Search results for: fuzzy mathematical programming

Authors: Shahrokh Barati

Abstract:

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

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

Procedia PDF Downloads 451

Authors: K. -H. Yang

Abstract:

Supplier selection problem is one of the important issues of supply chain problems. Two categories of methodologies include qualitative and quantitative approaches which can be applied to supplier selection problems. However, due to the complexities of the problem and lacking of reliable and quantitative data, qualitative approaches are more than quantitative approaches. This study considers operational cost and supplier’s reliability factor and solves the problem by using a quantitative approach. A mixed integer programming model is the primary analytic tool. Analyses of different scenarios with variable cost and reliability structures show that the effectiveness of this approach to the supplier selection problem.

Keywords: mixed integer programming, quantitative approach, supplier’s reliability, supplier selection

Procedia PDF Downloads 366

Authors: Manuel I. Capel, Antonio Tomeu, Alberto Salguero

Abstract:

Tumor growth from a transformed cancer-cell up to a clinically apparent mass spans through a range of spatial and temporal magnitudes. Through computer simulations, Cellular Automata (CA) can accurately describe the complexity of the development of tumors. Tumor development prognosis can now be made -without making patients undergo through annoying medical examinations or painful invasive procedures- if we develop appropriate CA-based software tools. In silico testing mainly refers to Computational Biology research studies of application to clinical actions in Medicine. To establish sound computer-based models of cellular behavior, certainly reduces costs and saves precious time with respect to carrying out experiments in vitro at labs or in vivo with living cells and organisms. These aim to produce scientifically relevant results compared to traditional in vitro testing, which is slow, expensive, and does not generally have acceptable reproducibility under the same conditions. For speeding up computer simulations of cellular models, specific literature shows recent proposals based on the CA approach that include advanced techniques, such the clever use of supporting efficient data structures when modeling with deterministic stochastic cellular automata. Multiparadigm and multiscale simulation of tumor dynamics is just beginning to be developed by the concerned research community. The use of stochastic cellular automata (SCA), whose parallel programming implementations are open to yield a high computational performance, are of much interest to be explored up to their computational limits. There have been some approaches based on optimizations to advance in multiparadigm models of tumor growth, which mainly pursuit to improve performance of these models through efficient memory accesses guarantee, or considering the dynamic evolution of the memory space (grids, trees,…) that holds crucial data in simulations. In our opinion, the different optimizations mentioned above are not decisive enough to achieve the high performance computing power that cell-behavior simulation programs actually need. The possibility of using multicore and GPU parallelism as a promising multiplatform and framework to develop new programming techniques to speed-up the computation time of simulations is just starting to be explored in the few last years. This paper presents a model that incorporates parallel processing, identifying the synchronization necessary for speeding up tumor growth simulations implemented in Java and C++ programming environments. The speed up improvement that specific parallel syntactic constructs, such as executors (thread pools) in Java, are studied. The new tumor growth parallel model is proved using implementations with Java and C++ languages on two different platforms: chipset Intel core i-X and a HPC cluster of processors at our university. The parallelization of Polesczuk and Enderling model (normally used by researchers in mathematical oncology) proposed here is analyzed with respect to performance gain. We intend to apply the model and overall parallelization technique presented here to solid tumors of specific affiliation such as prostate, breast, or colon. Our final objective is to set up a multiparadigm model capable of modelling angiogenesis, or the growth inhibition induced by chemotaxis, as well as the effect of therapies based on the presence of cytotoxic/cytostatic drugs.

Keywords: cellular automaton, tumor growth model, simulation, multicore and manycore programming, parallel programming, high performance computing, speed up

Procedia PDF Downloads 226

Authors: Agung Budi Muljono, I Made Ginarsa, I Made Ari Nrartha

Abstract:

A large-scale power system (LSPS) consists of two or more sub-systems connected by inter-connecting transmission. Loading pattern on an LSPS always changes from time to time and varies depend on consumer need. The serious instability problem is appeared in an LSPS due to load fluctuation in all of the bus. Adaptive neuro-fuzzy inference system (ANFIS)-based power system stabilizer (PSS) is presented to cover the stability problem and to enhance the stability of an LSPS. The ANFIS control is presented because the ANFIS control is more effective than Mamdani fuzzy control in the computation aspect. Simulation results show that the presented PSS is able to maintain the stability by decreasing peak overshoot to the value of −2.56 × 10−5 pu for rotor speed deviation Δω2−3. The presented PSS also makes the settling time to achieve at 3.78 s on local mode oscillation. Furthermore, the presented PSS is able to improve the peak overshoot and settling time of Δω3−9 to the value of −0.868 × 10−5 pu and at the time of 3.50 s for inter-area oscillation.

Keywords: ANFIS, large-scale, power system, PSS, stability enhancement

Procedia PDF Downloads 293

Authors: Roberto Cabezas H

Abstract:

The current work shows the methodology developed to create narrative lighting spaces for the multimedia performance piece 'cluster: the vanished paradise.' This empirical research is focused on exploring unconventional roles for machines in subjective creative processes, by delving into the semantics of data and machine intelligence algorithms in hybrid technological, creative contexts to expand epistemic domains trough human-machine cooperation. The creative process in scenic and performing arts is guided mostly by intuition; from that idea, we developed an approach to embed collective intuition in computational creative systems, by joining the properties of Generative Adversarial Networks (GAN’s) and Fuzzy Clustering based on a semi-supervised data creation and analysis pipeline. The model makes use of GAN’s to learn from phenomenological data (data generated from experience with lighting scenography) and algorithmic design data (augmented data by procedural design methods), fuzzy logic clustering is then applied to artificially created data from GAN’s to define narrative transitions built on membership index; this process allowed for the creation of simple and complex spaces with expressive capabilities based on position and light intensity as the parameters to guide the narrative. Hybridization comes not only from the human-machine symbiosis but also on the integration of different techniques for the implementation of the aided design system. Machine intelligence tools as proposed in this work are well suited to redefine collaborative creation by learning to express and expand a conglomerate of ideas and a wide range of opinions for the creation of sensory experiences. We found in GAN’s and Fuzzy Logic an ideal tool to develop new computational models based on interaction, learning, emotion and imagination to expand the traditional algorithmic model of computation.

Keywords: fuzzy clustering, generative adversarial networks, human-machine cooperation, hybrid collective data, multimedia performance

Procedia PDF Downloads 129

Authors: Genady Grabarnik, Serge Yaskolko

Abstract:

Generative Artificial Intelligence (AI) has significantly expanded its applicability with the incorporation of Large Language Models (LLMs) and become a technology with promise to automate some areas that were very difficult to automate before. The paper describes the introduction of generative Artificial Intelligence into Introductory Computer and Data Science courses and analysis of effect of such introduction. The generative Artificial Intelligence is incorporated in the educational process two-fold: For the instructors, we create templates of prompts for generation of tasks, and grading of the students work, including feedback on the submitted assignments. For the students, we introduce them to basic prompt engineering, which in turn will be used for generation of test cases based on description of the problems, generating code snippets for the single block complexity programming, and partitioning into such blocks of an average size complexity programming. The above-mentioned classes are run using Large Language Models, and feedback from instructors and students and courses’ outcomes are collected. The analysis shows statistically significant positive effect and preference of both stakeholders.

Keywords: introductory computer and data science education, generative AI, large language models, application of LLMS to computer and data science education

Procedia PDF Downloads 45

Authors: N. Ibrahim-Rassoul, A. Kessi, E. K. Si-Ahmed, N. Djilali, J. Legrand

Abstract:

The static and dynamic formation of liquid water bridges is analyzed using a combination of visualization experiments in a microchannel with a mathematical model. This paper presents experimental and theoretical findings of water plug/capillary bridge formation in a 250 μm squared microchannel. The approach combines mathematical and numerical modeling with experimental visualization and measurements. The generality of the model is also illustrated for flow conditions encountered in manipulation of polymeric materials and formation of liquid bridges between patterned surfaces. The predictions of the model agree favorably the observations as well as with the experimental recordings.

Keywords: green energy, mathematical modeling, fuel cell, water plug, gas diffusion layer, surface of revolution

Procedia PDF Downloads 504

Authors: Zahid Ullah, Atlas Khan

Abstract:

The rapid advancements in data collection, storage, and processing capabilities have led to an explosion of data in various domains. In this era of big data, mathematical sciences play a crucial role in uncovering valuable insights and driving informed decision-making through data analytics. The purpose of this abstract is to present the latest advances in mathematical sciences and their application in harnessing the power of data analytics. This abstract highlights the interdisciplinary nature of data analytics, showcasing how mathematics intersects with statistics, computer science, and other related fields to develop cutting-edge methodologies. It explores key mathematical techniques such as optimization, mathematical modeling, network analysis, and computational algorithms that underpin effective data analysis and interpretation. The abstract emphasizes the role of mathematical sciences in addressing real-world challenges across different sectors, including finance, healthcare, engineering, social sciences, and beyond. It showcases how mathematical models and statistical methods extract meaningful insights from complex datasets, facilitating evidence-based decision-making and driving innovation. Furthermore, the abstract emphasizes the importance of collaboration and knowledge exchange among researchers, practitioners, and industry professionals. It recognizes the value of interdisciplinary collaborations and the need to bridge the gap between academia and industry to ensure the practical application of mathematical advancements in data analytics. The abstract highlights the significance of ongoing research in mathematical sciences and its impact on data analytics. It emphasizes the need for continued exploration and innovation in mathematical methodologies to tackle emerging challenges in the era of big data and digital transformation. In summary, this abstract sheds light on the advances in mathematical sciences and their pivotal role in unveiling the power of data analytics. It calls for interdisciplinary collaboration, knowledge exchange, and ongoing research to further unlock the potential of mathematical methodologies in addressing complex problems and driving data-driven decision-making in various domains.

Keywords: mathematical sciences, data analytics, advances, unveiling

Procedia PDF Downloads 71

Authors: O. Poleshchuk, E. Komarov

Abstract:

In this paper, semantic spaces with the properties of completeness and orthogonality (complete orthogonal semantic spaces) were chosen as models of expert evaluations. As the theoretical and practical studies have shown all the properties of complete orthogonal semantic spaces correspond to the thinking activity of experts that is why these semantic spaces were chosen for modeling. Two methods of construction such spaces were proposed. Models of comparative and fuzzy cluster analysis of expert evaluations were developed. The practical application of the developed methods has demonstrated their viability and validity.

Keywords: expert evaluation, comparative analysis, fuzzy cluster analysis, theoretical and practical studies

Procedia PDF Downloads 515

Authors: Arup K. Sarma, Jayshree Hazarika

Abstract:

The North-eastern part of India, which receives heavier rainfall than other parts of the subcontinent, is of great concern now-a-days with regard to climate change. High intensity rainfall for short duration and longer dry spell, occurring due to impact of climate change, affects river morphology too. In the present study, an attempt is made to delineate the North-Eastern region of India into some homogeneous clusters based on the Fuzzy Clustering concept and to compare the resulting clusters obtained by using conventional methods and non conventional methods of clustering. The concept of clustering is adapted in view of the fact that, impact of climate change can be studied in a homogeneous region without much variation, which can be helpful in studies related to water resources planning and management. 10 IMD (Indian Meteorological Department) stations, situated in various regions of the North-east, have been selected for making the clusters. The results of the Fuzzy C-Means (FCM) analysis show different clustering patterns for different conditions. From the analysis and comparison it can be concluded that non conventional method of using GCM data is somehow giving better results than the others. However, further analysis can be done by taking daily data instead of monthly means to reduce the effect of standardization.

Keywords: climate change, conventional and nonconventional methods of clustering, FCM analysis, homogeneous regions

Procedia PDF Downloads 367

Authors: Amirhossein Khazali, Mohsen Kalantar

Abstract:

Energy storage units can play an important role to provide an economic and secure operation of future energy systems. In this paper, a stochastic energy and reserve market clearing scheme is presented considering storage energy units. The approach is proposed to deal with stochastic and non-dispatchable renewable sources with a high level of penetration in the energy system. A two stage stochastic programming scheme is formulated where in the first stage the energy market is cleared according to the forecasted amount of wind generation and demands and in the second stage the real time market is solved according to the assumed scenarios.

Keywords: energy and reserve market, energy storage device, stochastic programming, wind generation

Procedia PDF Downloads 556

Authors: Mohammed Abubakar Ndanusa, A. A. Hassan, R. W. Gimba, A. M. Alfa, M. T. Abari

Abstract:

This study investigated the Impact of Mathematical Modeling on Mathematics Achievement, Attitude and Interest of Pre-Service Teachers in Niger States, Nigeria. It was an attempt to ease students’ difficulties in comprehending mathematics. The study used randomized pretest, posttest control group design. Two Colleges of Education were purposively selected from Niger State with a sample size of eighty-four 84 students. Three research instruments used are Mathematical Modeling Achievement Test (MMAT), Attitudes Towards Mathematical Modeling Questionnaire (ATMMQ) and Mathematical Modeling Students Interest Questionnaire (MMSIQ). Pearson Product Moment Correlation (PPMC) formula was used for MMAT and Alpha Cronbach was used for ATMMQ and MMSIQ to determine their reliability coefficient and the values the following values were obtained respectively 0.76, 0.75 and 0.73. Independent t-test statistics was used to test hypothesis One while Mann Whitney U-test was used to test hypothesis Two and Three. Findings revealed that students taught Mathematics using Mathematical Modeling performed better than their counterparts taught using lecture method. However, there was a significant difference in the attitude and interest of pre-service mathematics teachers after being exposed to mathematical modeling. The strategy, therefore, was recommended to be used by Mathematics teachers with a view to improving students’ attitude and interest towards Mathematics. Also, modeling should be taught at NCE level in order to prepare pre-service teachers towards real task in the field of Mathematics.

Keywords: achievement, attitude, interest, mathematical modeling, pre-service teachers

Procedia PDF Downloads 288

Authors: Bharath R., Tharun Sai N., Bhuvan G.

Abstract:

This project focuses on developing a Parallel Computing method aimed at optimizing matrix multiplication through GPU acceleration. Addressing algorithmic challenges, GPU programming intricacies, and integration issues, the project aims to enhance efficiency and scalability. The methodology involves algorithm design, GPU programming, and optimization techniques. Future plans include advanced optimizations, extended functionality, and integration with high-level frameworks. User engagement is emphasized through user-friendly interfaces, open- source collaboration, and continuous refinement based on feedback. The project's impact extends to significantly improving matrix multiplication performance in scientific computing and machine learning applications.

Keywords: matrix multiplication, parallel processing, cuda, performance boost, neural networks

Procedia PDF Downloads 33

Authors: José Neves, M. Rosário Martins, Fátima Candeias, Diana Ferreira, Sílvia Arantes, Júlio Cruz-Morais, Guida Gomes, Joaquim Macedo, António Abelha, Henrique Vicente

Abstract:

Some plants of genus Schinus have been used in the folk medicine as topical antiseptic, digestive, purgative, diuretic, analgesic or antidepressant, and also for respiratory and urinary infections. Chemical composition of essential oils of S. molle and S. terebinthifolius had been evaluated and presented high variability according with the part of the plant studied and with the geographic and climatic regions. The pharmacological properties, namely antimicrobial, anti-tumoural and anti-inflammatory activities are conditioned by chemical composition of essential oils. Taking into account the difficulty to infer the pharmacological properties of Schinus essential oils without hard experimental approach, this work will focus on the development of a decision support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centered on Artificial Neural Networks and the respective Degree-of-Confidence that one has on such an occurrence.

Keywords: artificial neuronal networks, essential oils, knowledge representation and reasoning, logic programming, Schinus molle L., Schinus terebinthifolius Raddi

Procedia PDF Downloads 527

Authors: Kalliopi Kanaki, Michael Kalogiannakis

Abstract:

Nowadays, in the digital era, the early acquisition of basic programming skills and knowledge is encouraged, as it facilitates students’ exposure to computational thinking and empowers their creativity, problem-solving skills, and cognitive development. More and more researchers and educators investigate the introduction of computational thinking in K-12 since it is expected to be a fundamental skill for everyone by the middle of the 21st century, just like reading, writing and arithmetic are at the moment. In this paper, a doctoral research in the process is presented, which investigates the infusion of computational thinking into science curriculum in early childhood education. The whole attempt aims to develop young children’s computational thinking by introducing them to the fundamental concepts of object-oriented programming in an enjoyable, yet educational framework. The backbone of the research is the digital environment PhysGramming (an abbreviation of Physical Science Programming), which provides children the opportunity to create their own digital games, turning them from passive consumers to active creators of technology. PhysGramming deploys an innovative hybrid schema of visual and text-based programming techniques, with emphasis on object-orientation. Through PhysGramming, young students are familiarized with basic object-oriented programming concepts, such as classes, objects, and attributes, while, at the same time, get a view of object-oriented programming syntax. Nevertheless, the most noteworthy feature of PhysGramming is that children create their own digital games within the context of physical science courses, in a way that provides familiarization with the basic principles of object-oriented programming and computational thinking, even though no specific reference is made to these principles. Attuned to the ethical guidelines of educational research, interventions were conducted in two classes of second grade. The interventions were designed with respect to the thematic units of the curriculum of physical science courses, as a part of the learning activities of the class. PhysGramming was integrated into the classroom, after short introductory sessions. During the interventions, 6-7 years old children worked in pairs on computers and created their own digital games (group games, matching games, and puzzles). The authors participated in these interventions as observers in order to achieve a realistic evaluation of the proposed educational framework concerning its applicability in the classroom and its educational and pedagogical perspectives. To better examine if the objectives of the research are met, the investigation was focused on six criteria; the educational value of PhysGramming, its engaging and enjoyable characteristics, its child-friendliness, its appropriateness for the purpose that is proposed, its ability to monitor the user’s progress and its individualizing features. In this paper, the functionality of PhysGramming and the philosophy of its integration in the classroom are both described in detail. Information about the implemented interventions and the results obtained is also provided. Finally, several limitations of the research conducted that deserve attention are denoted.

Keywords: computational thinking, early childhood education, object-oriented programming, physical science courses

Procedia PDF Downloads 110

Authors: Jialin Wu, Zhiwei Lian, Jieyu Tang, Jingyun Shen

Abstract:

Tornado is a serious and unpredictable natural disaster, which has an important impact on people's production and life. The probability of being hit by tornadoes in China was analyzed considering the principles of tornado formation. Then some suggestions on layout and shapes for newly-built buildings were provided combined with the characteristics of tornado wind fields. Fuzzy clustering and inverse closeness methods were used to evaluate the probability levels of tornado risks in various provinces based on classification and ranking. GIS was adopted to display the results. Finally, wind field single-vortex tornado was studied to discuss the optimized design of rural low-rise houses in Yancheng, Jiangsu as an example. This paper may provide enough data to support building and urban design in some specific regions.

Keywords: tornado probability, computational fluid dynamics, fuzzy mathematics, optimal design

Procedia PDF Downloads 117

Authors: S. Valarmathi, P. B. Harathi, R. Sridhar, S. Balasubramanian

Abstract:

Machine learning tools in medical diagnosis is increasing due to the improved effectiveness of classification and recognition systems to help medical experts in diagnosing breast cancer. In this study, ID3 chooses the splitting attribute with the highest gain in information, where gain is defined as the difference between before the split versus after the split. It is applied for age, location, taluk, stage, year, period, martial status, treatment, heredity, sex, and habitat against Very Serious (VS), Very Serious Moderate (VSM), Serious (S) and Not Serious (NS) to calculate the gain of information. The ranked histogram gives the gain of each field for the breast cancer data. The doctors use TNM staging which will decide the risk level of the breast cancer and play an important decision making field in fuzzy logic for perception based measurement. Spatial risk area (taluk) of the breast cancer is calculated. Result clearly states that Coimbatore (North and South) was found to be risk region to the breast cancer than other areas at 20% criteria. Weighted value of taluk was compared with criterion value and integrated with Map Object to visualize the results. ID3 algorithm shows the high breast cancer risk regions in the study area. The study has outlined, discussed and resolved the algorithms, techniques / methods adopted through soft computing methodology like ID3 algorithm for prognostic decision making in the seriousness of the breast cancer.

Keywords: ID3 algorithm, breast cancer, fuzzy logic, MATLAB

Procedia PDF Downloads 503

Authors: Said Fares, Mary Fares

Abstract:

It is well documented that introductory computer programming courses are difficult and that failure rates are high. The aim of this project was to reduce the high failure and withdrawal rates in learning to program. This paper presents a number of changes in module organization and instructional delivery system in teaching CS1. Daily out of class help sessions and tutoring services were applied, interactive lectures and laboratories, online resources, and timely feedback were introduced. Five years of data of 563 students in 21 sections was collected and analyzed. The primary results show that the failure and withdrawal rates were cut by more than half. Student surveys indicate a positive evaluation of the modified instructional approach, overall satisfaction with the course and consequently, higher success and retention rates.

Keywords: failure rate, interactive learning, student engagement, CS1

Procedia PDF Downloads 293

Authors: Manish Jain, Dinesh Gopalani

Abstract:

Growing dependency of mankind on software technology increases the need for thorough testing of the software applications and automated testing techniques that support testing activities. We have outlined our testing strategy for performing various types of automated testing of Java applications using AspectJ which has become the de-facto standard for Aspect Oriented Programming (AOP). Likewise JUnit, a unit testing framework is the most popular Java testing tool. In this paper, we have evaluated our proposed AOP approach for automated testing and JUnit on various parameters. First we have provided the similarity between the two approaches and then we have done a detailed comparison of the two testing techniques on factors like lines of testing code, learning curve, testing of private members etc. We established that our AOP testing approach using AspectJ has got several advantages and is thus particularly more effective than JUnit.

Keywords: aspect oriented programming, AspectJ, aspects, JU-nit, software testing

Procedia PDF Downloads 308

Authors: Felicitas Pielsticker, Christoph Pielsticker, Ingo Witzke

Abstract:

Neurological findings provide foundational results for many different disciplines. In this article we want to discuss these with a special focus on mathematics education. The intention is to make neuroscience research useful for the description of cognitive mathematical learning processes. A key issue of mathematics education is that students often behave as if their mathematical knowledge is constructed in isolated compartments with respect to the specific context of the original learning situation; supporting students to link these compartments to form a coherent mathematical society of mind is a fundamental task not only for mathematics teachers. This aspect goes hand in hand with the question if there is such a thing as abstract general mathematical knowledge detached from concrete reality. Educational Neuroscience may give answers to the question why students develop their mathematical knowledge in isolated subjective domains of experience and if it is generally possible to think in abstract terms. To address these questions, we will provide examples from different fields of mathematics education e.g. students’ development and understanding of the general concept of variables or the mathematical notion of universal proofs. We want to discuss these aspects in the reflection of functional studies which elucidate the role of specific brain regions in mathematical learning processes. In doing this the paper addresses concept formation processes of students in the mathematics classroom and how to support them adequately considering the results of (educational) neuroscience.

Keywords: brain regions, concept formation processes in mathematics education, proofs, teaching-learning processes

Procedia PDF Downloads 129

Authors: Fatéma Zahra Benchara, Mohamed Youssfi, Omar Bouattane, Hassan Ouajji, Mohamed Ouadi Bensalah

Abstract:

The aim of this paper is to present a distributed implementation of the Type-2 Fuzzy algorithm in a parallel and distributed computing environment based on mobile agents. The proposed algorithm is assigned to be implemented on a SPMD (Single Program Multiple Data) architecture which is based on cooperative mobile agents as AVPE (Agent Virtual Processing Element) model in order to improve the processing resources needed for performing the big data image segmentation. In this work we focused on the application of this algorithm in order to process the big data MRI (Magnetic Resonance Images) image of size (n x m). It is encapsulated on the Mobile agent team leader in order to be split into (m x n) pixels one per AVPE. Each AVPE perform and exchange the segmentation results and maintain asynchronous communication with their team leader until the convergence of this algorithm. Some interesting experimental results are obtained in terms of accuracy and efficiency analysis of the proposed implementation, thanks to the mobile agents several interesting skills introduced in this distributed computational model.

Keywords: distributed type-2 fuzzy algorithm, image processing, mobile agents, parallel and distributed computing

Procedia PDF Downloads 411

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

Abstract:

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

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

Procedia PDF Downloads 403

Authors: Jian Zheng, Yoshiyasu Takahashi, Yuichi Kobayashi, Tatsuhiro Sato

Abstract:

In the study, we present a conceptual framework for developing a scheduling system that can generate self-explanatory and easy-understanding schedules. To this end, a user interface is conceived to help planners record factors that are considered crucial in scheduling, as well as internal and external sources relating to such factors. A hybrid approach combining machine learning and constraint programming is developed to generate schedules and the corresponding factors, and accordingly display them on the user interface. Effects of the proposed system on scheduling are discussed, and it is expected that scheduling efficiency and system understandability will be improved, compared with previous scheduling systems.

Keywords: constraint programming, factors considered in scheduling, machine learning, scheduling system

Procedia PDF Downloads 308

Authors: D. Calogine, O. Chau, S. Dotti, O. Ramiarinjanahary, P. Rasoavonjy, F. Tovondahiniriko

Abstract:

Mafate is a natural circus in the north-western part of Reunion Island, without an electrical grid and road network. A micro-grid concept is being experimented in this area, composed of a photovoltaic production combined with electrochemical batteries, in order to meet the local population for self-consumption of electricity demands. This work develops a discrete model as well as a stochastic model in order to reach an optimal equilibrium between production and consumptions for a cluster of houses. The management of the energy power leads to a large linearized programming system, where the time interval of interest is 24 hours The experimental data are solar production, storage energy, and the parameters of the different electrical devices and batteries. The unknown variables to evaluate are the consumptions of the various electrical services, the energy drawn from and stored in the batteries, and the inhabitants’ planning wishes. The objective is to fit the solar production to the electrical consumption of the inhabitants, with an optimal use of the energies in the batteries by satisfying as widely as possible the users' planning requirements. In the discrete model, the different parameters and solutions of the linear programming system are deterministic scalars. Whereas in the stochastic approach, the data parameters and the linear programming solutions become random variables, then the distributions of which could be imposed or established by estimation from samples of real observations or from samples of optimal discrete equilibrium solutions.

Keywords: photovoltaic production, power consumption, battery storage resources, random variables, stochastic modeling, estimations of probability distributions, mixed integer linear programming, smart micro-grid, self-consumption of electricity.

Procedia PDF Downloads 96

Authors: Hamed K. Esfahani, Bithin Datta

Abstract:

Transport of reactive chemical contaminant species in groundwater aquifers is a complex and highly non-linear physical and geochemical process especially for real life scenarios. Simulating this transport process involves solving complex nonlinear equations and generally requires huge computational time for a given aquifer study area. Development of optimal remediation strategies in aquifers may require repeated solution of such complex numerical simulation models. To overcome this computational limitation and improve the computational feasibility of large number of repeated simulations, Genetic Programming based trained surrogate models are developed to approximately simulate such complex transport processes. Transport process of acid mine drainage, a hazardous pollutant is first simulated using a numerical simulated model: HYDROGEOCHEM 5.0 for a contaminated aquifer in a historic mine site. Simulation model solution results for an illustrative contaminated aquifer site is then approximated by training and testing a Genetic Programming (GP) based surrogate model. Performance evaluation of the ensemble GP models as surrogate models for the reactive species transport in groundwater demonstrates the feasibility of its use and the associated computational advantages. The results show the efficiency and feasibility of using ensemble GP surrogate models as approximate simulators of complex hydrogeologic and geochemical processes in a contaminated groundwater aquifer incorporating uncertainties in historic mine site.

Keywords: geochemical transport simulation, acid mine drainage, surrogate models, ensemble genetic programming, contaminated aquifers, mine sites

Procedia PDF Downloads 262

Authors: Leon Pan

Abstract:

The first programming course within post-secondary education has long been recognized as a challenging endeavor for both educators and students alike. Historically, these courses have exhibited high failure rates and a notable number of dropouts. Instructors often lament students' lack of effort in their coursework, and students often express frustration that the teaching methods employed are not effective. Drawing inspiration from the successful principles of Extreme Programming, this study introduces an approach—the Extremes-based teaching model — aimed at enhancing the teaching of introductory programming courses. To empirically determine the effectiveness of the model, a comparison was made between a section taught using the extreme-based model and another utilizing traditional teaching methods. Notably, the extreme-based teaching class required students to work collaboratively on projects while also demanding continuous assessment and performance enhancement within groups. This paper details the application of the extreme-based model within the post-secondary online classroom context and presents the compelling results that emphasize its effectiveness in advancing the teaching and learning experiences. The extreme-based model led to a significant increase of 13.46 points in the weighted total average and a commendable 10% reduction in the failure rate.

Keywords: extreme-based teaching model, innovative pedagogical methods, project-based learning, team-based learning

Procedia PDF Downloads 46

Authors: Wook-Won Kim, Je-Seok Shin, Jin-O Kim

Abstract:

This paper propose the method for optimal scheduling for battery energy storage system with reliability constraint of energy storage system in reliability aspect. The optimal scheduling problem is solved by dynamic programming with proposed transition matrix. Proposed optimal scheduling method guarantees the minimum fuel cost within specific reliability constraint. For evaluating proposed method, the timely capacity outage probability table (COPT) is used that is calculated by convolution of probability mass function of each generator. This study shows the result of optimal schedule of energy storage system.

Keywords: energy storage system (ESS), optimal scheduling, dynamic programming, reliability constraints

Procedia PDF Downloads 385

Authors: Selin Guney, Andres Riquelme

Abstract:

Among the main purposes of optimal and efficient forecasts of agricultural commodity prices is to guide the firms to advance the economic decision making process such as planning business operations and marketing decisions. Governments are also the beneficiaries and suppliers of agricultural price forecasts. They use this information to establish a proper agricultural policy, and hence, the forecasts affect social welfare and systematic errors in forecasts could lead to a misallocation of scarce resources. Various empirical approaches have been applied to forecast commodity prices that have used different methodologies. Most commonly-used approaches to forecast commodity sectors depend on classical time series models that assume values of the response variables are precise which is quite often not true in reality. Recently, this literature has mostly evolved to a consideration of fuzzy time series models that provide more flexibility in terms of the classical time series models assumptions such as stationarity, and large sample size requirement. Besides, fuzzy modeling approach allows decision making with estimated values under incomplete information or uncertainty. A number of fuzzy time series models have been developed and implemented over the last decades; however, most of them are not appropriate for forecasting repeated and nonconsecutive transitions in the data. The modeling scheme used in this paper eliminates this problem by introducing Markov modeling approach that takes into account both the repeated and nonconsecutive transitions. Also, the determination of length of interval is crucial in terms of the accuracy of forecasts. The problem of determining the length of interval arbitrarily is overcome and a methodology to determine the proper length of interval based on the distribution or mean of the first differences of series to improve forecast accuracy is proposed. The specific purpose of this paper is to propose and investigate the potential of a new forecasting model that integrates methodologies for determining the proper length of interval based on the distribution or mean of the first differences of series and Fuzzy Time Series- Markov Chain model. Moreover, the accuracy of the forecasting performance of proposed integrated model is compared to different univariate time series models and the superiority of proposed method over competing methods in respect of modelling and forecasting on the basis of forecast evaluation criteria is demonstrated. The application is to daily corn and soybean prices observed at three commercially important North Carolina markets; Candor, Cofield and Roaring River for corn and Fayetteville, Cofield and Greenville City for soybeans respectively. One main conclusion from this paper is that using fuzzy logic improves the forecast performance and accuracy; the effectiveness and potential benefits of the proposed model is confirmed with small selection criteria value such MAPE. The paper concludes with a discussion of the implications of integrating fuzzy logic and nonarbitrary determination of length of interval for the reliability and accuracy of price forecasts. The empirical results represent a significant contribution to our understanding of the applicability of fuzzy modeling in commodity price forecasts.

Keywords: commodity, forecast, fuzzy, Markov

Procedia PDF Downloads 207

Authors: Ingrid Argote, John Archila, Marcelo Becker

Abstract:

In agriculture, intelligent systems applications have generated great advances in automating some of the processes in the production chain. In order to improve the efficiency of those systems is proposed a vision system to estimate the amount of fruits in sweet orange trees. This work presents a system proposal using capture of thermal images and fuzzy logic. A bibliographical review has been done to analyze the state-of-the-art of the different systems used in fruit recognition, and also the different applications of thermography in agricultural systems. The algorithm developed for this project uses the metrics of the fuzzines parameter to the contrast improvement and segmentation of the image, for the counting algorith m was used the Hough transform. In order to validate the proposed algorithm was created a bank of images of sweet orange Citrus (L.) Osbeck acquired in the Maringá Farm. The tests with the algorithm Indicated that the variation of the tree branch temperature and the fruit is not very high, Which makes the process of image segmentation using this differentiates, This Increases the amount of false positives in the fruit counting algorithm. Recognition of fruits isolated with the proposed algorithm present an overall accuracy of 90.5 % and grouped fruits. The accuracy was 81.3 %. The experiments show the need for a more suitable hardware to have a better recognition of small temperature changes in the image.

Keywords: Agricultural systems, Citrus, Fuzzy logic, Thermal images.

Procedia PDF Downloads 219

Authors: S. Shamohammadi, L. Razavi

Abstract:

This paper based on the principle concepts of SCS-CN model, a new mathematical model for computation of retention potential (S) presented. In the mathematical model, not only precipitation-runoff concepts in SCS-CN model are precisely represented in a mathematical form, but also new concepts, called “maximum retention” and “total retention” is introduced, and concepts of potential retention capacity, maximum retention, and total retention have been separated from each other. In the proposed model, actual retention (F), maximum actual retention (Fmax), total retention (S), maximum retention (Smax), and potential retention (Sp), for the first time clearly defined, so that Sp is not variable, but a function of morphological characteristics of the watershed. Indeed, based on the mathematical relation of the conceptual curve of SCS-CN model, the proposed model provides a new method for the computation of actual retention in watershed and it simply determined runoff based on. In the corresponding relations, in addition to Precipitation (P), Initial retention (Ia), cumulative values of actual retention capacity (F), total retention (S), runoff (Q), antecedent moisture (M), potential retention (Sp), total retention (S), we introduced Fmax and Fmin referring to maximum and minimum actual retention, respectively. As well as, ksh is a coefficient which depends on morphological characteristics of the watershed. Advantages of the modified version versus the original model include a better precision, higher performance, easier calibration and speed computing.

Keywords: model, mathematical, retention, watershed, SCS

Procedia PDF Downloads 438