Search results for: simulation techniques

Authors: Upal Barua

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Crash risk at intersections is a critical safety issue. This paper examines the effectiveness of removing an existing off-set at an intersection by realignment, in reducing crashes. Empirical Bayes method was applied to conduct a before-and-after study to assess the effect of this safety improvement. The Transportation Safety Improvement Program in Austin Transportation Department completed several safety improvement projects at high crash intersections with a view to reducing crashes. One of the common safety improvement techniques applied was the realignment of intersection approaches removing an existing off-set. This paper illustrates how this safety improvement technique is applied at a high crash intersection from inception to completion. This paper also highlights the significant crash reductions achieved from this safety improvement technique applying Empirical Bayes method in a before-and-after study. The result showed that realignment of intersection approaches removing an existing off-set can reduce crashes by 53%. This paper also features the state of the art techniques applied in planning, engineering, designing and construction of this safety improvement, key factors driving the success, and lessons learned in the process.

Keywords: crash risk, intersection, off-set, safety improvement technique, before-and-after study, empirical Bayes method

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Authors: Rui Chen, Shu-min Zhou, Xiong-jie Zhang, Ren-bo Wang, Fan Huang, Bin Tang

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During the temperature changes in reactor core, the nuclide cross section in reactor can vary with temperature, which eventually causes the changes of reactivity. To simulate the interaction between incident neutron and various materials at different temperatures on the nose, it is necessary to generate all the relevant reaction temperature-dependent cross section. Traditionally, the real time cross section generation method is used to avoid storing huge data but contains severe problems of low efficiency and adaptability for narrow energy region. Focused on the research on multi-temperature cross sections generation in real time during in neutron transport, this paper investigated the on-the-fly cross section generation method for resolved resonance region, thermal region and unresolved resonance region, and proposed the real time multi-temperature cross sections generation method based on double-exponential formula for resolved resonance region, as well as the Neville interpolation for thermal and unresolved resonance region. To prove the correctness and validity of multi-temperature cross sections generation based on wide energy region of incident neutron, the proposed method was applied in critical safety benchmark tests, which showed the capability for application in reactor multi-physical coupling simulation.

Keywords: cross section, neutron transport, numerical simulation, on-the-fly

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Authors: Hany M. Abd El-Lateef

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Two novel Schiff bases, 5-bromo-2-[(E)-(pyridin-3-ylimino) methyl] phenol (HBSAP) and 5-bromo-2-[(E)-(quinolin-8-ylimino) methyl] phenol (HBSAQ) have been synthesized. They have been characterized by elemental analysis and spectroscopic techniques (UV–Vis, IR and NMR). Moreover, the molecular structure of HBSAP and HBSAQ compounds are determined by single crystal X-ray diffraction technique. The inhibition activity of HBSAP and HBSAQ for carbon steel in 3.5 %NaCl+0.1 M HCl for both short and long immersion time, at different temperatures (20-50 ºC), was investigated using electrochemistry and surface characterization. The potentiodynamic polarization shows that the inhibitors molecule is more adsorbed on the cathodic sites. Its efficiency increases with increasing inhibitor concentrations (92.8 % at the optimal concentration of 10-3 M for HBSAQ). Adsorption of the inhibitors on the carbon steel surface was found to obey Langmuir’s adsorption isotherm with physical/chemical nature of the adsorption, as it is shown also by scanning electron microscopy. Further, the electronic structural calculations using quantum chemical methods were found to be in a good agreement with the results of the experimental studies.

Keywords: carbon steel, Schiff bases, corrosion inhibition, SEM, electrochemical techniques

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Authors: Abdulkarem Qasem Dammag, Basema Qasim Dammag, Jian Dai

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Change in Land use and Land cover (LU/LC) has a profound impact on the area's natural, economic, and ecological development, and the search for drivers of land cover change is one of the fundamental issues of LU/LC change. The study aimed to assess the temporal and Spatio-temporal dynamics of LU/LC in the past and to predict the future using Landsat images by exploring the characteristics of different LU/LC types. Spatio-temporal patterns of LU/LC change in Ibb Governorate, Yemen, were analyzed based on RS and GIS from 1990, 2005, and 2020. A socioeconomic survey and key informant interviews were used to assess potential drivers of LU/LC. The results showed that from 1990 to 2020, the total area of vegetation land decreased by 5.3%, while the area of barren land, grassland, built-up area, and waterbody increased by 2.7%, 1.6%, 1.04%, and 0.06%, respectively. Based on socio-economic surveys and key informant interviews, natural factors had a significant and long-term impact on land change. In contrast, site construction and socio-economic factors were the main driving forces affecting land change in a short time scale. The analysis results have been linked to the CA-Markov Land Use simulation and forecasting model for the years 2035 and 2050. The simulation results revealed from the period 2020 to 2050, the trend of dynamic changes in land use, where the total area of barren land decreased by 7.0% and grassland by 0.2%, while the vegetation land, built-up area, and waterbody increased by 4.6%, 2.6%, and 0.1 %, respectively. Overall, these findings provide LULC's past and future trends and identify drivers, which can play an important role in sustainable land use planning and management by balancing and coordinating urban growth and land use and can also be used at the regional level in different levels to provide as a reference. In addition, the results provide scientific guidance to government departments and local decision-makers in future land-use planning through dynamic monitoring of LU/LC change.

Keywords: LU/LC change, CA-Markov model, driving forces, change detection, LU/LC change simulation

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Authors: Vitalii Yu. Kapitan, Aleksandr V. Perzhu, Konstantin V. Nefedev

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The necessity of studying magnetic multilayer structures is explained by the prospects of their practical application as a technological base for creating new storages medium. Magnetic multilayer films have many unique features that contribute to increasing the density of information recording and the speed of storage devices. Multilayer structures are structures of alternating magnetic and nonmagnetic layers. In frame of the classical Heisenberg model, lattice spin systems with direct short- and long-range exchange interactions were investigated by Monte Carlo methods. The thermodynamic characteristics of multilayer structures, such as the temperature behavior of magnetization, energy, and heat capacity, were investigated. The processes of magnetization reversal of multilayer structures in external magnetic fields were investigated. The developed software is based on the new, promising programming language Rust. Rust is a new experimental programming language developed by Mozilla. The language is positioned as an alternative to C and C++. For the Monte Carlo simulation, the Metropolis algorithm and its parallel implementation using MPI and the Wang-Landau algorithm were used. We are planning to study of magnetic multilayer films with asymmetric Dzyaloshinskii–Moriya (DM) interaction, interfacing effects and skyrmions textures. This work was supported by the state task of the Ministry of Education and Science of the Russia # 3.7383.2017/8.9

Keywords: The Monte Carlo methods, Heisenberg model, multilayer structures, magnetic skyrmion

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Authors: Seyed Mehdi Ghezi, Hesam Hasanpoor

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This scientific article explores enhancement methods for ensemble learning with the aim of improving the performance of classifiers in the analysis and classification of brain signals. The research approach in this field consists of two main parts, each with its own strengths and weaknesses. The choice of approach depends on the specific research question and available resources. By combining these approaches and leveraging their respective strengths, researchers can enhance the accuracy and reliability of classification results, consequently advancing our understanding of the brain and its functions. The first approach focuses on utilizing machine learning methods to identify the best features among the vast array of features present in brain signals. The selection of features varies depending on the research objective, and different techniques have been employed for this purpose. For instance, the genetic algorithm has been used in some studies to identify the best features, while optimization methods have been utilized in others to identify the most influential features. Additionally, machine learning techniques have been applied to determine the influential electrodes in classification. Ensemble learning plays a crucial role in identifying the best features that contribute to learning, thereby improving the overall results. The second approach concentrates on designing and implementing methods for selecting the best classifier or utilizing meta-classifiers to enhance the final results in ensemble learning. In a different section of the research, a single classifier is used instead of multiple classifiers, employing different sets of features to improve the results. The article provides an in-depth examination of each technique, highlighting their advantages and limitations. By integrating these techniques, researchers can enhance the performance of classifiers in the analysis and classification of brain signals. This advancement in ensemble learning methodologies contributes to a better understanding of the brain and its functions, ultimately leading to improved accuracy and reliability in brain signal analysis and classification.

Keywords: ensemble learning, brain signals, classification, feature selection, machine learning, genetic algorithm, optimization methods, influential features, influential electrodes, meta-classifiers

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Authors: M. Graus, K. Westhoff, X. Xu

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The climate change causes a change in all aspects of society. While the expansion of renewable energies proceeds, industry could not be convinced based on general studies about the potential of demand side management to reinforce smart grid considerations in their operational business. In this article, a procedure model for a case-specific data-driven decision support for industrial energy management based on a holistic data analytics approach is presented. The model is executed on the example of the strategic decision problem, to integrate the aspect of renewable energies into industrial energy management. This question is induced due to considerations of changing the electricity contract model from a standard rate to volatile energy prices corresponding to the energy spot market which is increasingly more affected by renewable energies. The procedure model corresponds to a data analytics process consisting on a data model, analysis, simulation and optimization step. This procedure will help to quantify the potentials of sustainable production concepts based on the data from a factory. The model is validated with data from a printer in analogy to a simple production machine. The overall goal is to establish smart grid principles for industry via the transformation from knowledge-driven to data-driven decisions within manufacturing companies.

Keywords: data analytics, green production, industrial energy management, optimization, renewable energies, simulation

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Authors: Óscar Alfonso Gómez Sepúlveda, Julián Ernesto Jaramillo, Diego Camilo Durán

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The global climate crisis has affected different aspects of human life, and in an effort to reverse the effects generated, we seek to optimize and improve the equipment and plants that produce high emissions of CO₂, being possible to achieve this through numerical simulations. These equipments include biomass combustion chambers. The objective of this research is to visualize the thermal behavior of a gas chamber that is used in the process of obtaining vegetable extracts. The simulation is carried out with OpenFOAM taking into account the conservation of energy, turbulence, and radiation; for the purposes of the simulation, combustion is omitted and replaced by heat generation. Within the results, the streamlines generated by the primary and secondary flows are analyzed in order to visualize whether they generate the expected effect, and the energy is used to the maximum. The inclusion of radiation seeks to compare its influence and also simplify the computational times to perform mesh analysis. An analysis is carried out with simplified geometries and with experimental data to corroborate the selection of the models to be used, and it is obtained that for turbulence, the appropriate one is the standard k - w. As a means of verification, a general energy balance is made and compared with the results of the numerical analysis, where the error is 1.67%, which is considered acceptable. From the approach to improvement options, it was found that with the implementation of fins, heat can be increased by up to 7.3%.

Keywords: CFD analysis, biomass, heat transfer, radiation, OpenFOAM

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Authors: Kausar Ali

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There are several ground treatment techniques which may meet the twin objectives of increasing the bearing capacity with simultaneous reduction of settlements, but the use of stone columns is one of the most suited techniques for flexible structures such as embankments, oil storage tanks etc. that can tolerate some settlement and used worldwide. However, when the stone columns in very soft soils are loaded; stone columns undergo excessive settlement due to low lateral confinement provided by the soft soil, leading to the failure of the structure. The poor performance of stone columns under these conditions can be improved by encasing the columns with a suitable geosynthetic. In this study, the effect of reinforcement on bearing capacity of composite soil has been investigated by conducting laboratory model tests on floating and end bearing long stone columns with l/d ratio of 12. The columns were reinforced by providing geosynthetic encasement over varying column length (upper 25%, 50%, 75%, and 100% column length). In this study, a group of columns has been used instead of single column, because in the field, columns used for the purpose always remain in groups. The tests indicate that the encasement over the full column length gives higher failure stress as compared to the encasement over the partial column length for both floating and end bearing long columns. The performance of end-bearing columns was found much better than the floating columns.

Keywords: geosynthetic, ground improvement, soft clay, stone column

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Authors: Dorota Miroslaw-Swiatek, Mateusz Grygoruk, Sylwia Szporak

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We applied a two-dimensional surface water flow model with irregular wet boundaries. In this model, flow equations are in the form of a 2-D, non-linear diffusion equations which allows to account spatial variations in flow resistance and topography. Calculation domain to simulate the flow pattern in the floodplain is congruent with a Digital Elevation Model (DEM) grid. The rate and direction of sheet flow in wetlands is affected by vegetation type and density, therefore the developed model take into account spatial distribution vegetation resistance to the water flow. The model was tested in a part of the Biebrza Valley, of an outstanding heterogeneity in the elevation and flow resistance distributions due to various ecohydrological conditions and management measures. In our approach we used the highest-possible quality of the DEM in order to obtain hydraulic slopes and vegetation distribution parameters for the modelling. The DEM was created from the cloud of points measured in the LiDAR technology. The LiDAR reflects both the land surface as well as all objects on top of it such as vegetation. Depending on the density of vegetation cover the ability of laser penetration is variable. Therefore to obtain accurate land surface model the “vegetation effect” was corrected using data collected in the field (mostly the vegetation height) and satellite imagery such as Ikonos (to distinguish different vegetation types of the floodplain and represent them spatially). Model simulation was performed for the spring thaw flood in 2009.

Keywords: floodplain flow, Biebrza valley, model simulation, 2D surface flow model

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Authors: Ji Young Lee, Jong Woon Kim

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It is essential to secure high availability of railway vehicles to realize high quality and efficiency of railway service. Once the availability decreased, planned railway service could not be provided or more cars need to be reserved. additional cars need to be purchased or the frequency of railway service could be decreased. Such situation would be a big loss in terms of quality and cost related to railway service. Therefore, we make various efforts to get high availability of railway vehicles. Because it is a big loss to operators, we make various efforts to get high availability of railway vehicles. To secure high availability, the idle time of the vehicle needs to be reduced and the following methods are applied to railway vehicles. First, through modularization design, exchange time for line replaceable units is reduced which makes railway vehicles could be put into the service quickly. Second, to reduce periodic preventive maintenance time, preventive maintenance with short period would be proceeded test oriented to minimize the maintenance time, and reliability is secured through overhauls for each main component. With such design changes for railway vehicles, modularized components are exchanged first at the time of vehicle failure or overhaul so that vehicles could be put into the service quickly and exchanged components are repaired or overhauled. Therefore, spare components are required for any future failures or overhauls. And, as components are modularized and costs for components are high, it is considerably important to get reasonable quantities of spare components. Especially, when a number of railway vehicles were put into the service simultaneously, the time of overhauls come almost at the same time. Thus, for some vehicles, components need to be exchanged and overhauled before appointed overhaul period so that these components could be secured as spare parts for the next vehicle’s component overhaul. For this reason, components overhaul time and spare parts quantities should be decided at the same time. This study deals with the time of overhauls for repairable components of railway vehicles and the calculation of spare parts quantities in consideration of future failure/overhauls. However, as railway vehicles are used according to the service schedule, maintenance work cannot be proceeded after the service was closed thus it is quite difficult to resolve this situation mathematically. In this study, Simulation software system is used in this study for analyzing the time of overhauls for repairable components of railway vehicles and the spare parts for the railway systems.

Keywords: overhaul time, rolling stocks, simulation, spare parts

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Authors: Olutola Funmilayo Adekeye

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Tradition African practice of architecture can be said to be deeply rooted in Green Architecture in concept, design and execution. A study into the ancient building techniques in Ilorin Emirate depicts prominent (eco-centric approach of) Green Architecture principles. In the Pre-colonial era before the introduction of modern architecture and Western building materials, the Nigeria traditional communities built their houses to meet their cultural, religious and social needs using mainly indigenous building materials such as mud (Amo), cowdung (Boto), straws (koriko), palm fronts (Imo-Ope) to mention a few. This research attempts to identify the various techniques of applying the traditional African principles of Green Architecture to Ilorin traditional buildings. It will examine and assess some case studies to understand the extent to which Green architecture principles have been applied to traditional building designs that are still preserved today in Ilorin, Nigeria. Furthermore, this study intends to answer many questions, which can be summarized into two basic questions which are: (1) What aspects of what today are recognized as important green architecture principles have been applied to Ilorin traditional buildings? (2) To what extent have the principles of green architecture applied to Ilorin traditional buildings been ways of demonstrating a cultural attachment to the earth as an expression of the African sense of human being as one with nature?

Keywords: green architecture, Ilorin, traditional buildings, design principles, ecocentric, application

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Authors: Elisabetta Cianfanelli, Margherita Tufarelli, Paolo Pupparo

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The dialectical mediation between desires and objects or between mass production and consumption continues to evolve over time. This relationship is influenced both by variable geometries of contexts that are distant from the mere design of product form and by aspects rooted in the very definition of industrial design. In particular, the overcoming of macro-areas of innovation in the technological, social, cultural, formal, and morphological spheres, supported by recent theories in critical and speculative design, seems to be moving further and further away from the design of the formal dimension of advanced products. The articulated fabric of theories and practices that feed the definition of “hyperobjects”, and no longer objects describes a common tension in all areas of design and production of industrial products. The latter are increasingly detached from the design of the form and meaning of the same in mass productions, thus losing the quality of products capable of social transformation. For years we have been living in a transformative moment as regards the design process in the definition of the industrial product. We are faced with a dichotomy in which there is, on the one hand, a reactionary aversion to the new techniques of industrial production and, on the other hand, a sterile adoption of the techniques of mass production that we can now consider traditional. This ambiguity becomes even more evident when we talk about industrial products, and we realize that we are moving further and further away from the concepts of "form" as a synthesis of a design thought aimed at the aesthetic-emotional component as well as the functional one. The design of forms and their contents, as statutes of social acts, allows us to investigate the tension on mass production that crosses seasons, trends, technicalities, and sterile determinisms. The design culture has always determined the formal qualities of objects as a sum of aesthetic characteristics functional and structural relationships that define a product as a coherent unit. The contribution proposes a reflection and a series of practical experiences of research on the form of advanced products. This form is understood as a kaleidoscope of relationships through the search for an identity, the desire for democratization, and between these two, the exploration of the aesthetic factor. The study of form also corresponds to the study of production processes, technological innovations, the definition of standards, distribution, advertising, the vicissitudes of taste and lifestyles. Specifically, we will investigate how the genesis of new forms for new meanings introduces a change in the relative innovative production techniques. It becomes, therefore, fundamental to investigate, through the reflections and the case studies exposed inside the contribution, also the new techniques of production and elaboration of the forms of the products, as new immanent and determining element inside the planning process.

Keywords: industrial design, product advanced design, mass productions, new meanings

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Authors: Niklas Panten, Eberhard Abele

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This paper presents a novel approach for real-time and near-optimal control of industrial smart grids by deep reinforcement learning (DRL). To achieve highly energy-efficient factory systems, the energetic linkage of machines, technical building equipment and the building itself is desirable. However, the increased complexity of the interacting sub-systems, multiple time-variant target values and stochastic influences by the production environment, weather and energy markets make it difficult to efficiently control the energy production, storage and consumption in the hybrid industrial smart grids. The studied deep reinforcement learning approach allows to explore the solution space for proper control policies which minimize a cost function. The deep neural network of the DRL agent is based on a multilayer perceptron (MLP), Long Short-Term Memory (LSTM) and convolutional layers. The agent is trained within multiple Modelica-based factory simulation environments by the Advantage Actor Critic algorithm (A2C). The DRL controller is evaluated by means of the simulation and then compared to a conventional, rule-based approach. Finally, the results indicate that the DRL approach is able to improve the control performance and significantly reduce energy respectively operating costs of industrial smart grids.

Keywords: industrial smart grids, energy efficiency, deep reinforcement learning, optimal control

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Authors: Szymon Kowieski, Zygmunt Mikno

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The study involved the optimization of process parameters during resistance spot welding of Al-coated martensitic boron steel 22MnB5, applied in hot stamping, performed using a programme with a multiple current impulse mode and a programme with variable pressure force. The aim of this research work was to determine the possibilities of a growth in welded joint strength and to identify the expansion of a welding lobe. The process parameters were adjusted on the basis of welding process simulation and confronted with experimental data. 22MnB5 steel is known for its tendency to obtain high hardness values in weld nuggets, often leading to interfacial failures (observed in the study-related tests). In addition, during resistance spot welding, many production-related factors can affect process stability, e.g. welding lobe narrowing, and lead to the deterioration of quality. Resistance spot welding performed using the above-named welding programme featuring 3 levels of force made it possible to achieve 82% of welding lobe extension. Joints made using the multiple current impulse program, where the total welding time was below 1.4s, revealed a change in a peeling mode (to full plug) and an increase in weld tensile shear strength of 10%.

Keywords: 22MnB5, hot stamping, interfacial fracture, resistance spot welding, simulation, single lap joint, welding lobe

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Authors: Xhaklina Cani, Ilirjan Malollari, Ismet Beqiraj, Lorina Lici

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Oil characterization is an essential step in the design, simulation, and optimization of refining facilities. To achieve optimal crude selection and processing decisions, a refiner must have exact information refer to crude oil quality. This includes crude oil TBP-curve as the main data for correct operation of refinery crude oil atmospheric distillation plants. Crude oil is typically characterized based on a distillation assay. This procedure is reasonably well-defined and is based on the representation of the mixture of actual components that boil within a boiling point interval by hypothetical components that boil at the average boiling temperature of the interval. The crude oil assay typically includes TBP distillation according to ASTM D-2892, which can characterize this part of oil that boils up to 400 C atmospheric equivalent boiling point. To model the yield curves obtained by physical distillation is necessary to compare the differences between the modelling and the experimental data. Most commercial use a different number of components and pseudo-components to represent crude oil. Laboratory tests include distillations, vapor pressures, flash points, pour points, cetane numbers, octane numbers, densities, and viscosities. The aim of the study is the drawing of true boiling curves for different crude oil resources in Albania and to compare the differences between the modeling and the experimental data for optimal characterization of crude oil.

Keywords: TBP distillation curves, crude oil, optimization, simulation

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Authors: Nagham Ismail, Djamel Ouahrani

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Thermal resilience in the built environment reflects the building's capacity to adapt to extreme climate changes. In hot climates, power outages in office buildings pose risks to the health and productivity of workers. Therefore, it is of interest to quantify the thermal resilience of office buildings by developing a user-friendly simplified model. This simplified model begins with creating an assessment metric of thermal resilience that measures the duration between the power outage and the point at which the thermal habitability condition is compromised, considering different power interruption times (morning, noon, and afternoon). In this context, energy simulations of an office building are conducted for Qatar's summer weather by changing different parameters that are related to the (i) wall characteristics, (ii) glazing characteristics, (iii) load, (iv) orientation and (v) air leakage. The simulation results are processed using SPSS to derive linear regression equations, aiding stakeholders in evaluating the performance of commercial buildings during different power interruption times. The findings reveal the significant influence of glazing characteristics on thermal resilience, with the morning power outage scenario posing the most detrimental impact in terms of the shortest duration before compromising thermal resilience.

Keywords: thermal resilience, thermal envelope, energy modeling, building simulation, thermal comfort, power disruption, extreme weather

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Authors: Hiroyuki Aoki

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The physical properties of polymer materials are dependent on the conformation and molecular motion of a polymer chain. Therefore, the structure and dynamic behavior of the single polymer chain have been the most important concerns in the field of polymer physics. However, it has been impossible to directly observe the conformation of the single polymer chain in a bulk medium. In the current work, the novel techniques to study the conformation and dynamics of a single polymer chain are proposed. Since a fluorescence method is extremely sensitive, the fluorescence microscopy enables the direct detection of a single molecule. However, the structure of the polymer chain as large as 100 nm cannot be resolved by conventional fluorescence methods because of the diffraction limit of light. In order to observe the single chains, we developed the labeling method of polymer materials with a photo-switchable dye and the super-resolution microscopy. The real-space conformational analysis of single polymer chains with the spatial resolution of 15-20 nm was achieved. The super-resolution microscopy enables us to obtain the three-dimensional coordinates; therefore, we succeeded the conformational analysis in three dimensions. The direct observation by the nanometric optical microscopy would reveal the detailed information on the molecular processes in the various polymer systems.

Keywords: polymer materials, single molecule, super-resolution techniques, conformation

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Authors: Hamza Saeed, Khurram Iqbal Ahmad Khan

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Effective information management is crucial for any construction project and its success. Primary areas of information generation are either the construction site or the design office. There are different types of information required at different stages of construction involving various stakeholders creating complexity. There is a need for effective management of information flows to reduce uncertainty creating complexity. Nature provides a unique perspective in terms of dealing with complexity, in particular, information complexity. System dynamics methodology provides tools and techniques to address complexity. It involves modeling and simulation techniques that help address complexity. Nature has been dealing with complex systems since its creation 4.5 billion years ago. It has perfected its system by evolution, resilience towards sudden changes, and extinction of unadaptable and outdated species that are no longer fit for the environment. Nature has been accommodating the changing factors and handling complexity forever. Humans have started to look at their natural counterparts for inspiration and solutions for their problems. This brings forth the possibility of using a biomimetics approach to improve the management practices used in the construction sector. Ants inhabit different habitats. Cataglyphis and Pogonomyrmex live in deserts, Leafcutter ants reside in rainforests, and Pharaoh ants are native to urban developments of tropical areas. Detailed studies have been done on fifty species out of fourteen thousand discovered. They provide the opportunity to study the interactions in diverse environments to generate collective behavior. Animals evolve to better adapt to their environment. The collective behavior of ants emerges from feedback through interactions among individuals, based on a combination of three basic factors: The patchiness of resources in time and space, operating cost, environmental stability, and the threat of rupture. If resources appear in patches through time and space, the response is accelerating and non-linear, and if resources are scattered, the response follows a linear pattern. If the acquisition of energy through food is faster than energy spent to get it, the default is to continue with an activity unless it is halted for some reason. If the energy spent is rather higher than getting it, the default changes to stay put unless activated. Finally, if the environment is stable and the threat of rupture is low, the activation and amplification rate is slow but steady. Otherwise, it is fast and sporadic. To further study the effects and to eliminate the environmental bias, the behavior of four different ant species were studied, namely Red Harvester ants (Pogonomyrmex Barbatus), Argentine ants (Linepithema Humile), Turtle ants (Cephalotes Goniodontus), Leafcutter ants (Genus: Atta). This study aims to improve the information system in the construction sector by providing a guideline inspired by nature with a systems-thinking approach, using system dynamics as a tool. Identified factors and their interdependencies were analyzed in the form of a causal loop diagram (CLD), and construction industry professionals were interviewed based on the developed CLD, which was validated with significance response. These factors and interdependencies in the natural system corresponds with the man-made systems, providing a guideline for effective use and flow of information.

Keywords: biomimetics, complex systems, construction management, information management, system dynamics

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Authors: Xiuguo Zhao, Xudong Ren, Chen Su, Xinxi Xu, Fu Niu, Lingshuai Meng

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The respiratory diseases such as asthma, emphysema and bronchitis are connected with the air pollution and the number of these diseases tends to increase, which may attribute to the toxic aerosol deposition in human upper respiratory tract or in the bifurcation of human lung. The therapy of these diseases mostly uses pharmaceuticals in the form of aerosol delivered into the human upper respiratory tract or the lung. Understanding of airflow structures in human upper respiratory tract plays a very important role in the analysis of the “filtering” effect in the pharynx/larynx and for obtaining correct air-particle inlet conditions to the lung. However, numerical simulation based CFD (Computational Fluid Dynamics) technology has its own advantage on studying airflow structure in human upper respiratory tract. In this paper, a representative human upper respiratory tract is built and the CFD technology was used to investigate the air movement characteristic in the human upper respiratory tract. The airflow movement characteristic, the effect of the airflow movement on the shear stress distribution and the probability of the wall injury caused by the shear stress are discussed. Experimentally validated computational fluid-aerosol dynamics results showed the following: the phenomenon of airflow separation appears near the outer wall of the pharynx and the trachea. The high velocity zone is created near the inner wall of the trachea. The airflow splits at the divider and a new boundary layer is generated at the inner wall of the downstream from the bifurcation with the high velocity near the inner wall of the trachea. The maximum velocity appears at the exterior of the boundary layer. The secondary swirls and axial velocity distribution result in the high shear stress acting on the inner wall of the trachea and bifurcation, finally lead to the inner wall injury. The enhancement of breathing intensity enhances the intensity of the shear stress acting on the inner wall of the trachea and the bifurcation. If human keep the high breathing intensity for long time, not only the ability for the transportation and regulation of the gas through the trachea and the bifurcation fall, but also result in the increase of the probability of the wall strain and tissue injury.

Keywords: airflow structure, computational fluid dynamics, human upper respiratory tract, wall shear stress, numerical simulation

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Authors: Neetu Manocha

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Image segmentation is a technique where a picture is parted into distinct parts having similar features which have a place with similar items. Various segmentation strategies have been proposed as of late by prominent analysts. But, after ultimate thorough research, the novelists have analyzed that generally, the old methods do not decrease the segmentation error rate. Then author finds the technique HIST-SI to decrease the segmentation error rates. In this technique, cluster-based and threshold-based segmentation techniques are merged together. After then, to improve the result of HIST-SI, the authors added the method of filtering and linking in this technique named Imp_HIST-SI to decrease the segmentation error rates. The goal of this research is to find a new technique to decrease the segmentation error rates and produce much better results than the HIST-SI technique. For testing the proposed technique, a dataset of Bhuvan – a National Geoportal developed and hosted by ISRO (Indian Space Research Organisation) is used. Experiments are conducted using Scikit-image & OpenCV tools of Python, and performance is evaluated and compared over various existing image segmentation techniques for several matrices, i.e., Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR).

Keywords: satellite image, image segmentation, edge detection, error rate, MSE, PSNR, HIST-SI, linking, filtering, imp_HIST-SI

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Authors: Shi-Yu Xu

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The top three risk factors that have contributed to building collapses during past earthquake events in Taiwan are: "irregular floor plans or elevations," "insufficient columns in single-bay buildings," and the "weak-story problem." Fortunately, these unsound structural characteristics can be directly identified from the floor plans. However, due to the vast number of old buildings, conducting manual inspections to identify these compromised structural features in all existing structures would be time-consuming and prone to human errors. This study aims to develop an algorithm that utilizes artificial intelligence techniques to automatically pinpoint the structural components within a building's floor plans. The obtained spatial information will be utilized to construct a digital structural model of the building. This information, particularly regarding the distribution of columns in the floor plan, can then be used to conduct preliminary seismic assessments of the building. The study employs various image processing and pattern recognition techniques to enhance detection efficiency and accuracy. The study enables a large-scale evaluation of structural vulnerability for numerous old buildings, providing ample time to arrange for structural retrofitting in those buildings that are at risk of significant damage or collapse during earthquakes.

Keywords: structural vulnerability detection, object recognition, seismic capacity assessment, old buildings, artificial intelligence

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Authors: Hema Bhardwaj, D. Srinivasa Rao

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Technology for analysing, processing, and extracting meaningful data from enormous and complicated datasets can be termed as "big data." The technique of big data mining and big data analysis is extremely helpful for business movements such as making decisions, building organisational plans, researching the market efficiently, improving sales, etc., because typical management tools cannot handle such complicated datasets. Special computational and statistical issues, such as measurement errors, noise accumulation, spurious correlation, and storage and scalability limitations, are brought on by big data. These unique problems call for new computational and statistical paradigms. This research paper offers an overview of the literature on big data mining, its process, along with problems and difficulties, with a focus on the unique characteristics of big data. Organizations have several difficulties when undertaking data mining, which has an impact on their decision-making. Every day, terabytes of data are produced, yet only around 1% of that data is really analyzed. The idea of the mining and analysis of data and knowledge discovery techniques that have recently been created with practical application systems is presented in this study. This article's conclusion also includes a list of issues and difficulties for further research in the area. The report discusses the management's main big data and data mining challenges.

Keywords: big data, data mining, data analysis, knowledge discovery techniques, data mining challenges

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Authors: Kwan Hee Han

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External environment of enterprise is rapidly changing majorly by global competition, cost reduction pressures, and new technology. In these situations, production scheduling function plays a critical role to meet customer requirements and to attain the goal of operational efficiency. It deals with short-term decision making in the production process of the whole supply chain. The major task of production scheduling is to seek a balance between customer orders and limited resources. In manufacturing companies, this task is so difficult because it should efficiently utilize resource capacity under the careful consideration of many interacting constraints. At present, many computerized software solutions have been utilized in many enterprises to generate a realistic production schedule to overcome the complexity of schedule generation. However, most production scheduling systems do not provide sufficient information about the validity of the generated schedule except limited statistics. Process mining only recently emerged as a sub-discipline of both data mining and business process management. Process mining techniques enable the useful analysis of a wide variety of processes such as process discovery, conformance checking, and bottleneck analysis. In this study, the performance of generated production schedule is evaluated by mining event log data of production scheduling software system by using the process mining techniques since every software system generates event logs for the further use such as security investigation, auditing and error bugging. An application of process mining approach is proposed for the validation of the goodness of production schedule generated by scheduling software systems in this study. By using process mining techniques, major evaluation criteria such as utilization of workstation, existence of bottleneck workstations, critical process route patterns, and work load balance of each machine over time are measured, and finally, the goodness of production schedule is evaluated. By using the proposed process mining approach for evaluating the performance of generated production schedule, the quality of production schedule of manufacturing enterprises can be improved.

Keywords: data mining, event log, process mining, production scheduling

Procedia PDF Downloads 266

Authors: Parul Sahu

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In recent years, seawater desalination has been emerged as a potential resource to circumvent water scarcity, especially in coastal regions. Among the various methods, thermal evaporation or distillation and membrane operations like Reverse Osmosis (RO) has been exploited at commercial scale. However, the energy cost and maintenance expenses associated with these processes remain high. In this context Freeze Desalination (FD), subjected to the availability of low cost refrigeration, offers an exciting alternative. Liquefied Natural Gas (LNG) regasification terminals provide an opportunity to utilize the refrigeration available with regasification of LNG. This work presents the conceptualization and development of a process scheme integrating the ice and hydrate based FD to the LNG regasification process. This integration overcomes the high energy demand associated with FD processes by utilizing the refrigeration associated with LNG regasification. An optimal process scheme was obtained by performing process simulation using ASPEN PLUS simulator. The results indicated the new proposed process requires only 1 kWh/m³ of energy with the utilization of maximum refrigeration. In addition, a sensitivity analysis was also performed to study the effect of various process parameters on water recovery and energy consumption for the proposed process. The results show that the energy consumption decreases by 30% with an increase in water recovery from 30% to 60%. However, due to operational limitations associated with ice and hydrate handling in seawater, the water recovery cannot be maximized but optimized. The proposed process can be potentially used to desalinate seawater in integration with LNG regasification terminal.

Keywords: freeze desalination, liquefied natural gas regasification, process simulation, refrigeration

Procedia PDF Downloads 116

Authors: Philipp Ruf, Massiwa Chabbi, Christoph Reich, Djaffar Ould-Abdeslam

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In recent years, convolutional neural networks (CNN) have demonstrated high performance in image analysis, but oftentimes, there is only structured data available regarding a specific problem. By interpreting structured data as images, CNNs can effectively learn and extract valuable insights from tabular data, leading to improved predictive accuracy and uncovering hidden patterns that may not be apparent in traditional structured data analysis. In applying a single neural network for analyzing multimodal data, e.g., both structured and unstructured information, significant advantages in terms of time complexity and energy efficiency can be achieved. Converting structured data into images and merging them with existing visual material offers a promising solution for applying CNN in multimodal datasets, as they often occur in a medical context. By employing suitable preprocessing techniques, structured data is transformed into image representations, where the respective features are expressed as different formations of colors and shapes. In an additional step, these representations are fused with existing images to incorporate both types of information. This final image is finally analyzed using a CNN.

Keywords: CNN, image processing, tabular data, mixed dataset, data transformation, multimodal fusion

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Authors: Hira Lal Gope, Hidekazu Fukai

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The aim of this study is to develop a system which can identify and sort peaberries automatically at low cost for coffee producers in developing countries. In this paper, the focus is on the classification of peaberries and normal coffee beans using image processing and machine learning techniques. The peaberry is not bad and not a normal bean. The peaberry is born in an only single seed, relatively round seed from a coffee cherry instead of the usual flat-sided pair of beans. It has another value and flavor. To make the taste of the coffee better, it is necessary to separate the peaberry and normal bean before green coffee beans roasting. Otherwise, the taste of total beans will be mixed, and it will be bad. In roaster procedure time, all the beans shape, size, and weight must be unique; otherwise, the larger bean will take more time for roasting inside. The peaberry has a different size and different shape even though they have the same weight as normal beans. The peaberry roasts slower than other normal beans. Therefore, neither technique provides a good option to select the peaberries. Defect beans, e.g., sour, broken, black, and fade bean, are easy to check and pick up manually by hand. On the other hand, the peaberry pick up is very difficult even for trained specialists because the shape and color of the peaberry are similar to normal beans. In this study, we use image processing and machine learning techniques to discriminate the normal and peaberry bean as a part of the sorting system. As the first step, we applied Deep Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) as machine learning techniques to discriminate the peaberry and normal bean. As a result, better performance was obtained with CNN than with SVM for the discrimination of the peaberry. The trained artificial neural network with high performance CPU and GPU in this work will be simply installed into the inexpensive and low in calculation Raspberry Pi system. We assume that this system will be used in under developed countries. The study evaluates and compares the feasibility of the methods in terms of accuracy of classification and processing speed.

Keywords: convolutional neural networks, coffee bean, peaberry, sorting, support vector machine

Procedia PDF Downloads 132

Authors: Nada Mohamed, Abdel Aziz Mohamed

Abstract:

Climate change in coastal areas is a global issue that can be felt on local scale and will be around for decades and centuries to come to an end; it also has critical risks on the city’s economy, communities, and the natural environment. One of these changes that cause a huge risk on coastal cities is the sea level rise (SLR). SLR is a result of scarcity and reduction in global environmental system. The main cause of climate change and global warming is the countries with high development index (HDI) as Japan and Germany while the medium and low HDI countries as Egypt does not have enough awareness and advanced tactics to adapt with this changes that destroy urban areas and cause loss in land and economy. This is why Climate Resilience is one of the UN sustainable development goals 2030, which is calling for actions to strengthen climate change resilience through mitigation and adaptation. For many reasons, adaptation has received less attention than mitigation and it is only recently that adaptation has become a focal global point of attention. This adaption can be achieved through some actions such as upgrading the use and the design of the land, adjusting business and activities of people, and increasing community understanding of climate risks. To reach the adaption goals, and we have to apply a strategic pathway to Climate Resilience, which is the Urban Bioregionalism Paradigm. Resiliency has been framed as persistence, adaptation, and transformation. Climate Resilience decision support system includes a visualization platform where ecological, social, and economic information can be viewed alongside with specific geographies that's why Urban Bioregionalism is a socio-ecological system which is defined as a paradigm that has potential to help move social attitudes toward environmental understanding and deepen human-environment connections within ecological development. The research aim is to achieve an adaptive integrated urban development model throughout the analyses of tactics and strategies that can be used to adapt urban areas and coastal communities to the challenges of climate changes especially SLR and also simulation model using advanced technological software for a coastal city corridor to elaborates the suitable strategy to apply.

Keywords: climate resilience, sea level rise, SLR, coastal resilience, adaptive development simulation

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Authors: Mohsen Azarmjoo, Navid Sharifi, Zahra Alikhani Koopaei

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This article aims to conserve energy and optimize the performance of solar water heaters using modern modeling systems. In this study, a large-scale solar water heater is modeled using an AVR microcontroller, which is a digital processor from the AVR microcontroller family. This mechatronic system will be used to analyze the performance and design of solar collectors, with the ultimate goal of improving the efficiency of the system being used. The findings of this research provide insights into optimizing the performance of solar water heaters. By manipulating the arrangement of solar panels and controlling the water flow through them using the AVR microcontroller, researchers can identify the optimal configurations and operational protocols to achieve the desired temperature and flow conditions. These findings can contribute to the development of more efficient and sustainable heating and cooling systems. This article investigates the optimization of solar water heater performance. It examines the impact of solar panel layout on system efficiency and explores methods of controlling water flow to achieve the desired temperature and flow conditions. The results of this research contribute to the development of more sustainable heating and cooling systems that rely on renewable energy sources.

Keywords: energy conservation, solar water heaters, solar cooling, simulation, mechatronics

Procedia PDF Downloads 59

Authors: N. Ould cherchali, M. S. Boucherit, L. Barazane, A. Morsli

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Photovoltaic power is widely used to supply isolated or unpopulated areas (lighting, pumping, etc.). Great advantage is that this source is inexhaustible, it offers great safety in use and it is clean. But the dynamic models used to describe a photovoltaic system are complicated and nonlinear and due to nonlinear I-V and P–V characteristics of photovoltaic generators, a maximum power point tracking technique (MPPT) is required to maximize the output power. In this paper, two online techniques of maximum power point tracking using robust controller for photovoltaic systems are proposed, the first technique use fuzzy logic controller (FLC) and the second use sliding mode controller (SMC) for photovoltaic systems. The two maximum power point tracking controllers receive the partial derivative of power as inputs, and the output is the duty cycle corresponding to maximum power. A Photovoltaic generator with Boost converter is developed using MATLAB/Simulink to verify the preferences of the proposed techniques. SMC technique provides a good tracking speed in fast changing irradiation and when the irradiation changes slowly or is constant the panel power of FLC technique presents a much smoother signal with less fluctuations.

Keywords: fuzzy logic controller, maximum power point, photovoltaic system, tracker, sliding mode controller

Procedia PDF Downloads 531