty, Complexity and Ambiguity) lead to more frequent restructuring measures within a factory. A digital factory model is the planning basis for rebuilding measures and becomes an indispensable tool. Furthermore, digital building models are increasingly being used in factories to support facility management and manufacturing processes. The main research question of this paper is, therefore: What kind of digital factory model is suitable for the different areas of application during the operation of a factory? First, different types of digital factory models are investigated, and their properties and usabilities for use cases are analysed. Within the scope of investigation are point cloud models, building information models, photogrammetry models, and these enriched with sensor data are examined. It is investigated which digital models allow a simple integration of sensor data and where the differences are. Subsequently, possible application areas of digital factory models are determined by means of a survey and the respective digital factory models are assigned to the application areas. Finally, an application case from maintenance is selected and implemented with the help of the appropriate digital factory model. It is shown how a completely digitalized maintenance process can be supported by a digital factory model by providing information. Among other purposes, the digital factory model is used for indoor navigation, information provision, and display of sensor data. In summary, the paper shows a structuring of digital factory models that concentrates on the geometric representation of a factory building and its technical facilities. A practical application case is shown and implemented. Thus, the systematic selection of digital factory models with the corresponding application cases is evaluated.

Keywords: building information modeling, digital factory model, factory planning, maintenance

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Authors: Yoko Yamada, Chizumi Yamada

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Narrative psychology is based on the dialogical relationship between self and other. The dialogue can consist of divided, competitive, or opposite communication between self and other. We constructed models of coexistent dialogue in which self and other were positioned side by side and communicated sympathetically. We propose new mediation models for narrative relationships. The mediation models are based on triadic relationships that incorporate a medium or a mediator along with self and other. We constructed three types of mediation model. In the first type, called the “Joint Attention Model”, self and other are positioned side by side and share attention with the medium. In the second type, the “Triangle Model”, an agent mediates between self and other. In the third type, the “Caring Model”, a caregiver stands beside the communication between self and other. We apply the three models to the illness narratives of medical professionals and patients. As these groups have different views and experiences of disease or illness, triadic mediation facilitates the ability to see things from the other person’s perspective and to bridge differences in people’s experiences and feelings. These models would be useful for medical education in various situations, such as in considering the relationships between senior and junior doctors and between old and young patients.

Keywords: illness narrative, mediation, psychology, model, medical education

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Authors: A. A. A. Aboalnour, Ahmed M. Amasaib, Mohammed-Almujtaba A. Mohammed-Farah, Abdelhakam, A. Noreldien

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This paper presents a design and study of Parabolic Trough Solar Collector (PTC). Mathematical models were used in this work to find the direct and reflected solar radiation from the air layer on the surface of the earth per hour based on the total daily solar radiation on a horizontal surface. Also mathematical models had been used to calculate the radiation of the tilted surfaces. Most of the ingredients used in this project as previews data required on several solar energy applications, thermal simulation, and solar power systems. In addition, mathematical models had been used to study the flow of the fluid inside the tube (receiver), and study the effect of direct and reflected solar radiation on the pressure, temperature, speed, kinetic energy and forces of fluid inside the tube. Finally, the mathematical models had been used to study the (PTC) performances and estimate its thermal efficiency.

Keywords: CFD, experimental, mathematical models, parabolic trough, radiation

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Authors: Alireza Vafaei Sadr, Vida Abedi, Jiang Li, Ramin Zand

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Missing data is a common challenge in medical research and can lead to biased or incomplete results. When the data bias leaks into models, it further exacerbates health disparities; biased algorithms can lead to misclassification and reduced resource allocation and monitoring as part of prevention strategies for certain minorities and vulnerable segments of patient populations, which in turn further reduce data footprint from the same population – thus, a vicious cycle. This study compares the performance of six imputation techniques grouped into Linear and Non-Linear models on two different realworld electronic health records (EHRs) datasets, representing 17864 patient records. The mean absolute percentage error (MAPE) and root mean squared error (RMSE) are used as performance metrics, and the results show that the Linear models outperformed the Non-Linear models in terms of both metrics. These results suggest that sometimes Linear models might be an optimal choice for imputation in laboratory variables in terms of imputation efficiency and uncertainty of predicted values.

Keywords: EHR, machine learning, imputation, laboratory variables, algorithmic bias

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Authors: Julio Macedo

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Several methodologies are now available to conceive the improvements of a process so that it becomes competitive as for example total quality, process reengineering, six sigma, define measure analysis improvement control method. These improvements are of different nature and can be external to the process represented by an optimization model or a discrete simulation model. In addition, the process stakeholders are several and have different desired performances for the process. Hence, the methodologies above do not have a tool to aid in the conception of the required improvements. In order to fill this void we suggest the use of intelligent reference models. A reference model is a set of qualitative differential equations and an objective function that minimizes the gap between the current and the desired performance indexes of the process. The reference models are intelligent so when they receive the current state of the problematic process and the desired performance indexes they generate the required improvements for the problematic process. The reference models are fuzzy cognitive maps added with an objective function and trained using the improvements implemented by the high performance firms. Experiments done in a set of students show the reference models allow them to conceive more improvements than students that do not use these models.

Keywords: continuous improvement, fuzzy cognitive maps, process competitiveness, qualitative simulation, system dynamics

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Authors: Suriya

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Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.

Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar

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Authors: Y. N. Phang, E. F. Loh

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Many researchers have suggested the use of zero inflated Poisson (ZIP) and zero inflated negative binomial (ZINB) models in modeling over-dispersed medical count data with extra variations caused by extra zeros and unobserved heterogeneity. The studies indicate that ZIP and ZINB always provide better fit than using the normal Poisson and negative binomial models in modeling over-dispersed medical count data. In this study, we proposed the use of Zero Inflated Inverse Trinomial (ZIIT), Zero Inflated Poisson Inverse Gaussian (ZIPIG) and zero inflated strict arcsine models in modeling over-dispersed medical count data. These proposed models are not widely used by many researchers especially in the medical field. The results show that these three suggested models can serve as alternative models in modeling over-dispersed medical count data. This is supported by the application of these suggested models to a real life medical data set. Inverse trinomial, Poisson inverse Gaussian, and strict arcsine are discrete distributions with cubic variance function of mean. Therefore, ZIIT, ZIPIG and ZISA are able to accommodate data with excess zeros and very heavy tailed. They are recommended to be used in modeling over-dispersed medical count data when ZIP and ZINB are inadequate.

Keywords: zero inflated, inverse trinomial distribution, Poisson inverse Gaussian distribution, strict arcsine distribution, Pearson’s goodness of fit

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Authors: Jihye Jeon

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This paper analyzes the conceptual framework of three statistical methods, multiple regression, path analysis, and structural equation models. When establishing research model of the statistical modeling of complex social phenomenon, it is important to know the strengths and limitations of three statistical models. This study explored the character, strength, and limitation of each modeling and suggested some strategies for accurate explaining or predicting the causal relationships among variables. Especially, on the studying of depression or mental health, the common mistakes of research modeling were discussed.

Keywords: multiple regression, path analysis, structural equation models, statistical modeling, social and psychological phenomenon

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Authors: Flavio Cordeiro Fontanella, Asla Medeiros e Sá, Moacyr Alvim Horta Barbosa da Silva

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In the present work, we analyse the performance of football results forecasting models. In order to do so, we have performed the data collection from eight different forecasting models during the 2021 Brazilian football season. First, we guide the analysis through visual representations of the data, designed to highlight the most prominent features and enhance the interpretation of differences and similarities between the models. We propose using a 2-simplex triangle to investigate visual patterns from the results forecasting models. Next, we compute the expected points for every team playing in the championship and compare them to the final league standings, revealing interesting contrasts between actual to expected performances. Then, we evaluate forecasts’ accuracy using the Ranked Probability Score (RPS); models comparison accounts for tiny scale differences that may become consistent in time. Finally, we observe that the Wisdom of Crowds principle can be appropriately applied in the context, driving into a discussion of results forecasts usage in practice. This paper’s primary goal is to encourage football forecasts’ performance discussion. We hope to accomplish it by presenting appropriate criteria and easy-to-understand visual representations that can point out the relevant factors of the subject.

Keywords: accuracy evaluation, Brazilian championship, football results forecasts, forecasting models, visual analysis

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Authors: M. I. Youssef, A. E. Emam, M. Abd Elghany

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The performance of wireless communication systems is affected mainly by the environment of its associated channel, which is characterized by dynamic and unpredictable behavior. In this paper, different statistical earth-satellite channel models are studied with emphasize on two main models, first is the Rice-Log normal model, due to its representation for the environment including shadowing and multi-path components that affect the propagated signal along its path, and a three-state model that take into account different fading conditions (clear area, moderate shadow and heavy shadowing). The provided models are based on AWGN, Rician, Rayleigh, and log-normal distributions were their Probability Density Functions (PDFs) are presented. The transmission system Bit Error Rate (BER), Peak-Average-Power Ratio (PAPR), and the channel capacity vs. fading models are measured and analyzed. These simulations are implemented using MATLAB tool, and the results had shown the performance of transmission system over different channel models.

Keywords: fading channels, MIMO communication, RNS scheme, statistical modeling

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Authors: Debora Gottardello, Mireia Valverde Aparicio, Juan Llopis Taverner

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Globalization has given rise to a great diversity in the composition of people in organizations. Diversity management is therefore key to create growth in today’s competitive global marketplace. This work develops a literature review related to the existing models for managing diversity covering the period from 1980 until 2014. Furthermore, it identifies limitations in previous models. More specifically, the literature review reveals that there is a lack of information about how these models can be adapted from the headquarters to the subsidiaries. Therefore, the contribution of this paper is to suggest how the models should be adapted when they are directed to host countries. Our aim is to highlight the limitations of the developed models with regards to the translation of the diversity management practices to the subsidiaries. Accordingly, a model that will enable MNCs to ensure a global strategy is suggested. Taking advantage of the potential incorporated in a culturally diverse work team should be at the top of every international company’s aims. Executives from headquarters need to use different attitudes when transferring diversity practices towards their subsidiaries. Further studies should reassess local practices of diversity management to find out how this universal management model is translated.

Keywords: culture diversity, diversity management, human resources management, MNCs, subsidiaries, workforce diversity

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Authors: Aldin Justin Sundararaj, Austin Lord Tennyson, Divya Jose, A. N. Subash

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Blast waves are generated due to the explosions of high energy materials. An explosion yielding a blast wave has the potential to cause severe damage to buildings and its personnel. In order to understand the physics of effects of blast pressure on buildings, studies in the shock tube on generic configurations are carried out at various pressures on discrete models. The strength of shock wave is systematically varied by using different driver gases and diaphragm thickness. The basic material of the diaphragm is Aluminum. To simulate the effect of shock waves on discrete models a shock tube was used. Generic models selected for this study are suitably scaled cylinder, cone and cubical blocks. The experiments were carried out with 2mm diaphragm with burst pressure ranging from 28 to 31 bar. Numerical analysis was carried out over these discrete models. A 3D model of shock-tube with different discrete models inside the tube was used for CFD computation. It was found that cone has dissipated most of the shock pressure compared to cylinder and cubical block. The robustness and the accuracy of the numerical model were validation with the analytical and experimental data.

Keywords: shock wave, blast wave, discrete models, shock tube

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Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

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A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.

Keywords: big data, k-NN, machine learning, traffic speed prediction

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Authors: Pavan K. Rallabandi, Kailash C. Patidar

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In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence or pattern recognition/ classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata.

Keywords: hybrid systems, hidden markov models, recurrent neural networks, deterministic finite state automata

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Authors: Farrukh Javed, Krzysztof Podgórski

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We propose a new model that accounts for the asymmetric response of volatility to positive ('good news') and negative ('bad news') shocks in economic time series the so-called leverage effect. In the past, asymmetric powers of errors in the conditionally heteroskedastic models have been used to capture this effect. Our model is using the gamma difference representation of the generalized Laplace distributions that efficiently models the asymmetry. It has one additional natural parameter, the shape, that is used instead of power in the asymmetric power models to capture the strength of a long-lasting effect of shocks. Some fundamental properties of the model are provided including the formula for covariances and an explicit form for the conditional distribution of 'bad' and 'good' news processes given the past the property that is important for the statistical fitting of the model. Relevant features of volatility models are illustrated using S&P 500 historical data.

Keywords: heavy tails, volatility clustering, generalized asymmetric laplace distribution, leverage effect, conditional heteroskedasticity, asymmetric power volatility, GARCH models

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Authors: Minttu Laukkanen, Janne Huiskonen

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This study investigates the sharing economy business models as examples of the sustainable business models. The aim is to contribute to the limited literature on sharing economy in connection with sustainable business models by explaining sharing economy business models value capturing. Specifically, this research answers the following question: How business model choices affect captured sustainable value? A multiple case study approach is applied in this study. Twenty different successful sharing economy business models focusing on consumer business and covering four main areas, accommodation, mobility, food, and consumer goods, are selected for analysis. The secondary data available on companies’ websites, previous research, reports, and other public documents are used. All twenty cases are analyzed through the sharing economy business model framework and sustainable value analysis framework using qualitative data analysis. This study represents general sharing economy business model value attributes and their specifications, i.e. sustainable value propositions for different stakeholders, and further explains the sustainability impacts of different sharing economy business models through captured and uncaptured value. In conclusion, this study represents how business model choices affect sustainable value capturing through eight business model attributes identified in this study. This paper contributes to the research on sustainable business models and sharing economy by examining how business model choices affect captured sustainable value. This study highlights the importance of careful business model and sustainability impacts analyses including the triple bottom line, multiple stakeholders and value captured and uncaptured perspectives as well as sustainability trade-offs. It is not self-evident that sharing economy business models advance sustainability, and business model choices does matter.

Keywords: sharing economy, sustainable business model innovation, sustainable value, value capturing

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Authors: Manoj Reghu, Prabhu Rajagopal, C. V. Krishnamurthy, Krishnan Balasubramaniam

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A thorough understanding of guided ultrasonic wave behavior in structures is essential for the application of existing Non Destructive Evaluation (NDE) technologies, as well as for the development of new methods. However, the analysis of guided wave phenomena is challenging because of their complex dispersive and multimodal nature. Although numerical solution procedures have proven to be very useful in this regard, the increasing complexity of features and defects to be considered, as well as the desire to improve the accuracy of inspection often imposes a large computational cost. Hybrid models that combine numerical solutions for wave scattering with faster alternative methods for wave propagation have long been considered as a solution to this problem. However usually such models require modification of the base code of the solution procedure. Here we aim to develop Generic Hybrid models that can be directly applied to any two different solution procedures. With this goal in mind, a Numerical Hybrid model and an Analytical-Numerical Hybrid model has been developed. The concept and implementation of these Hybrid models are discussed in this paper.

Keywords: guided ultrasonic waves, Finite Element Method (FEM), Hybrid model

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Authors: Ibrahim Elnour Abdelrahman Eltayeb

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Computational modelling is a method used to investigate joint forces during a movement. It can get high accuracy in the joint forces via subject-specific models. However, the construction of subject-specific models remains time-consuming and expensive. The purpose of this paper was to identify what alterations we can make to generic computational models to get a better estimation of the joint forces. It appraised the impact of these alterations on the accuracy of the estimated joint forces. It found different strategies of alterations: joint model, muscle model, and an optimisation problem. All these alterations affected joint contact force accuracy, so showing the potential for improving the model predictions without involving costly and time-consuming medical images.

Keywords: joint force, joint model, optimisation problem, validation

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Authors: Leah Wright, Trevor Townsend

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Mode choice models are crucial instruments in the analysis of travel behaviour. These models show the relationship between an individual’s choice of transportation mode for a given O-D pair and the individual’s socioeconomic characteristics such as household size and income level, age and/or gender, and the features of the transportation system. The most popular functional forms of these models are based on Utility-Based Choice Theory, which addresses the uncertainty in the decision-making process with the use of an error term. However, with the development of artificial intelligence, many researchers have started to take a different approach to travel demand modelling. In recent times, researchers have looked at using neural networks, fuzzy logic and rough set theory to develop improved mode choice formulas. The concept of cloud theory has recently been introduced to model decision-making under uncertainty. Unlike the previously mentioned theories, cloud theory recognises a relationship between randomness and fuzziness, two of the most common types of uncertainty. This research aims to investigate the use of cloud theory in mode choice models. This paper highlights the conceptual framework of the mode choice model using cloud theory. Merging decision-making under uncertainty and mode choice models is state of the art. The cloud theory model is expected to address the issues and concerns with the nested logit and improve the design of mode choice models and their use in travel demand.

Keywords: Cloud theory, decision-making, mode choice models, travel behaviour, uncertainty

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Authors: H. Zormati, J. Chebil, J. Bel Hadj Tahar

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Next generation wireless transmission technology (5G) is expected to support the development of channel models for higher frequency bands, so clarification of high frequency bands is the most important issue in radio propagation research for 5G, multiple urban microcellular measurements have been carried out at 60 GHz. In this paper, the collected data is uniformly analyzed with focus on the path loss (PL), the objective is to compare simulation results of some studied channel models with the purpose of testing the performance of each one.

Keywords: 5G, channel model, 60GHz channel, millimeter-wave, urban microcell

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Authors: Mai A. Ibrahim, Mohammed El-Beltagy, Motaz Khorshid

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Emerging Markets return distributions have shown significance departure from normality were they are characterized by fatter tails relative to the normal distribution and exhibit levels of skewness and kurtosis that constitute a significant departure from normality. Therefore, the classical Markowitz Mean-Variance is not applicable for emerging markets since it assumes normally-distributed returns (with zero skewness and kurtosis) and a quadratic utility function. Moreover, the Markowitz mean-variance analysis can be used in cases of moderate non-normality and it still provides a good approximation of the expected utility, but it may be ineffective under large departure from normality. Higher moments models and median models have been suggested in the literature for asset allocation in this case. Higher moments models have been introduced to account for the insufficiency of the description of a portfolio by only its first two moments while the median model has been introduced as a robust statistic which is less affected by outliers than the mean. Tail risk measures such as Value-at Risk (VaR) and Conditional Value-at-Risk (CVaR) have been introduced instead of Variance to capture the effect of risk. In this research, higher moment models including the Mean-Variance-Skewness (MVS) and Mean-Variance-Skewness-Kurtosis (MVSK) are formulated as single-objective non-linear programming problems (NLP) and median models including the Median-Value at Risk (MedVaR) and Median-Mean Absolute Deviation (MedMAD) are formulated as a single-objective mixed-integer linear programming (MILP) problems. The higher moment models and median models are compared to some benchmark portfolios and tested on real financial data in the Egyptian main Index EGX30. The results show that all the median models outperform the higher moment models were they provide higher final wealth for the investor over the entire period of study. In addition, the results have confirmed the inapplicability of the classical Markowitz Mean-Variance to the Egyptian stock market as it resulted in very low realized profits.

Keywords: Egyptian stock exchange, emerging markets, higher moment models, median models, mixed-integer linear programming, non-linear programming

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Authors: Celina Pestano-Gabino, Concepcion Gonzalez-Concepcion, M. Candelaria Gil-Fariña

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Some estimation methods for VARMA models, and Multivariate Time Series Models in general, rely on the use of a Hankel matrix. It is known that if the data sample is populous enough and the dimension of the Hankel matrix is unnecessarily large, this may result in an unnecessary number of computations as well as in numerical problems. In this sense, the aim of this paper is two-fold. First, we provide some theoretical results for these matrices which translate into a lower dimension for the matrices normally used in the algorithms. This contribution thus serves to improve those methods from a numerical and, presumably, statistical point of view. Second, we have chosen an estimation algorithm to illustrate in practice our improvements. The results we obtained in a simulation of VARMA models show that an increase in the size of the Hankel matrix beyond the theoretical bound proposed as valid does not necessarily lead to improved practical results. Therefore, for future research, we propose conducting similar studies using any of the linear system estimation methods that depend on Hankel matrices.

Keywords: covariances Hankel matrices, Kronecker indices, system identification, VARMA models

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Authors: I. Sagaama, A. Kechiche, W. Trojet, F. Kamoun

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Replacing combustion engine vehicles by electric vehicles (EVs) is a major step in recent years due to their potential benefits. Battery autonomy and charging processes are still a big issue for that kind of vehicles. Therefore, reducing the energy consumption of electric vehicles becomes a necessity. Many researches target introducing recent information and communication technologies in EVs in order to propose reducing energy consumption services. Evaluation of realistic scenarios is a big challenge nowadays. In this paper, we will elaborate a state of the art of different proposed energy consumption models in the literature, then we will present a comparative study of these models, finally, we will extend previous works in order to propose an accurate and realistic energy model for calculating instantaneous power consumption of electric vehicles.

Keywords: electric vehicle, vehicular networks, energy models, traffic simulation

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Authors: Julio Manuel De Luis Ruiz, Javier Sedano Cibrián, RubéN Pérez Álvarez, Raúl Pereda García, Felipe Piña García

Abstract:

Nowadays it is common to resort to aerial photography to carry out the prospection and/or exploration of archaeological sites. In this sense, the classic 3D models are being applied to investigate the direction towards which the generally subterranean structures of an archaeological site may continue and therefore, to help in making the decisions that define the location of new excavations. In recent years, Unmanned Aerial Vehicles (UAVs) have been applied as the vehicles that carry the sensor. This implies certain advantages, such as the possibility of including low-cost sensors, given that these vehicles can carry the sensor at relatively low altitudes. Due to this, low-cost dual sensors have recently begun to be used. This new equipment can collaborate with classic Digital Elevation Models (DEMs) in the exploration of archaeological sites, but this entails the need for a methodological setting to optimise the acquisition, processing and exploitation of the information provided by low-cost dual sensors. This research focuses on the design of an appropriate workflow to obtain 3D models with low-cost sensors carried on UAVs, both in the RGB and thermal domains. All the foregoing has been applied to the archaeological site of Juliobriga, located in Cantabria (Spain).

Keywords: process optimization, RGB models, thermal models, , UAV, workflow

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Authors: Rabi Mouhcine, Amrouch Mustapha, Mahani Zouhir, Mammass Driss

Abstract:

In this paper, we present a system for offline recognition cursive Arabic handwritten text based on Hidden Markov Models (HMMs). The system is analytical without explicit segmentation used embedded training to perform and enhance the character models. Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution characteristics in the word image. These features are modelled using hidden Markov models and trained by embedded training. The experiments on images of the benchmark IFN/ENIT database show that the proposed system improves recognition.

Keywords: recognition, handwriting, Arabic text, HMMs, embedded training

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Authors: Arcady Ponosov

Abstract:

Many well-known age-structured population models are derived from the celebrated McKendrick-von Foerster equation (MFE), also called the biological conservation law. A similar technique is suggested for the stochastically perturbed MFE. This technique is shown to produce stochastic versions of the deterministic population models, which appear to be very different from those one can construct by simply appending additive stochasticity to deterministic equations. In particular, it is shown that stochastic Nicholson’s blowflies model should contain both additive and multiplicative stochastic noises. The suggested transformation technique is similar to that used in the deterministic case. The difference is hidden in the formulas for the exact solutions of the simplified boundary value problem for the stochastically perturbed MFE. The analysis is also based on the theory of stochastic delay differential equations.

Keywords: boundary value problems, population models, stochastic delay differential equations, stochastic partial differential equation

Procedia PDF Downloads 255