Search results for: deterministic model

Authors: Dimitrios J. Dimitriou

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It is widely recognised that the assets portfolio development is helping to enhance economic growth, productivity and competitiveness. While numerous studies and reports certify the positive effect of investments in large infrastructure investments on the local economy, still, the methodology to estimate the contribution in economic development is a challenging issue for researchers and economists. The key question is how to estimate those economic impacts in each economic system. This paper provides a compact and applicable methodological framework providing quantitative results in terms of the overall jobs and income generated into the project life cycle. According to a deterministic mathematical approach, the key variables and the modelling framework are presented. The numerical case study highlights key results for a new motorway project in Greece, which is experienced economic stress for many years, providing the opportunity for comparisons with similar cases.

Keywords: quantitative modelling, economic impact, large transport infrastructure, economic assessment

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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: A. Y. Mukhtar, J. B. Munyakazi, R. Ouifki

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Over the past few decades, the unprecedented increase in mobility has raised considerable concern about the relationship between mobility and vector-borne diseases and malaria in particular. Thus, one can claim that human mobility is one of the contributing factors to the resurgence of malaria. To assess human mobility on malaria burden among hosts, we formulate a movement-based model on a network of patches. We then extend human multi-group SEIAR deterministic epidemic models into a system of stochastic differential equations (SDEs). Our quantitative stochastic model which is expressed in terms of average rates of movement between compartments is fitted to time-series data (weekly malaria data of 2011 for each patch) using the maximum likelihood approach. Using the metapopulation (multi-group) model, we compute and analyze the basic reproduction number. The result shows that human movement is sufficient to preserve malaria disease firmness in the patches with the low transmission. With these results, we concluded that the sensitivity of malaria to the human mobility is turning to be greatly important over the implications of future malaria control in South Sudan.

Keywords: basic reproduction number, malaria, maximum likelihood, movement, stochastic model

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Authors: Ana Clara Santos, Maria Manuela Portela, Bettina Schaefli

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This work assesses the performance of an analytical model framework to generate daily flow duration curves, FDCs, based on climatic characteristics of the catchments and on their streamflow recession coefficients. According to the analytical model framework, precipitation is considered to be a stochastic process, modeled as a marked Poisson process, and recession is considered to be deterministic, with parameters that can be computed based on different models. The analytical model framework was tested for three case studies with different hydrological regimes located in Switzerland: pluvial, snow-dominated and glacier. For that purpose, five time intervals were analyzed (the four meteorological seasons and the civil year) and two developments of the model were tested: one considering a linear recession model and the other adopting a nonlinear recession model. Those developments were combined with recession coefficients obtained from two different approaches: forward and inverse estimation. The performance of the analytical framework when considering forward parameter estimation is poor in comparison with the inverse estimation for both, linear and nonlinear models. For the pluvial catchment, the inverse estimation shows exceptional good results, especially for the nonlinear model, clearing suggesting that the model has the ability to describe FDCs. For the snow-dominated and glacier catchments the seasonal results are better than the annual ones suggesting that the model can describe streamflows in those conditions and that future efforts should focus on improving and combining seasonal curves instead of considering single annual ones.

Keywords: analytical streamflow distribution, stochastic process, linear and non-linear recession, hydrological modelling, daily discharges

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Authors: Hongbin Yang, Mingxue Liao, Changwen Zheng, Mengyao Kong, Chaohui Liu

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Microfacet-based reflection models are widely used to model light reflections for rough surfaces. Microfacet models have become the standard surface material building block for describing specular components with varying roughness; and yet, while they possess many desirable properties as well as produce convincing results, their design ignores important sources of scattering, which can cause a significant loss of energy. Specifically, they only simulate the single scattering on the microfacets and ignore the subsequent interactions. As the roughness increases, the interaction will become more and more important. So a multiple-scattering microfacet model based on specular V-cavity is presented for this important open problem. However, it spends much unnecessary rendering time because of setting the same number of scatterings for different roughness surfaces. In this paper, we design a geometric attenuation term G to compute the BRDF (Bidirectional reflection distribution function) of multiple scattering of rough surfaces. Moreover, we consider determining the number of scattering by deterministic heuristics for different roughness surfaces. As a result, our model produces a similar appearance of the objects with the state of the art model with significantly improved rendering efficiency. Finally, we derive a multiple scattering BRDF based on the original microfacet framework.

Keywords: bidirectional reflection distribution function, BRDF, geometric attenuation term, multiple scattering, V-cavity model

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Authors: Christian H. Girsang, M. Razi B. Mansoor, Noorizal N. Huang

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An offshore platform was installed in 1977 at about 260km offshore West Malaysia at the water depth of 73.6m. Twelve (12) piles were installed with four (4) are skirt piles. The piles have 1.219m outside diameter and wall thickness of 31mm and were driven to 109m below seabed. Deterministic analyses of the pile capacity under axial loading were conducted using the current API (American Petroleum Institute) method and the four (4) CPT-based methods: the ICP (Imperial College Pile)-method, the NGI (Norwegian Geotechnical Institute)-Method, the UWA (University of Western Australia)-method and the Fugro-method. A statistical analysis of the model uncertainty associated with each pile capacity method was performed. There were two (2) piles analysed: Pile 1 and piles other than Pile 1, where Pile 1 is the pile that was most affected by shallow gas problems. Using the mean estimate of soil properties, the five (5) methods used for deterministic estimation of axial pile capacity in compression predict an axial capacity from 28 to 42MN for Pile 1 and 32 to 49MN for piles other than Pile 1. These values refer to the static capacity shortly after pile installation. They do not include the effects of cyclic loading during the design storm or time after installation on the axial pile capacity. On average, the axial pile capacity is expected to have increased by about 40% because of ageing since the installation of the platform in 1977. On the other hand, the cyclic loading effects during the design storm may reduce the axial capacity of the piles by around 25%. The study concluded that all piles have sufficient safety factor when the pile aging and cyclic loading effect are considered, as all safety factors are above 2.0 for maximum operating and storm loads.

Keywords: axial capacity, cyclic loading, pile ageing, shallow gas

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Authors: Arnab Mondal, Sanjeev Kumar Sharma, Ranjit Kumar Upadhyay

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We study the spatiotemporal dynamics of a neuronal cable. The processes of one- dimensional (1D) and 2D diffusion are considered for a single variable, which is the membrane voltage, i.e., membrane voltage diffusively interacts for spatiotemporal pattern formalism. The recovery and other variables interact through the membrane voltage. A 3D Leech-Heart (LH) model is introduced to investigate the nonlinear responses of an excitable neuronal cable. The deterministic LH model shows different types of firing properties. We explore the parameter space of the uncoupled LH model and based on the bifurcation diagram, considering v_k2_ashift as a bifurcation parameter, we analyze the 1D diffusion dynamics in three regimes: bursting, regular spiking, and a quiescent state. Depending on parameters, it is shown that the diffusive system may generate regular and irregular bursting or spiking behavior. Further, it is explored a 2D diffusion acting on the membrane voltage, where different types of patterns can be observed. The results show that the LH neurons with different firing characteristics depending on the control parameters participate in a collective behavior of an information processing system that depends on the overall network.

Keywords: bifurcation, pattern formation, spatio-temporal dynamics, stability analysis

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Authors: Getachew Tilahun, Oluwole Makinde, David Malonza

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We propose and analyze a non-linear mathematical model for the transmission dynamics of pneumonia disease in a population of varying size. The deterministic compartmental model is studied using stability theory of differential equations. The effective reproduction number is obtained and also the local and global asymptotically stability conditions for the disease free and as well as for the endemic equilibria are established. The model exhibit a backward bifurcation and the sensitivity indices of the basic reproduction number to the key parameters are determined. Using Pontryagin’s maximum principle, the optimal control problem is formulated with three control strategies; namely disease prevention through education, treatment and screening. The cost effectiveness analysis of the adopted control strategies revealed that the combination of prevention and treatment is the most cost effective intervention strategies to combat the pneumonia pandemic. Numerical simulation is performed and pertinent results are displayed graphically.

Keywords: cost effectiveness analysis, optimal control, pneumonia dynamics, stability analysis, numerical simulation

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Authors: Syaza Isma, Timothy Higgins

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In Malaysia, increased take-up rates of tertiary student borrowing, and reliance on retirement savings to fund children's education show the importance of public higher education financing schemes (PTPTN). PTPTN has been operating for 2 decades now; however, there are some critical issues and challenges that include low loan recovery and loan default that suggest a detailed consideration of student loan/financing scheme alternatives is crucial. In addition, the decline in funding level per student following introduction of the new PTPTN full and partial loan scheme has raised ongoing concerns over the sustainability of the scheme to provide continuous financial assistance to students in tertiary education. This research seeks to assess these issues that put greater efficiency in an effort to ensure equitable access to student funding for current and future generations. We explore the extent of repayment hardship under the current loan arrangements that presumably led to low recovery from the borrowers, particularly low-income graduates. The concept of manageable debt exists in the design of income-contingent repayment schemes, as practiced in Australia, New Zealand, UK, Hungary, USA (in limited form), the Netherlands, and South Korea. Can Income Contingent Loans (ICL) offer the best practice for an education financing scheme, and address the issue of repayment hardship and concurrently, can a properly designed ICL scheme provide a solution to the current issues and challenges facing Malaysia student financing? We examine the different potential ICL models using deterministic and stochastic approach to simulate income of graduates.

Keywords: deterministic, income contingent loan, repayment burden, simulation, stochastic

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Authors: Farzad Jafarpour Taher, Maghsud Solimanpur

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Production planning is one of the complex tasks in multi-product firms that produce a wide range of products. Since resources in mass production companies are limited and different products use common resources, there must be a careful plan so that firms can respond to customer needs efficiently. Azar-battery Company is a firm that provides twenty types of products for its customers. Therefore, careful planning must be performed in this company. In this research, the current conditions of Azar-battery Company were investigated to provide a mathematical programming model to determine the optimum production rate of the products in this company. The production system of this company is multi-stage, multi-product and multi-period. This system is studied in terms of a one-year planning horizon regarding the capacity of machines and warehouse space limitation. The problem has been modeled as a linear programming model with deterministic demand in which shortage is not allowed. The objective function of this model is to minimize costs (including raw materials, assembly stage, energy costs, packaging, and holding). Finally, this model has been solved by Lingo software using the branch and bound approach. Since the computation time was very long, the solver interrupted, and the obtained feasible solution was used for comparison. The proposed model's solution costs have been compared to the company’s real data. This non-optimal solution reduces the total production costs of the company by about %35.

Keywords: multi-period, multi-product production, multi-stage, production planning

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Authors: Martins Onyekwelu Onuorah, Jnr Dahiru Usman

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Introduction: In the realm of public health, the threat posed by Monkeypox continues to elicit concern, prompting rigorous studies to understand its dynamics and devise effective containment strategies. Particularly significant is its recurrence in variable populations, such as the observed outbreak in Nigeria in 2022. In light of this, our study undertakes a meticulous analysis, employing a data-driven approach to explore, validate, and propose optimized intervention strategies tailored to the distinct dynamics of Monkeypox within varying demographic structures. Utilizing a deterministic mathematical model, we delved into the intricate dynamics of Monkeypox, with a particular focus on a variable population context. Our qualitative analysis provided insights into the disease-free equilibrium, revealing its stability when R0 is less than one and discounting the possibility of backward bifurcation, as substantiated by the presence of a single stable endemic equilibrium. The model was rigorously validated using real-time data from the Nigerian 2022 recorded cases for Epi weeks 1 – 52. Transitioning from qualitative to quantitative, we augmented our deterministic model with optimal control, introducing three time-dependent interventions to scrutinize their efficacy and influence on the epidemic's trajectory. Numerical simulations unveiled a pronounced impact of the interventions, offering a data-supported blueprint for informed decision-making in containing the disease. A comprehensive cost-effectiveness analysis employing the Infection Averted Ratio (IAR), Average Cost-Effectiveness Ratio (ACER), and Incremental Cost-Effectiveness Ratio (ICER) facilitated a balanced evaluation of the interventions’ economic and health impacts. In essence, our study epitomizes a holistic approach to understanding and mitigating Monkeypox, intertwining rigorous mathematical modeling, empirical validation, and economic evaluation. The insights derived not only bolster our comprehension of Monkeypox's intricate dynamics but also unveil optimized, cost-effective interventions. This integration of methodologies and findings underscores a pivotal stride towards aligning public health imperatives with economic sustainability, marking a significant contribution to global efforts in combating infectious diseases.

Keywords: monkeypox, equilibrium states, stability, bifurcation, optimal control, cost-effectiveness

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Authors: Mochamad Beta Auditama, Yusuf Kurniawan

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A smartphone or tablet require a strong randomness to establish secure encrypted communication, encrypt files, etc. Therefore, random number generation is one of the main keys to provide secrecy. Android devices are equipped with hardware-based sensors, such as accelerometer, gyroscope, etc. Each of these sensors provides a stochastic process which has a potential to be used as an extra randomness source, in addition to /dev/random and /dev/urandom pseudorandom number generators. Android sensors can provide randomness automatically. To obtain randomness from Android sensors, each one of Android sensors shall be used to construct an entropy source. After all entropy sources are constructed, output from these entropy sources are combined to provide more entropy. Then, a deterministic process is used to produces a sequence of random bits from the combined output. All of these processes are done in accordance with NIST SP 800-22 and the series of NIST SP 800-90. The operation conditions are done 1) on Android user-space, and 2) the Android device is placed motionless on a desk.

Keywords: Android hardware-based sensor, deterministic process, entropy source, random number generation/generators

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Authors: Guenther Schuh, Michael Riesener, Frederic Diels

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Nowadays, manufacturing companies are faced with the challenge of meeting heterogeneous customer requirements in short product life cycles with a variety of product functions. So far, some of the functional requirements remain unknown until late stages of the product development. A way to handle these uncertainties is the highly iterative product development (HIP) approach. By structuring the development project as a highly iterative process, this method provides customer oriented and marketable products. There are first approaches for combined, hybrid models comprising deterministic-normative methods like the Stage-Gate process and empirical-adaptive development methods like SCRUM on a project management level. However, almost unconsidered is the question, which development scopes can preferably be realized with either empirical-adaptive or deterministic-normative approaches. In this context, a development scope constitutes a self-contained section of the overall development objective. Therefore, this paper focuses on a methodology that deals with the uncertainty of requirements within the early development stages and the corresponding selection of the most appropriate development approach. For this purpose, internal influencing factors like a company’s technology ability, the prototype manufacturability and the potential solution space as well as external factors like the market accuracy, relevance and volatility will be analyzed and combined into an Agile-Indicator. The Agile-Indicator is derived in three steps. First of all, it is necessary to rate each internal and external factor in terms of the importance for the overall development task. Secondly, each requirement has to be evaluated for every single internal and external factor appropriate to their suitability for empirical-adaptive development. Finally, the total sums of internal and external side are composed in the Agile-Indicator. Thus, the Agile-Indicator constitutes a company-specific and application-related criterion, on which the allocation of empirical-adaptive and deterministic-normative development scopes can be made. In a last step, this indicator will be used for a specific clustering of development scopes by application of the fuzzy c-means (FCM) clustering algorithm. The FCM-method determines sub-clusters within functional clusters based on the empirical-adaptive environmental impact of the Agile-Indicator. By means of the methodology presented in this paper, it is possible to classify requirements, which are uncertainly carried out by the market, into empirical-adaptive or deterministic-normative development scopes.

Keywords: agile, highly iterative development, agile-indicator, product development

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Authors: Deepshikha Shukla, C. H. Solanki, Mayank Desai

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In the last few years, it has been observed that earthquake is proving a threat to the scientist across the world. With a large number of earthquakes occurring in day to day life, the threat to life and property has increased manifolds which call for an urgent attention of all the researchers globally to carry out the research in the field of Earthquake Engineering. Any hazard related to the earthquake and seismicity is considered to be seismic hazards. The common forms of seismic hazards are Ground Shaking, Structure Damage, Structural Hazards, Liquefaction, Landslides, Tsunami to name a few. Among all the natural hazards, the most devastating and damaging is the earthquake as all other hazards are triggered only after the occurrence of an earthquake. In order to quantify and estimate the seismicity and seismic hazards, many methods and approaches have been proposed in the past few years. Such approaches are Mathematical, Conventional and Computational. Convex Set Theory, Empirical Green’s Function are some of the Mathematical Approaches whereas the Deterministic and Probabilistic Approaches are the Conventional Approach for the estimation of the seismic Hazards. Ground response and Ground Shaking of a particular area or region plays an important role in the damage caused due to the earthquake. In this paper, seismic study using Deterministic Approach and 1 D Ground Response Analysis has been carried out for Madhya Pradesh Tourism Office in Indore Region in Madhya Pradesh in Central India. Indore lies in the seismic zone III (IS: 1893, 2002) in the Seismic Zoning map of India. There are various faults and lineament in this area and Narmada Some Fault and Gavilgadh fault are the active sources of earthquake in the study area. Deepsoil v6.1.7 has been used to perform the 1 D Linear Ground Response Analysis for the study area. The Peak Ground Acceleration (PGA) of the city ranges from 0.1g to 0.56g.

Keywords: seismicity, seismic hazards, deterministic, probabilistic methods, ground response analysis

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Authors: Muhammad Luqman, Yusuf Kurniawan

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S-Boxes is one of the linear parts of the cryptographic algorithm. The existence of S-Box in the cryptographic algorithm is needed to maintain non-linearity of the algorithm. Nowadays, modern cryptographic algorithms use an S-Box as a part of algorithm process. Despite the fact that several cryptographic algorithms today reuse theoretically secure and carefully constructed S-Boxes, there is an evaluation characteristic that can measure security properties of S-Boxes and hence the corresponding primitives. Analysis of an S-Box usually is done using manual mathematics calculation. Several S-Boxes are presented as a Truth Table without any mathematical background algorithm. Then, it’s rather difficult to determine the strength of Truth Table S-Box without a mathematical algorithm. A comprehensive analysis should be applied to the Truth Table S-Box to determine the characteristic. Several important characteristics should be owned by the S-Boxes, they are Nonlinearity, Balancedness, Algebraic degree, LAT, DAT, differential delta uniformity, correlation immunity and global avalanche criterion. Then, a comprehensive tool will be present to automatically calculate the characteristics of S-Boxes and determine the strength of S-Box. Comprehensive analysis is done on a deterministic process to produce a sequence of S-Boxes characteristic and give advice for a better S-Box construction.

Keywords: cryptographic properties, Truth Table S-Boxes, S-Boxes characteristic, deterministic process

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Authors: Yifu Ding, Vijay Avinash, Malcolm McCulloch

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As the proliferation of decentralized energy systems, the UK power system allows small-scale entities such as microgrids (MGs) to tender multiple energy services including energy arbitrage and frequency responses (FRs). However, its operation requires the balance between the uncertain renewable generations and loads in real-time and has to fulfill their provision requirements of contract services continuously during the time window agreed, otherwise it will be penalized for the under-delivered provision. To hedge against risks due to uncertainties and maximize the economic benefits, we propose a stochastic model predictive control (SMPC) framework to optimize its operation for the multi-service provision. Distinguished from previous works, we include a detailed economic-degradation model of the lithium-ion battery to quantify the costs of different service provisions, as well as accurately describe the changing dynamics of the battery. Considering a branch of load and generation scenarios and the battery aging, we formulate a risk-averse cost function using conditional value at risk (CVaR). It aims to achieve the maximum expected net revenue and avoids severe losses. The framework will be performed on a case study of a PV-battery grid-tied microgrid in the UK with real-life data. To highlight its performance, the framework will be compared with the case without the degradation model and the deterministic formulation.

Keywords: model predictive control (MPC), battery degradation, frequency response, microgrids

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Authors: S. Dorbani, M. Badaoui, D. Benouar

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The aim of this paper is to investigate the effect of the building fundamental period of reinforced concrete buildings of (6, 9, and 12-storey), with different floor plans: Symmetric, mono-symmetric, and unsymmetric. These structures are erected at different epicentral distances. Using the Boumerdes, Algeria (2003) earthquake data, we focused primarily on the establishment of the deterministic formulation linking the base shear force to two parameters: The first one is the fundamental period that represents the numerical fingerprint of the structure, and the second one is the epicentral distance used to represent the impact of the earthquake on this force. In a second step, with a view to highlight the effect of uncertainty in these parameters on the analyzed response, these parameters are modeled as random variables with a log-normal distribution. The variability of the coefficients of variation of the chosen uncertain parameters, on the statistics on the seismic base shear force, showed that the effect of uncertainty on fundamental period on this force statistics is low compared to the epicentral distance uncertainty influence.

Keywords: base shear force, fundamental period, epicentral distance, uncertainty, lognormal variables, statistics

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Authors: Nusrat T. Chowdhury

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Green Supply Chain Management (GSCM) is becoming a key to success for profitable businesses. The various activities contributing to carbon emissions in a supply chain are transportation, ordering and holding of inventory. This research work develops a mixed-integer nonlinear programming (MINLP) model that considers the scenario of a supply chain with multiple periods, multiple products and multiple suppliers. The model assumes that the demand is deterministic, the buyer has a limited storage space in each period, the buyer is responsible for the transportation cost, a supplier-dependent ordering cost applies for each period in which an order is placed on a supplier and inventory shortage is permissible. The model provides an optimal decision regarding what products to order, in what quantities, with which suppliers, and in which periods in order to maximize the profit. For the purpose of evaluating the carbon emissions, three different carbon regulating policies i.e., carbon cap-and-trade, the strict cap on carbon emission and carbon tax on emissions, have been considered. The proposed MINLP has been validated using a randomly generated data set.

Keywords: green supply chain, carbon emission, mixed integer non-linear program, inventory shortage, carbon cap-and-trade

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Authors: Afia Naheed, Manmohan Singh, David Lucy

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This work is based on a mathematical as well as statistical study of an SEIJTR deterministic model for the interpretation of transmission of severe acute respiratory syndrome (SARS). Based on the SARS epidemic in 2003, the parameters are estimated using Runge-Kutta (Dormand-Prince pairs) and least squares methods. Possible graphical and numerical techniques are used to validate the estimates. Then effect of the model parameters on the dynamics of the disease is examined using sensitivity and uncertainty analysis. Sensitivity and uncertainty analytical techniques are used in order to analyze the affect of the uncertainty in the obtained parameter estimates and to determine which parameters have the largest impact on controlling the disease dynamics.

Keywords: infectious disease, severe acute respiratory syndrome (SARS), parameter estimation, sensitivity analysis, uncertainty analysis, Runge-Kutta methods, Levenberg-Marquardt method

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Authors: Giovanni Masala, Stefania Marica

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Forecasting electricity load plays a crucial role regards decision making and planning for economical purposes. Besides, in the light of the recent privatization and deregulation of the power industry, the forecasting of future electricity load turned out to be a very challenging problem. Empirical data about electricity load highlights a clear seasonal behavior (higher load during the winter season), which is partly due to climatic effects. We also emphasize the presence of load periodicity at a weekly basis (electricity load is usually lower on weekends or holidays) and at daily basis (electricity load is clearly influenced by the hour). Finally, a long-term trend may depend on the general economic situation (for example, industrial production affects electricity load). All these features must be captured by the model. The purpose of this paper is then to build an hourly electricity load model. The deterministic component of the model requires non-linear regression and Fourier series while we will investigate the stochastic component through econometrical tools. The calibration of the parameters’ model will be performed by using data coming from the Italian market in a 6 year period (2007- 2012). Then, we will perform a Monte Carlo simulation in order to compare the simulated data respect to the real data (both in-sample and out-of-sample inspection). The reliability of the model will be deduced thanks to standard tests which highlight a good fitting of the simulated values.

Keywords: ARMA-GARCH process, electricity load, fitting tests, Fourier series, Monte Carlo simulation, non-linear regression

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Authors: Agnieszka Dudzik

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The problem of correlation between random variables in the structural reliability analysis has been extensively discussed in literature on the subject. The cases taken under consideration were usually related to correlation between random variables from one side of ultimate limit state: correlation between particular loads applied on structure or correlation between resistance of particular members of a structure as a system. It has been proved that positive correlation between these random variables reduces the reliability of structure and increases the probability of failure. In the paper, the problem of correlation between random variables from both side of the limit state equation will be taken under consideration. The simplest case where these random variables are of the normal distributions will be concerned. The case when a degree of that correlation is described by the covariance or the coefficient of correlation will be used. Special attention will be paid on questions: how much that correlation changes the reliability level and can it be ignored. In reliability analysis will be used well-known methods for assessment of the failure probability: based on the Hasofer-Lind reliability index and Monte Carlo method adapted to the problem of correlation. The main purpose of this work will be a presentation how correlation of random variables influence on reliability index of steel bar structures. Structural design parameters will be defined as deterministic values and random variables. The latter will be correlated. The criterion of structural failure will be expressed by limit functions related to the ultimate and serviceability limit state. In the description of random variables will be used only for the normal distribution. Sensitivity of reliability index to the random variables will be defined. If the reliability index sensitivity due to the random variable X will be low when compared with other variables, it can be stated that the impact of this variable on failure probability is small. Therefore, in successive computations, it can be treated as a deterministic parameter. Sensitivity analysis leads to simplify the description of the mathematical model, determine the new limit functions and values of the Hasofer-Lind reliability index. In the examples, the NUMPRESS software will be used in the reliability analysis.

Keywords: correlation of random variables, reliability index, sensitivity of reliability index, steel structure

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Authors: Jann-Eike Saathoff, Kirill Alexander Schmoor, Martin Achmus, Mauricio Terceros

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By using partial factors of safety, uncertainties due to the inherent variability of the soil properties and loads are taken into account in the geotechnical design process. According to the reliability index concept in Eurocode-0 in conjunction with Eurocode-7 a minimum safety level of β = 3.8 for reliability class RC2 shall be established. The reliability of the system depends heavily on the choice of the prespecified safety factor and the choice of the characteristic soil properties. The safety factors stated in the standards are mainly based on experience. However, no general accepted method for the calculation of a characteristic value within the current design practice exists. In this study, a laterally loaded monopile is investigated and the influence of the chosen quantile values of the deterministic system, calculated with p-y springs, will be presented. Monopiles are the most common foundation concepts for offshore wind energy converters. Based on the calculations for non-cohesive soils, a recommendation for an appropriate quantile value for the necessary safety level according to the standards for a deterministic design is given.

Keywords: asymptotic sampling, characteristic value, monopile foundation, probabilistic design, quantile values

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Authors: Dorna Kargar, Mehrasa Masih

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Due to its special geological and geographical conditions, Iran has always been exposed to various natural hazards. Earthquake is one of the natural hazards with random nature that can cause significant financial damages and casualties. This is a serious threat, especially in areas with active faults. Therefore, considering the population density in some parts of the country, locating and zoning high-risk areas are necessary and significant. In the present study, seismic hazard assessment via probabilistic and deterministic method for Tehran, the capital of Iran, which is located in Alborz-Azerbaijan province, has been done. The seismicity study covers a range of 200 km from the north of Tehran (X=35.74° and Y= 51.37° in LAT-LONG coordinate system) to identify the seismic sources and seismicity parameters of the study region. In order to identify the seismic sources, geological maps at the scale of 1: 250,000 are used. In this study, we used Kijko-Sellevoll's method (1992) to estimate seismicity parameters. The maximum likelihood estimation of earthquake hazard parameters (maximum regional magnitude Mmax, activity rate λ, and the Gutenberg-Richter parameter b) from incomplete data files is extended to the case of uncertain magnitude values. By the combination of seismicity and seismotectonic studies of the site, the acceleration with antiseptic probability may happen during the useful life of the structure is calculated with probabilistic and deterministic methods. Applying the results of performed seismicity and seismotectonic studies in the project and applying proper weights in used attenuation relationship, maximum horizontal and vertical acceleration for return periods of 50, 475, 950 and 2475 years are calculated. Horizontal peak ground acceleration on the seismic bedrock for 50, 475, 950 and 2475 return periods are 0.12g, 0.30g, 0.37g and 0.50, and Vertical peak ground acceleration on the seismic bedrock for 50, 475, 950 and 2475 return periods are 0.08g, 0.21g, 0.27g and 0.36g.

Keywords: peak ground acceleration, probabilistic and deterministic, seismic hazard assessment, seismicity parameters

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Authors: Mikheil Kalmakhelidze

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Record linkage (RL) problem has become more and more important in recent years due to the growing interest towards big data analysis. The problem can be formulated in a very simple way: Given two entries a and b of a database, decide whether they represent the same object or not. There are two classical deterministic and probabilistic ways of solving the RL problem. Using simple Bayes classifier in many cases produces useful results but sometimes they show to be poor. In recent years several successful approaches have been made towards solving specific RL problems by neural network algorithms including single layer perception, multilayer back propagation network etc. In our work, we model the RL problem for specific dataset of student applications in fuzzy description logic (FDL) where linkage of specific pair (a,b) depends on the truth value of corresponding formula A(a,b) in a canonical FDL model. As a main result, we build neural network for deciding truth value of FDL formulas in a canonical model and thus link RL problem to machine learning. We apply the approach to dataset with 10000 entries and also compare to classical RL solving approaches. The results show to be more accurate than standard probabilistic approach.

Keywords: description logic, fuzzy logic, neural networks, record linkage

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Authors: Ghada Moussa, Mamoud Owais

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Transit Route Network Design Problem (TrNDP) is the most important component in Transit planning, in which the overall cost of the public transportation system highly depends on it. The main purpose of this study is to develop a novel solution methodology for the TrNDP, which goes beyond pervious traditional sophisticated approaches. The novelty of the solution methodology, adopted in this paper, stands on the deterministic operators which are tackled to construct bus routes. The deterministic manner of the TrNDP solution relies on using linear and integer mathematical formulations that can be solved exactly with their standard solvers. The solution methodology has been tested through Mandl’s benchmark network problem. The test results showed that the methodology developed in this research is able to improve the given network solution in terms of number of constructed routes, direct transit service coverage, transfer directness and solution reliability. Although the set of routes resulted from the methodology would stand alone as a final efficient solution for TrNDP, it could be used as an initial solution for meta-heuristic procedures to approach global optimal. Based on the presented methodology, a more robust network optimization tool would be produced for public transportation planning purposes.

Keywords: integer programming, transit route design, transportation, urban planning

Procedia PDF Downloads 233

Authors: Romisaa Ali

Abstract:

This paper presents a comparison between twin-delayed Deep Deterministic Policy Gradient (TD3) and Soft Actor-Critic (SAC) reinforcement learning algorithms in the context of training robust navigation policies for Jackal robots. By leveraging an open-source framework and custom motion control environments, the study evaluates the performance, robustness, and transferability of the trained policies across a range of scenarios. The primary focus of the experiments is to assess the training process, the adaptability of the algorithms, and the robot’s ability to navigate in previously unseen environments. Moreover, the paper examines the influence of varying environmental complexities on the learning process and the generalization capabilities of the resulting policies. The results of this study aim to inform and guide the development of more efficient and practical reinforcement learning-based navigation policies for Jackal robots in real-world scenarios.

Keywords: Jackal robot environments, reinforcement learning, TD3, SAC, robust navigation, transferability, custom environment

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Authors: Farshad Zeinali, Sajedeh Norouzi, Nader Mokari, Eduard Jorswieck

Abstract:

The capacity of fifth-generation (5G) vehicle-to-everything (V2X) networks poses significant challenges. To ad- dress this challenge, this paper utilizes New Radio (NR) and New Radio Unlicensed (NR-U) networks to develop a heterogeneous vehicular network (HetNet). We propose a new framework, named joint BS assignment and resource allocation (JBSRA) for mobile V2X users and also consider coexistence schemes based on flexible duty cycle (DC) mechanism for unlicensed bands. Our objective is to maximize the average throughput of vehicles while guaranteeing the WiFi users' throughput. In simulations based on deep reinforcement learning (DRL) algorithms such as deep deterministic policy gradient (DDPG) and deep Q network (DQN), our proposed framework outperforms existing solutions that rely on fixed DC or schemes without consideration of unlicensed bands.

Keywords: vehicle-to-everything (V2X), resource allocation, BS assignment, new radio (NR), new radio unlicensed (NR-U), coexistence NR-U and WiFi, deep deterministic policy gradient (DDPG), deep Q-network (DQN), joint BS assignment and resource allocation (JBSRA), duty cycle mechanism

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Authors: Sanam Haseen, Abdul Bari

Abstract:

In this paper a three stage multivariate programming problem with random survey cost and variances as random variables has been formulated as a non-linear stochastic programming problem. The problem has been converted into an equivalent deterministic form using chance constraint programming and modified E-modeling. An empirical study of the problem has been done at the end of the paper using R-simulation.

Keywords: chance constraint programming, modified E-model, stochastic programming, stratified sample surveys, three stage sample surveys

Procedia PDF Downloads 424

Authors: Abderrahmane Soufi

Abstract:

The building sector is the largest consumer of energy in France, accounting for 44% of French consumption. To reduce energy consumption and improve energy efficiency, France implemented an energy transition law targeting 40% energy savings by 2030 in the tertiary building sector. Building simulation tools are used to predict the energy performance of buildings but the reliability of these tools is hampered by discrepancies between the real and simulated energy performance of a building. This performance gap lies in the simplified assumptions of certain factors, such as the behavior of occupants on air conditioning and heating, which is considered deterministic when setting a fixed operating schedule and a fixed interior comfort temperature. However, the behavior of occupants on air conditioning and heating is stochastic, diverse, and complex because it can be affected by many factors. Probabilistic models are an alternative to deterministic models. These models are usually derived from statistical data and express occupant behavior by assuming a probabilistic relationship to one or more variables. In the literature, logistic regression has been used to model the behavior of occupants with regard to heating and air conditioning systems by considering univariate logistic models in residential buildings; however, few studies have developed multivariate models for higher education and vocational training buildings in a Mediterranean climate. Therefore, in this study, occupant behavior on heating and air conditioning systems was modeled using logistic regression. Occupant behavior related to the turn-on heating and air conditioning systems was studied through experimental measurements collected over a period of one year (June 2023–June 2024) in three classrooms occupied by several groups of students in engineering schools and professional training. Instrumentation was provided to collect indoor temperature and indoor relative humidity in 10-min intervals. Furthermore, the state of the heating/air conditioning system (off or on) and the set point were determined. The outdoor air temperature, relative humidity, and wind speed were collected as weather data. The number of occupants, age, and sex were also considered. Logistic regression was used for modeling an occupant turning on the heating and air conditioning systems. The results yielded a proposed model that can be used in building simulation tools to predict the energy performance of teaching buildings. Based on the first months (summer and early autumn) of the investigations, the results illustrate that the occupant behavior of the air conditioning systems is affected by the indoor relative humidity and temperature in June, July, and August and by the indoor relative humidity, temperature, and number of occupants in September and October. Occupant behavior was analyzed monthly, and univariate and multivariate models were developed.

Keywords: occupant behavior, logistic regression, behavior model, mediterranean climate, air conditioning, heating

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Authors: Firman Riyudha, Endrik Mifta Shaiful

Abstract:

The spread of drug users is one kind of behavioral epidemiology that becomes a threat to every country in the world. This problem caused various crisis simultaneously, including financial or economic crisis, social, health, until human crisis. Most drug users are teenagers at school age. A new deterministic model would be constructed to determine the dynamics of the spread of drug users by considering level of education in a susceptible population. Based on the analytical model, two equilibria points were obtained; there were E₀ (zero user) and E₁ (endemic equilibrium). Existence of equilibrium and local stability of equilibria depended on the Basic Reproduction Ratio (R₀). This parameter was defined as the expected rate of secondary prevalence and primary prevalence in virgin population along spreading primary prevalence. The zero-victim equilibrium would be locally asymptotically stable if R₀ < 1 while if R₀ > 1 the endemic equilibrium would be locally asymptotically stable. The result showed that R₀ was proportional to the rate of interaction of each susceptible population based on educational level with the users' population. It is concluded that there was a need to be given a control in interaction, so that drug users population could be minimized. Numerical simulations were also provided to support analytical results.

Keywords: drugs users, level education, mathematical model, stability

Procedia PDF Downloads 442