Search results for: data center optimization

Authors: Reihaneh Rafiemanzelt

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The need to protect our cultural heritage was stressed on revitalization of historical city centers in communities. The main goals the proses was to attract finance and activities to the historical city centers through the citizens and municipalities participation while cities expanded their boundaries toward suburban areas. Todays the main problems which facing to the most historical city centers, is loss of their centrality through effect of urbanization on any point of the cities which is the most important issue on neglect and abandonment of the historical central area by decentralizing living, commerce and public areas. This article evaluate the ways in which city center revitalization can be effect on vitality and viability of the central area in case of Naghshe Jahan square which situated at the center of Isfahan city, Iran. Constructed between 1598 and 1629, it is now an important historical site, and one of UNESCO's World Heritage Sites.

Keywords: urban development, revitalization, city centers, vitality and viability

Procedia PDF Downloads 612

Authors: Yin Zhang, Kai Qiao, Xiyang Zhi, Jinnan Gong, Jianming Hu

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In order to effectively detect aerial targets over long distances, an optimal matching design method of space-based optical payload is proposed. Firstly, main factors affecting optical detectability of small targets under complex environment are analyzed based on the full link of a detection system, including band center, band width and spatial resolution. Then a performance characterization model representing the relationship between image signal-to-noise ratio (SCR) and the above influencing factors is established to describe a detection system. Finally, an optimal matching design example is demonstrated for a typical aerial target by simulating and analyzing its SCR under different scene clutter coupling with multi-scale characteristics, and the optimized detection band and spatial resolution are presented. The method can provide theoretical basis and scientific guidance for space-based detection system design, payload specification demonstration and information processing algorithm optimization.

Keywords: space-based detection, aerial targets, optical system design, detectability characterization

Procedia PDF Downloads 167

Authors: Khan Shahid, Shahid Zakia

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The article demonstrates how Theology and Islamic Philosophy can be illuminated and enhanced through the application of the SOUL framework (Sincere act, Optimization effort, Ultimate goal, Law compliance). The study explores historical development using a phenomenological approach and integrates the SOUL framework to enrich Theology and Islamic Philosophy. The proposed framework highlights the significance of these elements, ultimately leading to a deeper understanding of Theology and Islamic Philosophy.

Keywords: SOUL framework, illuminating human identity, theology, Islamic Philosophy, sincerity act, optimization effort, ultimate goals, law compliance

Procedia PDF Downloads 88

Authors: Meng Wu

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Motion planning is a common task required to be fulfilled by robots. A strategy combining Ant Colony Optimization (ACO) and gravity gradient inversion algorithm is proposed for motion planning of mobile robots. In this paper, in order to realize optimal motion planning strategy, the cost function in ACO is designed based on gravity gradient inversion algorithm. The obstacles around mobile robot can cause gravity gradient anomalies; the gradiometer is installed on the mobile robot to detect the gravity gradient anomalies. After obtaining the anomalies, gravity gradient inversion algorithm is employed to calculate relative distance and orientation between mobile robot and obstacles. The relative distance and orientation deduced from gravity gradient inversion algorithm is employed as cost function in ACO algorithm to realize motion planning. The proposed strategy is validated by the simulation and experiment results.

Keywords: motion planning, gravity gradient inversion algorithm, ant colony optimization

Procedia PDF Downloads 136

Authors: Qian Zaijian

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E-government is one of the hot issues in the current academic research of public policy and management. As the organic integration of information and communication technology (ICT) and public administration, e-government is one of the most important areas in contemporary information society. Policy information system is a basic subsystem of public policy system, its operation affects the overall effect of the policy process or even exerts a direct impact on the operation of a public policy and its success or failure. The basic principle of its operation is information collection, processing, analysis and release for a specific purpose. The function of E-government for public policy information system lies in the promotion of public access to the policy information resources, information transmission through e-participation, e-consultation in the process of policy analysis and processing of information and electronic services in policy information stored, to promote the optimization of policy information systems. However, due to many factors, the function of e-government to promote policy information system optimization has its practical limits. In the building of E-government in our country, we should take such path as adhering to the principle of freedom of information, eliminating the information divide (gap), expanding e-consultation, breaking down information silos and other major path, so as to promote the optimization of public policy information systems.

Keywords: China, e-consultation, e-democracy, e-government, e-participation, ICTs, public policy information systems

Procedia PDF Downloads 862

Authors: Xiangtuo Chen, Paul-Henry Cournéde

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Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest

Procedia PDF Downloads 229

Authors: A. Nourbakhsh

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A finite difference/front tracking method is used to study the motion of three-dimensional deformable drops suspended in plane Poiseuille flow at non-zero Reynolds numbers. A parallel version of the code was used to study the behavior of suspension on a reasonable grid resolution (grids). The viscosity and density of drops are assumed to be equal to that of the suspending medium. The effect of the Reynolds number is studied in detail. It is found that drops with small deformation behave like rigid particles and migrate to an equilibrium position about half way between the wall and the center line (the Segre-Silberberg effect). However, for highly deformable drops there is a tendency for drops to migrate to the middle of the channel, and the maximum concentration occurs at the center line. The effective viscosity of suspension and the fluctuation energy of the flow across the channel increases with the Reynolds number of the flow.

Keywords: suspensions, Poiseuille flow, effective viscosity, Reynolds number

Procedia PDF Downloads 353

Authors: Iman Atighi, Jalal Soleimannejad, Ahmad Akbarinasab, Saeid Moradpour

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In the past, we could increase profit by increasing product prices. But in the new decade, a competitive market does not let us to increase profit with increase prices. Therefore, the only way to increase profit will be reduce costs. A significant percentage of production costs are the maintenance costs, and analysis of these costs could achieve more profit. Most maintenance strategies such as RCM (Reliability-Center-Maintenance), TPM (Total Productivity Maintenance), PM (Preventive Maintenance) etc., are trying to reduce maintenance costs. In this paper, decreasing the maintenance costs of Concentration Plant of Golgohar Company (GEG) was examined by using of MTBF (Mean Time between Failures) and MTTR (Mean Time to Repair) analyses. These analyses showed that instead of buying new machines and increasing costs in order to promote capacity, the improving of MTBF and MTTR indexes would solve capacity problems in the best way and decrease costs.

Keywords: Golgohar Iron Ore Mining and Industrial Company, maintainability, maintenance costs, reliability-center-maintenance

Procedia PDF Downloads 300

Authors: N. R. Putra, A. H. Abdul Aziz, A. S. Zaini, Z. Idham, F. Idrus, M. Z. Bin Zullyadini, M. A. Che Yunus

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The content of omega-3 in soybean oil is important in the development of infants and is an alternative for the omega-3 in fish oils. The investigation of extraction of soybean oil is needed to obtain the bioactive compound in the extract. Supercritical carbon dioxide extraction is modern and green technology to extract herbs and plants to obtain high quality extract due to high diffusivity and solubility of the solvent. The aim of this study was to obtain the optimum condition of soybean oil extraction by modified supercritical carbon dioxide. The soybean oil was extracted by using modified supercritical carbon dioxide (SC-CO₂) under the temperatures of 40, 60, 80 °C, pressures of 150, 250, 350 Bar, and constant flow-rate of 10 g/min as the parameters of extraction processes. An experimental design was performed in order to optimize three important parameters of SC-CO₂ extraction which are pressure (X₁), temperature (X₂) to achieve optimum yields of soybean oil. Box Behnken Design was applied for experimental design. From the optimization process, the optimum condition of extraction of soybean oil was obtained at pressure 338 Bar and temperature 80 °C with oil yield of 2.713 g. Effect of pressure is significant on the extraction of soybean oil by modified supercritical carbon dioxide. Increasing of pressure will increase the oil yield of soybean oil.

Keywords: soybean oil, SC-CO₂ extraction, yield, optimization

Procedia PDF Downloads 254

Authors: Karan Vishavjit, Aakash Lakra, Shafaq Khan

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The COVID-19 pandemic has pushed healthcare professionals to use big data analytics as a vital tool for tracking and evaluating the effects of contagious viruses. To effectively analyze huge datasets, efficient NoSQL databases are needed. The analysis of post-COVID-19 health and well-being outcomes and the evaluation of the effectiveness of government efforts during the pandemic is made possible by this research’s integration of several datasets, which cuts down on query processing time and creates predictive visual artifacts. We recommend applying sharding and indexing technologies to improve query effectiveness and scalability as the dataset expands. Effective data retrieval and analysis are made possible by spreading the datasets into a sharded database and doing indexing on individual shards. Analysis of connections between governmental activities, poverty levels, and post-pandemic well being is the key goal. We want to evaluate the effectiveness of governmental initiatives to improve health and lower poverty levels. We will do this by utilising advanced data analysis and visualisations. The findings provide relevant data that supports the advancement of UN sustainable objectives, future pandemic preparation, and evidence-based decision-making. This study shows how Big Data and NoSQL databases may be used to address problems with global health.

Keywords: big data, COVID-19, health, indexing, NoSQL, sharding, scalability, well being

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Authors: Kapse Swapnil, K. Shankar

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Aiming at optimizing the weight and deflection of cantilever beam subjected to maximum stress and maximum deflection, Multi-objective Particle Swarm Optimization (MOPSO) with Utopia Point based local search is implemented. Utopia point is used to govern the search towards the Pareto Optimal set. The elite candidates obtained during the iterations are stored in an archive according to non-dominated sorting and also the archive is truncated based on least crowding distance. Local search is also performed on elite candidates and the most diverse particle is selected as the global best. This method is implemented on standard test functions and it is observed that the improved algorithm gives better convergence and diversity as compared to NSGA-II in fewer iterations. Implementation on practical structural problem shows that in 5 to 6 iterations, the improved algorithm converges with better diversity as evident by the improvement of cantilever beam on an average of 0.78% and 9.28% in the weight and deflection respectively compared to NSGA-II.

Keywords: Utopia point, multi-objective particle swarm optimization, local search, cantilever beam

Procedia PDF Downloads 517

Authors: Nakul V. Naphade, Sandra G. L. Persiani, Yew Wah Wong, Pramod S. Kamath, Avinash H. Anantharam, Hui Ling Aw, Yann Grynberg

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Achieving zero-energy targets in existing buildings is known to be a difficult task requiring important cuts in the building energy consumption, which in many cases clash with the functional necessities of the building wherever the on-site energy generation is unable to match the overall energy consumption. Between the building’s consumption optimization limit and the energy, target stretches a case-specific optimization grey zone, which requires tailored intervention and enhanced user’s commitment. In the view of the future adoption of more stringent energy-efficiency targets in the context of hot-humid tropical climates, this study aims to define the energy optimization grey zone by assessing the energy-efficiency limit in the state-of-the-art typical mid- and high-rise full AC office buildings, through the integration of currently available technologies. Energy models of two code-compliant generic office-building typologies were developed as a baseline, a 20-storey ‘high-rise’ and a 7-storey ‘mid-rise’. Design iterations carried out on the energy models with advanced market ready technologies in lighting, envelope, plug load management and ACMV systems and controls, lead to a representative energy model of the current maximum technical potential. The simulations showed that ZEB targets could be achieved in fully AC buildings under an average of seven floors only by compromising on energy-intense facilities (as full AC, unlimited power-supply, standard user behaviour, etc.). This paper argues that drastic changes must be made in tropical buildings to span the energy optimization grey zone and achieve zero energy. Fully air-conditioned areas must be rethought, while smart technologies must be integrated with an aggressive involvement and motivation of the users to synchronize with the new system’s energy savings goal.

Keywords: energy simulation, office building, tropical climate, zero energy buildings

Procedia PDF Downloads 183

Authors: Sultana Razia

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The purpose of this study was to investigate the effect of early therapeutic intervention on children with an autism spectrum disorder. Participants were 140 children with autism spectrum disorder from Autism Corner in a selected rehabilitation center of Bangladesh. This study included children who are at aged of 18-month to 36-month and who were taking occupational therapy and speech and language therapy from the autism center. They were primarily screened using M-CHAT; however, children with other physical disabilities or medical conditions were excluded. 3-months interventions of 6 sessions per week are a minimum of 45-minutes long per session, one to one interaction followed by parent-led structured home-based therapy were provided. The results indicated that early intensive therapeutic intervention improves understanding, social skills and sensory skills. It can be concluded that therapeutic early intervention has a positive effect on diminishing symptoms of Autism Spectrum Disorder.

Keywords: autism, m-chat, reciprocal social behavior, CRP

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Authors: Jaleh Mohammad Aliha, Rezvan Razazi, Nasim Naderi

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Introduction: Despite the progress in new effective drugs in the treatment of heart failure, the disease still accompanied with frequent hospitalization, impaired quality of life, early mortality and significant economic burden. Patients with chronic disease and consequently patients with heart failure need the knowledge and optimal health literacy to improve the quality of life and minimize the rate of rehopitalizatio. So, considering to importance of knowledge and health literacy in this patients as well as contradictory literature, this study conducted to investigate the relationship between health literacy and Knowledge of heart failure with rehospitalization in patients with heart failure admitted to heart failure clinic in Rajai Heart center in 1394. Methods: The cross-sectional method with convenience sampling method was used in this study. After obtaining the necessary permissions from the ethics committee and the Shahid Rajai Heart center, 238 patients who were older than 18 years and had ejection fraction 35% or less with the ability to read and write and lack of psychiatric, neurological and cognitive disorders and signed the informed consent were recruited. Data collection were perfomed through demographic data questionnaire, short standard health literacy questionnaire 'Short-TOFHLA-16' and Vanderwall (2005) knowledge of heart failure questionnaire. Reliability was assessed by internal consistency method and Cronbach's alpha for both questionnaires was more than 0.7. Then data were analysed by SPSS-20 with descriptive statistic and analytical statistic such as T-test, Chi-square and ANOVA. Results: The majority of patients were male (66%), married (80%) and had age between 50 to 70 years old (42%). The majority of studied men and women have good health literacy and About half of them have adequate knowledge about heart failure. Fisher's exact test showed that there was a significant statistical correlation between health literacy and knowlegh about heart failure. In other words, higher health literacy associated with more knowledge about their condition. Also findings showed that there was no significant statistical correlation between health literacy and knowledge about heart failure and frequency of CCU and emergency admissions. Conclusion: The study results showed that the higher health literacy, associated with the greater knowledge about heart failure and patients' perception about caring recommendations and disease outcomes. Therefore, the knowledge about heart failure and factors which related to severity of the disease, is the important issue to problem identification and treatment and reduction of rehospitalization.

Keywords: health literacy, heart failure, knowlegde, rehospitalization

Procedia PDF Downloads 399

Authors: Avent Grange, Frederic Bertrand, Jean-Baptiste Droin, Amandine Marrel, Jean-Henry Ferrasse, Olivier Boutin

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CEA and its industrial partners are designing a gas Power Conversion System (PCS) based on a Brayton cycle for the ASTRID Sodium-cooled Fast Reactor. Investigations of control and regulation requirements to operate this PCS during operating, incidental and accidental transients are necessary to adapt core heat removal. To this aim, we developed a methodology to optimize the thermal-hydraulic behavior of the reactor during normal operations, incidents and accidents. This methodology consists of a multi-objective optimization for a specific sequence, whose aim is to increase component lifetime by reducing simultaneously several thermal stresses and to bring the reactor into a stable state. Furthermore, the multi-objective optimization complies with safety and operating constraints. Operating, incidental and accidental sequences use specific regulations to control the thermal-hydraulic reactor behavior, each of them is defined by a setpoint, a controller and an actuator. In the multi-objective problem, the parameters used to solve the optimization are the setpoints and the settings of the controllers associated with the regulations included in the sequence. In this way, the methodology allows designers to define an optimized and specific control strategy of the plant for the studied sequence and hence to adapt PCS piloting at its best. The multi-objective optimization is performed by evolutionary algorithms coupled to surrogate models built on variables computed by the thermal-hydraulic system code, CATHARE2. The methodology is applied to a loss of off-site power sequence. Three variables are controlled: the sodium outlet temperature of the sodium-gas heat exchanger, turbomachine rotational speed and water flow through the heat sink. These regulations are chosen in order to minimize thermal stresses on the gas-gas heat exchanger, on the sodium-gas heat exchanger and on the vessel. The main results of this work are optimal setpoints for the three regulations. Moreover, Proportional-Integral-Derivative (PID) control setting is considered and efficient actuators used in controls are chosen through sensitivity analysis results. Finally, the optimized regulation system and the reactor control procedure, provided by the optimization process, are verified through a direct CATHARE2 calculation.

Keywords: gas power conversion system, loss of off-site power, multi-objective optimization, regulation, sodium fast reactor, surrogate model

Procedia PDF Downloads 306

Authors: Wei-Po Wu, Wen-Yu Liu, Wei−Ting Lin, Hen-Yu Lien

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Introduction: Improving balance function for individuals after stroke is a crucial target in physiotherapy. Aquatic therapy which challenges individual’s postural control in an unstable fluid environment may be beneficial in enhancing balance functions. The purposes of the systematic review with meta-analyses were to validate the effects of aquatic therapy in improving balance functions for individuals with strokes in contrast to conventional physiotherapy. Method: Available studies were explored from three electronic databases: PubMed, Scopus, and Web of Science. During literature search, the published date of studies was not limited. The study design of the included studies should be randomized controlled trials (RCTs) and the studies should contain at least one outcome measurement of balance function. The PEDro scale was adopted to assess the quality of included studies, while the 'Oxford Centre for Evidence-Based Medicine 2011 Levels of Evidence' was used to evaluate the level of evidence. After the data extraction, studies with same outcome measures were pooled together for meta-analysis. Result: Ten studies with 282 participants were included in analyses. The research qualities of the studies were ranged from fair to good (4 to 8 points). Levels of evidence of the included studies were graded as level 2 and 3. Finally, scores of Berg Balance Scale (BBS), Eye closed force plate center of pressure velocity (anterior-posterior, medial-lateral axis) and Timed up and Go test were pooled and analyzed separately. The pooled results shown improvement in balance function (BBS mean difference (MD): 1.39 points; 95% confidence interval (CI): 0.05-2.29; p=0.002) (Eye closed force plate center of pressure velocity (anterior-posterior axis) MD: 1.39 mm/s; 95% confidence interval (CI): 0.93-1.86; p<0.001) (Eye closed force plate center of pressure velocity (medial-lateral) MD: 1.48 mm/s; 95% confidence interval (CI): 0.15-2.82; p=0.03) and mobility (MD: 0.9 seconds; 95% CI: 0.07-1.73; p=0.03) of stroke individuals after aquatic therapy compared to conventional therapy. Although there were significant differences between two treatment groups, the differences in improvement were relatively small. Conclusion: The aquatic therapy improved general balance function and mobility in the individuals with stroke better than conventional physiotherapy.

Keywords: aquatic therapy, balance function, meta-analysis, stroke, systematic review

Procedia PDF Downloads 200

Authors: Stephen Akong Takim

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This study focused on the optimization of the flexural strength of bio-composite samples of palm kernel, whelks, clams, periwinkles shells and bamboo fiber reinforced with resin for engineering applications. The aim of the study was to formulate different samples of bio-composite reinforced with resin for engineering applications and to evaluate the flexural strength of the fabricated composite. The hand lay-up technique was used for the composites produced by incorporating different percentage compositions of the shells/fiber (10%, 15%, 20%, 25% and 30%) into varied proportions of epoxy resin and catalyst. The cured samples, after 24 hours, were subjected to tensile, impact, flexural and water absorption tests. The experiments were conducted using the Taguchi optimization method L25 (5x5) with five design parameters and five level combinations in Minitab 18 statistical software. The results showed that the average value of flexural was 114.87MPa when compared to the unreinforced 72.33MPa bio-composite. The study recommended that agricultural waste, like palm kernel shells, whelk shells, clams, periwinkle shells and bamboo fiber, should be converted into important engineering applications.

Keywords: bio-composite, resin, palm kernel shells, welk shells, periwinkle shells, bamboo fiber, Taguchi techniques and engineering application

Procedia PDF Downloads 73

Authors: Taha Benarbia

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The advent of automated and connected vehicles has revolutionized the transportation industry, presenting new opportunities for enhancing the efficiency, safety, and sustainability of our transportation networks. This paper explores the integration of automated and connected vehicles into a smart transportation framework, leveraging the power of deep learning techniques to optimize the overall network performance. The first aspect addressed in this paper is the deployment of automated vehicles (AVs) within the transportation system. AVs offer numerous advantages, such as reduced congestion, improved fuel efficiency, and increased safety through advanced sensing and decisionmaking capabilities. The paper delves into the technical aspects of AVs, including their perception, planning, and control systems, highlighting the role of deep learning algorithms in enabling intelligent and reliable AV operations. Furthermore, the paper investigates the potential of connected vehicles (CVs) in creating a seamless communication network between vehicles, infrastructure, and traffic management systems. By harnessing real-time data exchange, CVs enable proactive traffic management, adaptive signal control, and effective route planning. Deep learning techniques play a pivotal role in extracting meaningful insights from the vast amount of data generated by CVs, empowering transportation authorities to make informed decisions for optimizing network performance. The integration of deep learning with automated and connected vehicles paves the way for advanced transportation network optimization. Deep learning algorithms can analyze complex transportation data, including traffic patterns, demand forecasting, and dynamic congestion scenarios, to optimize routing, reduce travel times, and enhance overall system efficiency. The paper presents case studies and simulations demonstrating the effectiveness of deep learning-based approaches in achieving significant improvements in network performance metrics

Keywords: automated vehicles, connected vehicles, deep learning, smart transportation network

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Authors: Konstantinos Metaxiotis, Konstantinos Liagkouras

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The objective of this study is to examine the performance of three well-known multiobjective evolutionary algorithms for solving optimization problems. The first algorithm is the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the second one is the Strength Pareto Evolutionary Algorithm 2 (SPEA-2), and the third one is the Multiobjective Evolutionary Algorithms based on decomposition (MOEA/D). The examined multiobjective algorithms are analyzed and tested on the ZDT set of test functions by three performance metrics. The results indicate that the NSGA-II performs better than the other two algorithms based on three performance metrics.

Keywords: MOEAs, multiobjective optimization, ZDT test functions, evolutionary algorithms

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Authors: Ivo Zution Goncalves, Christopher M. U. Neale, Hiran Medeiros, Everardo Mantovani, Natalia Souza

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Multispectral and thermal infrared imagery from satellite sensors coupled with climate and soil datasets were used to estimate evapotranspiration and biomass in center pivots planted to maize in Brazil during the 2016 season. The hybrid remote sensing based model named Spatial EvapoTranspiration Modelling Interface (SETMI) was applied using multispectral and thermal infrared imagery from the Landsat Thematic Mapper instrument. Field data collected by the IRRIGER center pivot management company included daily weather information such as maximum and minimum temperature, precipitation, relative humidity for estimating reference evapotranspiration. In addition, soil water content data were obtained every 0.20 m in the soil profile down to 0.60 m depth throughout the season. Early season soil samples were used to obtain water-holding capacity, wilting point, saturated hydraulic conductivity, initial volumetric soil water content, layer thickness, and saturated volumetric water content. Crop canopy development parameters and irrigation application depths were also inputs of the model. The modeling approach is based on the reflectance-based crop coefficient approach contained within the SETMI hybrid ET model using relationships developed in Nebraska. The model was applied to several fields located in Minas Gerais State in Brazil with approximate latitude: -16.630434 and longitude: -47.192876. The model provides estimates of real crop evapotranspiration (ET), crop irrigation requirements and all soil water balance outputs, including biomass estimation using multi-temporal satellite image inputs. An interpolation scheme based on the growing degree-day concept was used to model the periods between satellite inputs, filling the gaps between image dates and obtaining daily data. Actual and accumulated ET, accumulated cold temperature and water stress and crop water requirements estimated by the model were compared with data measured at the experimental fields. Results indicate that the SETMI modeling approach using data assimilation, showed reliable daily ET and crop water requirements for maize, interpolated between remote sensing observations, confirming the applicability of the SETMI model using new relationships developed in Nebraska for estimating mainly ET and water requirements in Brazil under tropical conditions.

Keywords: basal crop coefficient, irrigation, remote sensing, SETMI

Procedia PDF Downloads 140

Authors: Jinyan Li, Simon Fong, Raymond Wong, Mohammed Sabah, Fiaidhi Jinan

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Clinical data analysis and forecasting have make great contributions to disease control, prevention and detection. However, such data usually suffer from highly unbalanced samples in class distributions. In this paper, we target at the binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat-inspired algorithm, and combine both of them with the synthetic minority over-sampling technique (SMOTE) for processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reveal that while the performance improvements obtained by the former methods are not scalable to larger data scales, the later one, which we call Adaptive Swarm Balancing Algorithms, leads to significant efficiency and effectiveness improvements on large datasets. We also find it more consistent with the practice of the typical large imbalanced medical datasets. We further use the meta-heuristic algorithms to optimize two key parameters of SMOTE. Leading to more credible performances of the classifier, and shortening the running time compared with the brute-force method.

Keywords: Imbalanced dataset, meta-heuristic algorithm, SMOTE, big data

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Authors: Bahamin Bazooyar, Xinyan Wang, Hua Zhao

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As a zero-carbon fuel, hydrogen can be used in combustion engines to avoid carbon emissions. This paper numerically investigates the engine performance of a two-stroke opposed piston hydrogen engine by using three-dimensional (3D) Computational Fluid Dynamics (CFD) simulations. The engine displacement is 12.2 cm, and the compression ratio of 39. RANS simulations with the k-ε turbulence model and coupled chemistry combustion models are performed at an engine speed of 4500 rpm and hydrogen flow rate of up to 100 gr/s. In order to model the hydrogen injection process, the hydrogen nozzle was meshed with refined mesh, and injection pressure varied between 100 and 200 bars. In order to optimize the hydrogen combustion process, the injection timing was optimized between 15 before the top dead center and 10. The results showed that the combustion efficiency was mostly influenced by the injection pressures due to its impact on the fuel/air mixing and charge inhomogeneity. Nitrogen oxide (NOₓ) emissions are well correlated with engine peak temperatures, demonstrating that the thermal NO mechanism is dominant under engine conditions. Through the optimization of hydrogen injection timing and pressure, the peak thermal efficiency of 45 and NOx emission of 15 ppm/kWh can be achieved at an injection timing of 350 CA and pressure of 160 bars.

Keywords: engine, hydrogen, diesel, two-stroke, opposed-piston, decarbonisation

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Authors: Qais M. Yousef, Yasmeen A. Alshaer

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Over the last few years, the amount of data available on the globe has been increased rapidly. This came up with the emergence of recent concepts, such as the big data and the Internet of Things, which have furnished a suitable solution for the availability of data all over the world. However, managing this massive amount of data remains a challenge due to their large verity of types and distribution. Therefore, locating the required file particularly from the first trial turned to be a not easy task, due to the large similarities of names for different files distributed on the web. Consequently, the accuracy and speed of search have been negatively affected. This work presents a method using Electroencephalography signals to locate the files based on their contents. Giving the concept of natural mind waves processing, this work analyses the mind wave signals of different people, analyzing them and extracting their most appropriate features using multi-objective metaheuristic algorithm, and then classifying them using artificial neural network to distinguish among files with similar names. The aim of this work is to provide the ability to find the files based on their contents using human thoughts only. Implementing this approach and testing it on real people proved its ability to find the desired files accurately within noticeably shorter time and retrieve them as a first choice for the user.

Keywords: artificial intelligence, data contents search, human active memory, mind wave, multi-objective optimization

Procedia PDF Downloads 175

Authors: Fabian Velasquez, John Espitia, Henry Hernadez, Sebastian Escobar, Jader Rodriguez

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Non-centrifuged cane sugar (NCS) is a concentrated product obtained through the evaporation of water contain from sugarcane juice inopen heat exchangers (OE). The heat supplied to the evaporation stages is obtained from the cane bagasse through the thermochemical process of combustion, where the thermal energy released is transferred to OE by the flue gas. Therefore, the optimization of energy usage becomes essential for the proper design of the production process. For optimize the energy use, it is necessary modeling and simulation of heat transfer between the combustion gases and the juice and to understand the major mechanisms involved in the heat transfer. The main objective of this work was simulated heat transfer phenomena between the flue gas and open heat exchangers using Computational Fluid Dynamics model (CFD). The simulation results were compared to field measured data. Numerical results about temperature profile along the flue gas pipeline at the measurement points are in good accordance with field measurements. Thus, this study could be of special interest in design NCS production process and the optimization of the use of energy.

Keywords: mathematical modeling, design variables, computational fluid dynamics, overall thermal efficiency

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Authors: Rit M., Girard R., Villot J., Thorel M.

Abstract:

In a scenario of extensive residential housing renovation, stakeholders need models that support decision-making through a deep understanding of the existing building stock and accurate energy demand simulations. To address this need, we have modified an optimization model using open data that enables the study of renovation strategies at both territorial and national scales. This approach provides (1) a definition of a strategy to simplify decision trees from theoretical combinations, (2) input to decision makers on real-world renovation constraints, (3) more reliable identification of energy-saving measures (changes in technology or behaviour), and (4) discrepancies between currently planned and actually achieved strategies. The main contribution of the studies described in this document is the geographic scale: all residential buildings in the areas of interest were modeled and simulated using national data (geometries and attributes). These buildings were then renovated, when necessary, in accordance with the environmental objectives, taking into account the constraints applicable to each territory (number of renovations per year) or at the national level (renovation of thermal deficiencies (Energy Performance Certificates F&G)). This differs from traditional approaches that focus only on a few buildings or archetypes. This model can also be used to analyze the evolution of a building stock as a whole, as it can take into account both the construction of new buildings and their demolition or sale. Using specific case studies of French territories, this paper highlights a significant discrepancy between the strategies currently advocated by decision-makers and those proposed by our optimization model. This discrepancy is particularly evident in critical metrics such as the relationship between the number of renovations per year and achievable climate targets or the financial support currently available to households and the remaining costs. In addition, users are free to seek optimizations for their building stock across a range of different metrics (e.g., financial, energy, environmental, or life cycle analysis). These results are a clear call to re-evaluate existing renovation strategies and take a more nuanced and customized approach. As the climate crisis moves inexorably forward, harnessing the potential of advanced technologies and data-driven methodologies is imperative.

Keywords: residential housing renovation, MILP, energy demand simulations, data-driven methodology

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Authors: Mahdi Pirmoradian, Olayinka Adigun, Christos Politis

Abstract:

Cooperative spectrum sensing is a crucial challenge in cognitive radio networks. Cooperative sensing can increase the reliability of spectrum hole detection, optimize sensing time and reduce delay in cooperative networks. In this paper, an efficient central capacity optimization algorithm is proposed to minimize cooperative sensing time in a homogenous sensor network using OR decision rule subject to the detection and false alarm probabilities constraints. The evaluation results reveal significant improvement in the sensing time and normalized capacity of the cognitive sensors.

Keywords: cooperative networks, normalized capacity, sensing time

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Authors: Wadea Ameen

Abstract:

Rapid prototyping (RP) technology, such as fused deposition modeling (FDM), is gaining popularity because it can produce functioning components with intricate geometric patterns in a reasonable amount of time. A multitude of process variables influences the quality of manufactured parts. In this study, four important process parameters such as layer thickness, model interior fill style, support fill style and orientation are considered. Their influence on three responses, such as build time, model material, and support material, is studied. Experiments are conducted based on factorial design, and the results are presented.

Keywords: fused deposition modeling, factorial design, optimization, 3D printing

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Authors: Shafqat Wahab, Waseem Tahir, Manzoor Ahmad, Sarfraz Khan, M. Azam

Abstract:

Optimization and analysis of tool flank wear width and surface finish of alloy steel rods are studied in the presence of cryogenic media (LN2) by using Tungsten Carbide Insert (CNMG 120404- WF 4215). Robust design concept of Taguchi L9(34) method and ANOVA is applied to determine the contribution of key cutting parameters and their optimum conditions. Through analysis, it revealed that cryogenic impact is more significant in reduction of the tool flank wear width while surface finish is mostly dependent on feed rate.

Keywords: turning, cryogenic fluid, liquid nitrogen, flank wear, surface roughness, taguchi

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Authors: Tawfik Thelaidjia, Salah Chenikher

Abstract:

Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, feature extraction from faulty bearing vibration signals is performed by a combination of the signal’s Kurtosis and features obtained through the preprocessing of the vibration signal samples using Db2 discrete wavelet transform at the fifth level of decomposition. In this way, a 7-dimensional vector of the vibration signal feature is obtained. After feature extraction from vibration signal, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. To improve the classification accuracy for bearing fault prediction, particle swarm optimization (PSO) is employed to simultaneously optimize the SVM kernel function parameter and the penalty parameter. The results have shown feasibility and effectiveness of the proposed approach

Keywords: condition monitoring, discrete wavelet transform, fault diagnosis, kurtosis, machine learning, particle swarm optimization, roller bearing, rotating machines, support vector machine, vibration measurement

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Authors: Siti Indati Mustapa, Hussain Ali Bekhet

Abstract:

Transportation sector represents more than 40% of total energy consumption in Malaysia. This sector is a major user of fossils based fuels, and it is increasingly being highlighted as the sector which contributes least to CO2 emission reduction targets. Considering this fact, this paper attempts to investigate the problem of reducing CO2 emission using linear programming approach. An optimization model which is used to investigate the optimal level of CO2 emission reduction in the road transport sector is presented. In this paper, scenarios have been used to demonstrate the emission reduction model: (1) utilising alternative fuel scenario, (2) improving fuel efficiency scenario, (3) removing fuel subsidy scenario, (4) reducing demand travel, (5) optimal scenario. This study finds that fuel balancing can contribute to the reduction of the amount of CO2 emission by up to 3%. Beyond 3% emission reductions, more stringent measures that include fuel switching, fuel efficiency improvement, demand travel reduction and combination of mitigation measures have to be employed. The model revealed that the CO2 emission reduction in the road transportation can be reduced by 38.3% in the optimal scenario.

Keywords: CO2 emission, fuel consumption, optimization, linear programming, transportation sector, Malaysia

Procedia PDF Downloads 421