Search results for: grasshopper optimization algorithm

Authors: Neha Babbar, S. Van Roy, W. Dejonghe, S. Sforza, K. Elst

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Pectic oligosaccharides (a class of prebiotics) are non-digestible carbohydrates which benefits the host by stimulating the growth of healthy gut micro flora. Production of prebiotic pectic oligosaccharides (POS) from pectin rich agricultural residues involves a cutting of long chain polymer of pectin to oligomers of pectin while avoiding the formation of monosaccharides. The objective of the present study is to develop a two-step continuous biocatalytic membrane reactor (MER) for the continuous production of POS (from sugar beet pulp) in which conversion is combined with separation. Optimization of the ratio of POS/monosaccharides, stability and productivities of the process was done by testing various residence times (RT) in the reactor vessel with diluted (10 RT, 20 RT, and 30 RT) and undiluted (30 RT, 40 RT and 60 RT) substrate. The results show that the most stable processes (steady state) were 20 RT and 30 RT for diluted substrate and 40 RT and 60 RT for undiluted substrate. The highest volumetric and specific productivities of 20 g/L/h and 11 g/gE/h; 17 g/l/h and 9 g/gE/h were respectively obtained with 20 RT (diluted substrate) and 40 RT (undiluted substrate). Under these conditions, the permeates of the reactor test with 20 RT (diluted substrate) consisted of 80 % POS fractions while that of 40 RT (undiluted substrate) resulted in 70% POS fractions. A two-step continuous biocatalytic MER for the continuous POS production looks very promising for the continuous production of tailor made POS. Although both the processes i.e 20 RT (diluted substrate) and 40 RT (undiluted substrate) gave the best results, but for an Industrial application it is preferable to use an undiluted substrate.

Keywords: pectic oligosaccharides, membrane reactor, residence time, specific productivity, volumetric productivity

Procedia PDF Downloads 440

Authors: Ehsan Mehryaar, Reza Bushehri

Abstract:

One of the principal parameters defining the clay soil dynamic response is the strain-shear modulus relation. Predicting the strain and, subsequently, shear modulus reduction of the soil is essential for performance analysis of structures exposed to earthquake and dynamic loadings. Many soil properties affect soil’s dynamic behavior. In order to capture those effects, in this study, a database containing 1193 data points consists of maximum shear modulus, strain, moisture content, initial void ratio, plastic limit, liquid limit, initial confining pressure resulting from dynamic laboratory testing of 21 clays is collected for predicting the shear modulus vs. strain curve of soil. A model based on an extreme gradient boosting technique is proposed. A tree-structured parzan estimator hyper-parameter tuning algorithm is utilized simultaneously to find the best hyper-parameters for the model. The performance of the model is compared to the existing empirical equations using the coefficient of correlation and root mean square error.

Keywords: XGBoost, hyper-parameter tuning, soil shear modulus, dynamic response

Procedia PDF Downloads 201

Authors: Chukwuma Mgboji

Abstract:

Since the advent of Artificial Intelligence, robots have become a major stay in developing societies. Robots are deployed in Education, Health, Food and in other spheres of life. Nigeria a country in West Africa has a very low profile in the advancement of Artificial Intelligence especially in the grass roots. The benefits of Artificial intelligence are not fully maximised and harnessed. Advances in artificial intelligence are perceived as impossible or observed as irrelevant. This study seeks to ascertain the needed skills for the development of artificialintelligence amongst secondary schools in Nigeria. The study focused on South East Nigeria with Five states namely Imo, Abia, Ebonyi, Anambra and Enugu. The sample size is 1000 students drawn from Five Government owned Universities offering Computer Science, Computer Education, Electronics Engineering across the Five South East states. Survey method was used to solicit responses from respondents. The findings from the study identified mathematical skills, analytical skills, problem solving skills, computing skills, programming skills, algorithm skills amongst others. The result of this study to the best of the author’s knowledge will be highly beneficial to all stakeholders involved in the advancements and development of artificial intelligence.

Keywords: artificial intelligence, secondary school, robotics, skills

Procedia PDF Downloads 154

Authors: Ndidi F. Amulu, Godian O. Mbah, Maxwel I. Onyiah, Callistus N. Ude

Abstract:

Analysis of the properties of coconut (Cocos nucifera) and its oil was evaluated in this work using standard analytical techniques. The analyses carried out include proximate composition of the fruit, extraction of oil from the fruit using different process parameters and physicochemical analysis of the extracted oil. The results showed the percentage (%) moisture, crude lipid, crude protein, ash, and carbohydrate content of the coconut as 7.59, 55.15, 5.65, 7.35, and 19.51 respectively. The oil from the coconut fruit was odourless and yellowish liquid at room temperature (30oC). The treatment combinations used (leaching time, leaching temperature and solute: solvent ratio) showed significant differences (P˂0.05) in the yield of oil from coconut flour. The oil yield ranged between 36.25%-49.83%. Lipid indices of the coconut oil indicated the acid value (AV) as 10.05 Na0H/g of oil, free fatty acid (FFA) as 5.03%, saponification values (SV) as 183.26 mgKOH-1 g of oil, iodine value (IV) as 81.00 I2/g of oil, peroxide value (PV) as 5.00 ml/ g of oil and viscosity (V) as 0.002. A standard statistical package minitab version 16.0 program was used in the regression analysis and analysis of variance (ANOVA). The statistical software mentioned above was also used to generate various plots such as single effect plot, interactions effect plot and contour plot. The response or yield of oil from the coconut flour was used to develop a mathematical model that correlates the yield to the process variables studied. The maximum conditions obtained that gave the highest yield of coconut oil were leaching time of 2 hrs, leaching temperature of 50 oC and solute/solvent ratio of 0.05 g/ml.

Keywords: coconut, oil-extraction, optimization, physicochemical, proximate

Procedia PDF Downloads 353

Authors: Jeremiah L. Chukwuneke, Obiora E. Anisiji, Chinonso H. Achebe, Paul C. Okolie

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This paper developed a mathematical model of a commercial biogas plant for urban area clean energy requirement. It identified biodegradable waste materials like domestic/city refuse as economically viable alternative source of energy. The mathematical formulation of the proposed gas plant follows the fundamental principles of thermodynamics, and further analyses were accomplished to develop an algorithm for evaluating the plant performance preferably in terms of daily production capacity. In addition, the capacity of the plant is equally estimated for a given cycle of operation and presented in time histories. A nominal 1500 m3 power gas plant was studied characteristically and its performance efficiency evaluated. It was observed that the rate of bio gas production is essentially a function of the reactor temperature, pH, substrate concentration, rate of degradation of the biomass, and the accumulation of matter in the system due to bacteria growth. The results of this study conform to a very large extent with reported empirical data of some existing plant and further model validations were conducted in line with classical records found in literature.

Keywords: energy and mass conservation, specific growth rate, thermophilic bacteria, temperature, rate of bio gas production

Procedia PDF Downloads 442

Authors: Yuanxiang Miao

Abstract:

Couchsurfing is a free homestay and social networking service accessible via the website and mobile app. Couchsurfers can directly request free accommodations from others and receive offers from each other. However, it is typically difficult for people to make a decision that accepts or declines a request when they receive it from Couchsurfers because they do not know each other at all. People are expected to meet up with some Couchsurfers who are kind, generous, and interesting while it is unavoidable to meet up with someone unfriendly. This paper utilized classification algorithms of Machine Learning to help people to find out the Good Couchsurfers and Not Good Couchsurfers on the Couchsurfing website. By knowing the prior experience, like Couchsurfer’s profiles, the latest references, and other factors, it became possible to recognize what kind of the Couchsurfers, and furthermore, it helps people to make a decision that whether to host the Couchsurfers or not. The value of this research lies in a case study in Kyoto, Japan in where the author has hosted 54 Couchsurfers, and the author collected relevant data from the 54 Couchsurfers, finally build a model based on classification algorithms for people to predict Couchsurfers. Lastly, the author offered some feasible suggestions for future research.

Keywords: Couchsurfing, Couchsurfers prediction, classification algorithm, hospitality tourism platform, hospitality sciences, machine learning

Procedia PDF Downloads 131

Authors: H. Shokouhmand, A. Ghanami

Abstract:

Heat pipe is simple heat transfer device which combines the conduction and phase change phenomena to control the heat transfer without any need for external power source. At hot surface of heat pipe, the liquid phase absorbs heat and changes to vapor phase. The vapor phase flows to condenser region and with the loss of heat changes to liquid phase. Due to gravitational force the liquid phase flows to evaporator section.In HVAC systems the working fluid is chosen based on the operating temperature. The heat pipe has significant capability to reduce the humidity in HVAC systems. Each HVAC system which uses heater, humidifier or dryer is a suitable nominate for the utilization of heat pipes. Generally heat pipes have three main sections: condenser, adiabatic region and evaporator.Performance investigation and optimization of heat pipes operation in order to increase their efficiency is crucial. In present article, a parametric study is performed to improve the heat pipe performance. Therefore, the heat capacity of heat pipe with respect to geometrical and confining parameters is investigated. For the better observation of heat pipe operation in HVAC systems, a CFD simulation in Eulerian- Eulerian multiphase approach is also performed. The results show that heat pipe heat transfer capacity is higher for water as working fluid with the operating temperature of 340 K. It is also observed that the vertical orientation of heat pipe enhances it’s heat transfer capacity.

Keywords: heat pipe, HVAC system, grooved heat pipe, heat pipe limits

Procedia PDF Downloads 377

Authors: H. Shokouhmand, A. Ghanami

Abstract:

Heat pipe is a simple heat transfer device which combines the conduction and phase change phenomena to control the heat transfer without any need for external power source. At hot surface of the heat pipe, the liquid phase absorbs heat and changes to vapor phase. The vapor phase flows to condenser region and with the loss of heat changes to liquid phase. Due to gravitational force, the liquid phase flows to evaporator section. In HVAC systems, the working fluid is chosen based on the operating temperature. The heat pipe has significant capability to reduce the humidity in HVAC systems. Each HVAC system which uses heater, humidifier or dryer is a suitable nominate for the utilization of heat pipes. Generally, heat pipes have three main sections: condenser, adiabatic region, and evaporator.Performance investigation and optimization of heat pipes operation in order to increase their efficiency is crucial. In the present article, a parametric study is performed to improve the heat pipe performance. Therefore, the heat capacity of the heat pipe with respect to geometrical and confining parameters is investigated. For the better observation of heat pipe operation in HVAC systems, a CFD simulation in Eulerian- Eulerian multiphase approach is also performed. The results show that heat pipe heat transfer capacity is higher for water as working fluid with the operating temperature of 340 K. It is also showed that the vertical orientation of heat pipe enhances its heat transfer capacity.

Keywords: heat pipe, HVAC system, grooved heat pipe, CFD simulation

Procedia PDF Downloads 495

Authors: M. Heydari, A. Ghanami

Abstract:

Heat pipe is simple heat transfer device which combines the conduction and phase change phenomena to control the heat transfer without any need for external power source. At hot surface of heat pipe, the liquid phase absorbs heat and changes to vapor phase. The vapor phase flows to condenser region and with the loss of heat changes to liquid phase. Due to gravitational force the liquid phase flows to evaporator section.In HVAC systems the working fluid is chosen based on the operating temperature. The heat pipe has significant capability to reduce the humidity in HVAC systems. Each HVAC system which uses heater, humidifier or dryer is a suitable nominate for the utilization of heat pipes. Generally heat pipes have three main sections: condenser, adiabatic region and evaporator.Performance investigation and optimization of heat pipes operation in order to increase their efficiency is crucial. In present article, a parametric study is performed to improve the heat pipe performance. Therefore, the heat capacity of heat pipe with respect to geometrical and confining parameters is investigated. For the better observation of heat pipe operation in HVAC systems, a CFD simulation in Eulerian- Eulerian multiphase approach is also performed. The results show that heat pipe heat transfer capacity is higher for water as working fluid with the operating temperature of 340 K. It is also showed that the vertical orientation of heat pipe enhances it’s heat transfer capacity.

Keywords: heat pipe, HVAC system, grooved heat pipe, heat pipe limits

Procedia PDF Downloads 444

Authors: Diana Gomez-Cano, J. C. Ochoa-Botero, Roberto Bernal Correa, Yhan Paul Arias

Abstract:

To obtain high mechanical performance, the fresh conditions of a mortar are decisive. Measuring the absorption of aggregates used in mortar mixes is a fundamental requirement for proper design of the mixes prior to their placement in construction sites. In this sense, absorption is a determining factor in the design of a mix because it conditions the amount of water, which in turn affects the water/cement ratio and the final porosity of the mortar. Thus, this work focuses on the mechanical behavior of recycled mortars manufactured from moisture correction using the Thermogravimetric Balancing Halogen Light (TBHL) technique in comparison with the traditional ASTM C 128 International Standard method. The advantages of using the TBHL technique are favorable in terms of reduced consumption of resources such as materials, energy, and time. The results show that in contrast to the ASTM C 128 method, the TBHL alternative technique allows obtaining a higher precision in the absorption values of recycled aggregates, which is reflected not only in a more efficient process in terms of sustainability in the characterization of construction materials but also in an effect on the mechanical performance of recycled mortars.

Keywords: alternative raw materials, halogen light, recycled mortar, resources optimization, water absorption

Procedia PDF Downloads 114

Authors: V. Hashim, P. N. Dileep

Abstract:

Stent implantation is being seen as the most successful method to treat coronary artery diseases. Different types of stents are available in the market these days and the success of a stent implantation greatly depends on the proper selection of a suitable stent for a patient. Computer numerical simulation is the cost effective way to choose the compatible stent. Studies confirm that the design characteristics of stent do have great importance with regards to the pressure it can sustain, the maximum displacement it can produce, the developed stress concentration and so on. In this paper different designs of stent were analyzed together with balloon to optimize the stent and balloon system. Commercially available stent Palmaz-Schatz has been selected for analysis. Abaqus software is used to simulate the system. This work is the finite element analysis of the artery stent implant to find out the design factors affecting the stress and strain. The work consists of two phases. In the first phase, stress distribution of three models were compared - stent without balloon, stent with balloon of equal length and stent with balloon of extra length than stent. In second phase, three different design models of Palmaz-Schatz stent were compared by keeping the balloon length constant. The results obtained from analysis shows that, the design of the strut have strong effect on the stress distribution. A design with chamfered slots found better results. The length of the balloon also has influence on stress concentration of the stent. Increase in length of the balloon will reduce stress, but will increase dog boning effect.

Keywords: coronary stent, finite element analysis, restenosis, stress concentration

Procedia PDF Downloads 623

Authors: E. Pretorius, A. M. Viljoen, M. van der Bank

Abstract:

Introduction: The phytochemistry of Agathosma crenulata and A. betulina has been studied extensively, while their molecular analysis through DNA barcoding remains virtually unexplored. This technique can confirm the identity of plant species included in a herbal product, thereby ensuring the efficacy of the herbal product and the accuracy of its label. Materials and methods: Authentic Agathosma reference material of A. betulina (n=16) and A. crenulata (n=10) were obtained. Thirteen commercial products were purchased from various health shops around Johannesburg, South Africa, using the search term “Agathosma” or “Buchu.” The plastid regions matK and ycf1 were used to barcode the Buchu products, and BRONX analysis confirmed the taxonomic identity of the samples. UPLC-MS analyses were also performed. Results: Only (30/60) 60% of the traded samples tested from 13 suppliers contained A. betulina in their herbal products. Similar results were also obtained for the UPLC-MS analysis. Conclusion: In this study, we demonstrate the application of DNA barcoding in combination with phytochemical analysis to authenticate herbal products claiming to contain Agathosma plants as an ingredient in their products. This supports manufacturing efforts to ensure that herbal products that are safe for the consumer.

Keywords: Buchu, substitution, barcoding, BRONX algorithm, matK, ycf1, UPLC-MS

Procedia PDF Downloads 129

Authors: Atinkut Atinafu Yilma, Eyob Messele Sefene

Abstract:

Anomaly detection is one of the essential mechanisms to control and reduce production loss, especially in today's smart manufacturing. Quick anomaly detection aids in reducing the cost of production by minimizing the possibility of producing defective products. However, developing an anomaly detection model that can rapidly detect a production change is challenging. This paper proposes Gated Recurrent Unit (GRU) combined with Random Forest (RF) to detect anomalies in the production process in real-time quickly. The GRU is used as a feature detector, and RF as a classifier using the input features from GRU. The model was tested using various synthesis and real-world datasets against benchmark methods. The results show that the proposed GRU-RF outperforms the benchmark methods with the shortest time taken to detect anomalies in the production process. Based on the investigation from the study, this proposed model can eliminate or reduce unnecessary production costs and bring a competitive advantage to manufacturing industries.

Keywords: anomaly detection, multivariate time series data, smart manufacturing, gated recurrent unit network, random forest

Procedia PDF Downloads 118

Authors: Manoj Malviya, Shubham Shinde

Abstract:

The present study focuses on the determination of torsional stiffness of a space frame chassis and comparison of elements used in the Finite Element Analysis of frame. The study also discusses various concepts and design aspects of a space frame chassis with the emphasis on their applicability in BAJA SAE vehicles. Torsional stiffness is a very important factor that determines the chassis strength, vehicle control, and handling. Therefore, it is very important to determine the torsional stiffness of the vehicle before designing an optimum chassis so that it should not fail during extreme conditions. This study determines the torsional stiffness of frame with respect to suspension shocks, roll-stiffness and anti-roll bar rates. A spring model is developed to study the effects of suspension parameters. The engine greatly contributes to torsional stiffness, and therefore, its effects on torsional stiffness need to be considered. Deflections in the tire have not been considered in the present study. The proper element shape should be selected to analyze the effects of various loadings on chassis while implementing finite element methods. The study compares the accuracy of results and computational time for different element types. Shape functions of these elements are also discussed. Modelling methodology is discussed for the multibody analysis of chassis integrated with suspension arms and engine. Proper boundary conditions are presented so as to replicate the real life conditions.

Keywords: space frame chassis, torsional stiffness, multi-body analysis of chassis, element selection

Procedia PDF Downloads 354

Authors: M. Baldini, H. K. Jacobsen, M. Jaccard

Abstract:

In light of the international efforts to comply with the Paris agreement and emission targets for future energy systems, Denmark and Canada are among the front-runner countries dealing with climate change. The experiences in the energy sector have seen both countries coping with trade-offs between investments in renewable energy technologies and energy efficiency, thus tackling the climate issue from the supply and demand side respectively. On the demand side, the industrial sector is going through a remarkable transformation, with implementation of energy efficiency measures, change of input fuel for end-use processes and forecasted electrification as main features under the spotlight. By looking at Canada and Denmark's experiences as pathfinders on the demand and supply approach to climate change, it is possible to obtain valuable experience that may be applied to other countries aiming at the same goal. This paper presents a comparative study on industrial energy efficiency between Canada and Denmark. The study focuses on technologies and system options, policy design and implementation and modelling methodologies when implementing industrial energy savings in optimization models in comparison to simulation models. The study identifies gaps and junctures in the approach towards climate change actions and, learning from each other, lessen the differences to further foster the adoption of energy efficiency measurements in the industrial sector, aiming at reducing energy consumption and, consequently, CO₂ emissions.

Keywords: industrial energy efficiency, comparative study, CO₂ reduction, energy system modelling

Procedia PDF Downloads 172

Authors: Megha Gupta, Nupur Prakash

Abstract:

Identification of plant diseases has been performed using machine learning and deep learning models on the datasets containing images of healthy and diseased plant leaves. The current study carries out an evaluation of some of the deep learning models based on convolutional neural network (CNN) architectures for identification of plant diseases. For this purpose, the publicly available New Plant Diseases Dataset, an augmented version of PlantVillage dataset, available on Kaggle platform, containing 87,900 images has been used. The dataset contained images of 26 diseases of 14 different plants and images of 12 healthy plants. The CNN models selected for the study presented in this paper are AlexNet, ZFNet, VGGNet (four models), GoogLeNet, and ResNet (three models). The selected models are trained using PyTorch, an open-source machine learning library, on Google Colaboratory. A comparative study has been carried out to analyze the high degree of accuracy achieved using these models. The highest test accuracy and F1-score of 99.59% and 0.996, respectively, were achieved by using GoogLeNet with Mini-batch momentum based gradient descent learning algorithm.

Keywords: comparative analysis, convolutional neural networks, deep learning, plant disease identification

Procedia PDF Downloads 198

Authors: Raghunandan Gurumurthy, Aricson Pereira, Sandeep Patil

Abstract:

The escalating adoption of electric vehicles (EVs) across the globe has underscored the critical importance of advancing battery system technologies. This has catalyzed a shift towards the design and development of battery systems that not only exhibit higher energy efficiency but also boast enhanced thermal performance and sophisticated multi-material enclosures. A significant leap in this domain has been the incorporation of simulation-based design optimization for battery packs and Battery Management Systems (BMS), a move further enriched by integrating artificial intelligence/machine learning (AI/ML) approaches. These strategies are pivotal in refining the design, manufacturing, and operational processes for electric vehicles and energy storage systems. By leveraging AI/ML, stakeholders can now predict battery performance metrics—such as State of Health, State of Charge, and State of Power—with unprecedented accuracy. Furthermore, as Li-ion batteries (LIBs) become more prevalent in urban settings, the imperative for bolstering thermal and fire resilience has intensified. This has propelled Battery Thermal Management Systems (BTMs) to the forefront of energy storage research, highlighting the role of machine learning and AI not just as tools for enhanced safety management through accurate temperature forecasts and diagnostics but also as indispensable allies in the early detection and warning of potential battery fires.

Keywords: electric vehicles, battery thermal management, industrial engineering, machine learning, artificial intelligence, manufacturing

Procedia PDF Downloads 97

Authors: Omer Cahana, Ofer Levi, Maya Herman

Abstract:

Magnetic Resonance Imaging (MRI) is a lengthy medical scan that stems from a long acquisition time. Its length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach such as Compress Sensing (CS) or Parallel Imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. To achieve that, two conditions must be satisfied: i) the signal must be sparse under a known transform domain, and ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm must be applied to recover the signal. While the rapid advances in Deep Learning (DL) have had tremendous successes in various computer vision tasks, the field of MRI reconstruction is still in its early stages. In this paper, we present an end-to-end method for MRI reconstruction from k-space to image. Our method contains two parts. The first is sensitivity map estimation (SME), which is a small yet effective network that can easily be extended to a variable number of coils. The second is reconstruction, which is a top-down architecture with lateral connections developed for building high-level refinement at all scales. Our method holds the state-of-art fastMRI benchmark, which is the largest, most diverse benchmark for MRI reconstruction.

Keywords: magnetic resonance imaging, image reconstruction, pyramid network, deep learning

Procedia PDF Downloads 91

Authors: H. Shokouhmand, A. Ghanami

Abstract:

A heat pipe is simple heat transfer device which combines the conduction and phase change phenomena to control the heat transfer without any need for external power source. At a hot surface of the heat pipe, the liquid phase absorbs heat and changes to the vapor phase. The vapor phase flows to condenser region and with the loss of heat changes to the liquid phase. Due to gravitational force the liquid phase flows to the evaporator section. In HVAC systems, the working fluid is chosen based on the operating temperature. The heat pipe has significant capability to reduce the humidity in HVAC systems. Each HVAC system which uses the heater, humidifier, or dryer is a suitable nominate for the utilization of heat pipes. Generally, heat pipes have three main sections: condenser, adiabatic region, and evaporator. Performance investigation and optimization of heat pipes operation in order to increase their efficiency is crucial. In the present article, a parametric study is performed to improve the heat pipe performance. Therefore, the heat capacity of the heat pipe with respect to geometrical and confining parameters is investigated. For the better observation of heat pipe operation in HVAC systems, a CFD simulation in Eulerian-Eulerian multiphase approach is also performed. The results show that heat pipe heat transfer capacity is higher for water as working fluid with the operating temperature of 340 K. It is also showed that the vertical orientation of heat pipe enhances its heat transfer capacity.

Keywords: heat pipe, HVAC system, grooved heat pipe, heat pipe limits

Procedia PDF Downloads 436

Authors: Krishan P. Sharma, T. P. Sharma

Abstract:

Wireless sensor nodes are resource constrained battery powered devices usually deployed in hostile and ill-disposed areas to cooperatively monitor physical or environmental conditions. Due to their limited power supply, the major challenge for researchers is to utilize their battery power for enhancing the lifetime of whole network. Communication and sensing are two major sources of energy consumption in sensor networks. In this paper, we propose a deployment strategy for enhancing the average lifetime of a sensor network by effectively utilizing communication and sensing energy to provide full coverage. The proposed scheme is based on the fact that due to heavy relaying load, sensor nodes near to the sink drain energy at much faster rate than other nodes in the network and consequently die much earlier. To cover this imbalance, proposed scheme finds optimal communication and sensing ranges according to effective load at each node and uses a non-uniform deployment strategy where there is a comparatively high density of nodes near to the sink. Probable relaying load factor at particular node is calculated and accordingly optimal communication distance and sensing range for each sensor node is adjusted. Thus, sensor nodes are placed at locations that optimize energy during network operation. Formal mathematical analysis for calculating optimized locations is reported in present work.

Keywords: load factor, network lifetime, non-uniform deployment, sensing range

Procedia PDF Downloads 383

Authors: Elaid Bouchetob, Bouchra Nadji

Abstract:

This research paper compares the efficiency and reliability (R(t)) of SiC-based and Si-based DC-DC buck converters in thin layer PV systems with an AI-based MPPT controller. Using Simplorer/Simulink simulations, the study assesses their performance under varying conditions. Results show that the SiC-based converter outperforms the Si-based one in efficiency and cost-effectiveness, especially in high temperature and low irradiance conditions. It also exhibits superior reliability, particularly at high temperature and voltage. Reliability calculation (R(t)) is analyzed to assess system performance over time. The SiC-based converter demonstrates better reliability, considering factors like component failure rates and system lifetime. The research focuses on the buck converter's role in charging a Lithium battery within the PV system. By combining the SiC-based converter and AI-based MPPT controller, higher charging efficiency, improved reliability, and cost-effectiveness are achieved. The SiC-based converter proves superior under challenging conditions, emphasizing its potential for optimizing PV system charging. These findings contribute insights into the efficiency, reliability, and reliability calculation of SiC-based and Si-based converters in PV systems. SiC technology's advantages, coupled with advanced control strategies, promote efficient and sustainable energy storage using Lithium batteries. The research supports PV system design and optimization for reliable renewable energy utilization.

Keywords: efficiency, reliability, artificial intelligence, sic device, thin layer, buck converter

Procedia PDF Downloads 62

Authors: Amira Abdelhafez, Rufus Brunt

Abstract:

Geothermal energy in Egypt represents a significant untapped renewable resource that can reduce reliance on conventional power generation. Exploiting these geothermal resources depends on depth, temperature range, and geological characteristics. The intracontinental rift setting of the Gulf of Suez (GoS)-Red Sea rift is a favourable tectonic setting for convection-dominated geothermal plays. The geothermal gradient across the GoS ranges from 24.9 to 86.66 °C/km, with a heat flow of 31-127.2 mW/m². Surface expressions of convective heat loss emerge along the gulf flanks as hot springs (e.g., Hammam Faraun) accompanying deeper geothermal resources. These thermal anomalies are driven mainly by the local tectonic configuration. Characterizing the structural framework of major faults and their control on reservoir properties and subsurface hydrothermal fluid circulation is vital for geothermal applications in the gulf. The geothermal play systems of the GoS depend on structural and lithological properties that contribute to heat storage and vertical transport. Potential geothermal reservoirs include the Nubia sandstones, which, due to their thickness, continuity, and contact with hot basement rocks at a mean depth of 3 km, create an extensive reservoir for geothermal fluids. To develop these geothermal resources for energy production, defining the permeability anisotropy of the reservoir due to faults and facies variation is a crucial step in our study, particularly the evaluation of influence on thermal breakthrough and production rates.

Keywords: geothermal, October field, site specific study, reservoir modelling

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Authors: Hao Mi, Ming Yang, Tian-yue Yang

Abstract:

Ultrasonic infrared nondestructive testing is a kind of testing method with high speed, accuracy and localization. However, there are still some problems, such as the detection requires manual real-time field judgment, the methods of result storage and viewing are still primitive. An intelligent non-destructive detection system based on embedded linux is put forward in this paper. The hardware part of the detection system is based on the ARM (Advanced Reduced Instruction Set Computer Machine) core and an embedded linux system is built to realize image processing and defect detection of thermal images. The CLAHE algorithm and the Butterworth filter are used to process the thermal image, and then the boa server and CGI (Common Gateway Interface) technology are used to transmit the test results to the display terminal through the network for real-time monitoring and remote monitoring. The system also liberates labor and eliminates the obstacle of manual judgment. According to the experiment result, the system provides a convenient and quick solution for industrial non-destructive testing.

Keywords: remote monitoring, non-destructive testing, embedded Linux system, image processing

Procedia PDF Downloads 224

Authors: Kingsley Nnaemeka Ogbu, Constantine Mbajiorgu

Abstract:

Vegetation has a marked effect on runoff and has become an important component in hydrologic model. The watershed Resources Management (WRM) model, a process-based, continuous, distributed parameter simulation model developed for hydrologic and soil erosion studies at the watershed scale lack a crop growth component. As such, this model assumes a constant parameter values for vegetation and hydraulic parameters throughout the duration of hydrologic simulation. Our approach is to develop a crop growth algorithm based on the original plant growth model used in the Environmental Policy Integrated Climate Model (EPIC) model. This paper describes the development of a single crop growth model which has the capability of simulating all crops using unique parameter values for each crop. Simulated crop growth processes will reflect the vegetative seasonality of the natural watershed system. An existing model was employed for evaluating vegetative resistance by hydraulic and vegetative parameters incorporated into the WRM model. The improved WRM model will have the ability to evaluate the seasonal variation of the vegetative roughness coefficient with depth of flow and further enhance the hydrologic model’s capability for accurate hydrologic studies

Keywords: crop yield, roughness coefficient, PAR, WRM model

Procedia PDF Downloads 409

Authors: Nosakhare Enoma, Alphose Zingoni

Abstract:

The requirements for various toroidal shell forms are increasing due to new applications, available storage space and the consideration of appearance. Because of the complexity of some of these structural forms, the finite element method is nowadays mainly used for their analysis, even for simple static studies. This paper presents an easy-to-use analytical algorithm for pressurized multi-segmented toroidal shells of revolution. The membrane solution, which acts as a particular solution of the bending-theory equations, is developed based on membrane theory of shells, and a general approach is formulated for quantifying discontinuity effects at the shell junctions using the well-known Geckeler’s approximation. On superimposing these effects, and applying the ensuing solution to the problem of the pressurized toroid with four segments, closed-form stress results are obtained for the entire toroid. A numerical example is carried out using the developed method. The analytical results obtained show excellent agreement with those from the finite element method, indicating that the proposed method can be also used for complementing and verifying FEM results, and providing insights on other related problems.

Keywords: bending theory of shells, membrane hypothesis, pressurized toroid, segmented toroidal vessel, shell analysis

Procedia PDF Downloads 320

Authors: Vesna Lukovic

Abstract:

Online platforms as new business models play an important role in today’s economy and the functioning of the EU’s internal market. In the travel industry, algorithms used by online platforms for short-stay accommodation provide suggestions and price information to travelers. Those suggestions and recommendations are displayed in search results via recommendation (ranking) systems. There has been a growing consensus that the current legal framework was not sufficient to resolve problems arising from platform practices. In order to enhance the potential of the EU’s Single Market, smaller businesses should be protected, and their rights strengthened vis-à-vis large online platforms. The Regulation (EU) 2019/1150 of the European Parliament and of the Council on promoting fairness and transparency for business users of online intermediation services aims to level the playing field in that respect. This research looks at Airbnb through the lenses of this regulation. The research explores key determinants and finds that although regulation is an important step in the right direction, it is not enough. It does not entail sufficient clarity obligations that would make online platforms an intermediary service which both accommodation providers and travelers could use with ease.

Keywords: algorithm, online platforms, ranking, consumers, EU regulation

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Authors: Mebarek Boukelkoul, Abdelhalim Haroun

Abstract:

By means of the first principle calculation, we have investigated the structural, magnetic and magneto-optical properties of the ultra-thin films of Fen/Cu(001) with (n=1, 2, 3). We adopted a relativistic approach using DFT theorem with local spin density approximation (LSDA). The electronic structure is performed within the framework of the Spin-Polarized Relativistic (SPR) Linear Muffin-Tin Orbitals (LMTO) with the Atomic Sphere Approximation (ASA) method. During the variational principle, the crystal wave function is expressed as a linear combination of the Bloch sums of the so-called relativistic muffin-tin orbitals centered on the atomic sites. The crystalline structure is calculated after an atomic relaxation process using the optimization of the total energy with respect to the atomic interplane distance. A body-centered tetragonal (BCT) pseudomorphic crystalline structure with a tetragonality ratio c/a larger than unity is found. The magnetic behaviour is characterized by an enhanced magnetic moment and a ferromagnetic interplane coupling. The polar magneto-optical Kerr effect spectra are given over a photon energy range extended to 15eV and the microscopic origin of the most interesting features are interpreted by interband transitions. Unlike thin layers, the anisotropy in the ultra-thin films is characterized by a perpendicular magnetization which is perpendicular to the film plane.

Keywords: ultrathin films, magnetism, magneto-optics, pseudomorphic structure

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Authors: Felix Givois, Nicolas R. Gauger, Matthias Kabel

Abstract:

The efficient digital material characterization is of great interest to many fields of application. It consists of the following three steps. First, a 3D reconstruction of 2D scans must be performed. Then, the resulting gray-value image of the material sample is enhanced by image processing methods. Finally, partial differential equations (PDE) are solved on the segmented image, and by averaging the resulting solutions fields, effective properties like stiffness or conductivity can be computed. Due to the high resolution of current CT images, the latter is typically performed with matrix-free solvers. Among them, a solver that uses the explicit formula of the Green-Eshelby operator in Fourier space has been proposed by Moulinec and Suquet. Its algorithmic, most complex part is the Fast Fourier Transformation (FFT). In our talk, we will discuss the potential quantum advantage that can be obtained by replacing the FFT with the Quantum Fourier Transformation (QFT). We will especially show that the data transfer for noisy intermediate-scale quantum (NISQ) devices can be improved by using appropriate boundary conditions for the PDE, which also allows using semi-classical versions of the QFT. In the end, we will compare the results of the QFT-based algorithm for simple geometries with the results of the FFT-based homogenization method.

Keywords: most likelihood amplitude estimation (MLQAE), numerical homogenization, quantum Fourier transformation (QFT), NISQ devises

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Joseph Uzuazomaro Oju

Abstract:

The ease at which manipulated media can be created, and the increasing difficulty in identifying fake media makes it a great threat. Most of the applications used for the creation of these high-quality fake videos and images are built with deep learning. Hence, the use of deep learning in creating a detection mechanism cannot be overemphasized. Any successful fake media that is being detected before it reached the populace will save people from the self-doubt of either a content is genuine or fake and will ensure the credibility of videos and images. The methodology introduced in this paper approaches the manipulated media detection challenge using a combo of VGG-19 and a deep capsule network. In the case of videos, they are converted into frames, which, in turn, are resized and cropped to the face region. These preprocessed images/videos are fed to the VGG-19 network to extract the latent features. The extracted latent features are inputted into a deep capsule network enhanced with a 3D -convolution dynamic routing agreement. The 3D –convolution dynamic routing agreement algorithm helps to reduce the linkages between capsules networks. Thereby limiting the poor learning shortcoming of multiple capsule network layers. The resultant output from the deep capsule network will indicate a media to be either genuine or fake.

Keywords: deep capsule network, dynamic routing, fake media detection, manipulated media

Procedia PDF Downloads 132