Search results for: vector optimization

Authors: Seyed Sadegh Naseralavi, Sadegh Balaghi, Ehsan Khojastehfar

Abstract:

Time history seismic analysis is supposed to be the most accurate method to predict the seismic demand of structures. On the other hand, the required computational time of this method toward achieving the result is its main deficiency. While being applied in optimization process, in which the structure must be analyzed thousands of time, reducing the required computational time of seismic analysis of structures makes the optimization algorithms more practical. Apparently, the invented approximate methods produce some amount of errors in comparison with exact time history analysis but the recently proposed method namely, Complete Quadratic Combination (CQC) and Sum Root of the Sum of Squares (SRSS) drastically reduces the computational time by combination of peak responses in each mode. In the present research, the Basic Modal Displacement (BMD) method is introduced and applied towards estimation of seismic demand of main structure. Seismic demand of sampled structure is estimated by calculation of modal displacement of basic structure (in which the modal displacement has been calculated). Shear steel sampled structures are selected as case studies. The error applying the introduced method is calculated by comparison of the estimated seismic demands with exact time history dynamic analysis. The efficiency of the proposed method is demonstrated by application of three types of earthquakes (in view of time of peak ground acceleration).

Keywords: time history dynamic analysis, basic modal displacement, earthquake-induced demands, shear steel structures

Procedia PDF Downloads 355

Authors: Jean Berger, Nassirou Lo, Martin Noel

Abstract:

Perfectly suited for natural or man-made emergency and disaster management situations such as flood, earthquakes, tornadoes, or tsunami, multi-target search path planning for a team of rescue agents is known to be computationally hard, and most techniques developed so far come short to successfully estimate optimality gap. A novel mixed-integer linear programming (MIP) formulation is proposed to optimally solve the multi-target multi-agent discrete search and rescue (SAR) path planning problem. Aimed at maximizing cumulative probability of successful target detection, it captures anticipated feedback information associated with possible observation outcomes resulting from projected path execution, while modeling agent discrete actions over all possible moving directions. Problem modeling further takes advantage of network representation to encompass decision variables, expedite compact constraint specification, and lead to substantial problem-solving speed-up. The proposed MIP approach uses CPLEX optimization machinery, efficiently computing near-optimal solutions for practical size problems, while giving a robust upper bound obtained from Lagrangean integrality constraint relaxation. Should eventually a target be positively detected during plan execution, a new problem instance would simply be reformulated from the current state, and then solved over the next decision cycle. A computational experiment shows the feasibility and the value of the proposed approach.

Keywords: search path planning, search and rescue, multi-agent, mixed-integer linear programming, optimization

Procedia PDF Downloads 371

Authors: Ghaneshree Moonsamy, Nodumo N. Zulu, Rajesh Lalloo, Suren Singh, Santosh O. Ramchuran

Abstract:

The abalone industry of South Africa is under severe pressure due to illegal harvesting and poaching of this seafood delicacy. These abalones are harvested excessively; as a result, these animals do not have a chance to replace themselves in their habitats, ensuing in a drastic decrease in natural stocks of abalone. Abalone has an extremely slow growth rate and takes approximately four years to reach a size that is market acceptable; therefore, it was imperative to investigate methods to boost the overall growth rate and immunity of the animal. The University of Cape Town (UCT) began to research, which resulted in the isolation of two microorganisms, a yeast isolate Debaryomyces hansenii and a bacterial isolate Vibrio midae, from the gut of the abalone and characterised them for their probiotic abilities. This work resulted in an internationally competitive concept technology that was patented. The next stage of research was to develop a suitable bioprocess to enable commercial production. Numerous steps were taken to develop an efficient production process for V. midae, one of the isolates found by UCT. The initial stages of research involved the development of a stable and robust inoculum and the optimization of physiological growth parameters such as temperature and pH. A range of temperature and pH conditions were evaluated, and data obtained revealed an optimum growth temperature of 30ᵒC and a pH of 6.5. Once these critical growth parameters were established further media optimization studies were performed. Corn steep liquor (CSL) and high test molasses (HTM) were selected as suitable alternatives to more expensive, conventionally used growth medium additives. The optimization of CSL (6.4 g.l⁻¹) and HTM (24 g.l⁻¹) concentrations in the growth medium resulted in a 180% increase in cell concentration, a 5716-fold increase in cell productivity and a 97.2% decrease in the material cost of production in comparison to conventional growth conditions and parameters used at the onset of the study. In addition, a stable market-ready liquid probiotic product, encompassing the viable but not culturable (VBNC) state of Vibrio midae cells, was developed during the downstream processing aspect of the study. The demonstration of this technology at a full manufacturing scale has further enhanced the attractiveness and commercial feasibility of this production process.

Keywords: probiotics, abalone aquaculture, bioprocess technology, manufacturing scale technology development

Procedia PDF Downloads 152

Authors: Omar Alzeley, Sergey Utev

Abstract:

Minimizing a constrained multivariate function is the fundamental of Machine learning, and these algorithms are at the core of data mining and data visualization techniques. The decision function that maps input points to output points is based on the result of optimization. This optimization is the central of learning theory. One approach to complex systems where the dynamics of the system is inferred by a statistical analysis of the fluctuations in time of some associated observable is time series analysis. The purpose of this paper is a mathematical transition from the autoregressive model of classical time series to the matrix formalization of quantum theory. Firstly, we have proposed a quantum time series model (QTS). Although Hamiltonian technique becomes an established tool to detect a deterministic chaos, other approaches emerge. The quantum probabilistic technique is used to motivate the construction of our QTS model. The QTS model resembles the quantum dynamic model which was applied to financial data. Secondly, various statistical methods, including machine learning algorithms such as the Kalman filter algorithm, are applied to estimate and analyses the unknown parameters of the model. Finally, simulation techniques such as Markov chain Monte Carlo have been used to support our investigations. The proposed model has been examined by using real and simulated data. We establish the relation between quantum statistical machine and quantum time series via random matrix theory. It is interesting to note that the primary focus of the application of QTS in the field of quantum chaos was to find a model that explain chaotic behaviour. Maybe this model will reveal another insight into quantum chaos.

Keywords: machine learning, simulation techniques, quantum probability, tensor product, time series

Procedia PDF Downloads 469

Authors: Anders Schou Simonsen, Kim Sorensen, Thomas Condra

Abstract:

Wet scrubbers are used for flue gas desulfurization by injecting water directly into the flue gas stream from a set of sprayers. The water droplets will flow freely inside the scrubber, and flow down along the scrubber walls as a thin wall film while reacting with the gas phase to remove SO₂. This complex multiphase phenomenon can be divided into three main contributions: the continuous gas phase, the liquid droplet phase, and the liquid wall film phase. This study proposes a complete model, where all three main contributions are taken into account and resolved using OpenFOAM for the continuous gas phase, and MATLAB for the liquid droplet and wall film phases. The 3D continuous gas phase is composed of five species: CO₂, H₂O, O₂, SO₂, and N₂, which are resolved along with momentum, energy, and turbulence. Source terms are present for four species, energy and momentum, which are affecting the steady-state solution. The liquid droplet phase experiences breakup, collisions, dynamics, internal chemistry, evaporation and condensation, species mass transfer, energy transfer and wall film interactions. Numerous sub-models have been implemented and coupled to realise the above-mentioned phenomena. The liquid wall film experiences impingement, acceleration, atomization, separation, internal chemistry, evaporation and condensation, species mass transfer, and energy transfer, which have all been resolved using numerous sub-models as well. The continuous gas phase has been coupled with the liquid phases using source terms by an approach, where the two software packages are couples using a link-structure. The complete CFD model has been verified using 16 experimental tests from an existing scrubber installation, where a gradient-based pattern search optimization algorithm has been used to tune numerous model parameters to match the experimental results. The CFD model needed to be fast for evaluation in order to apply this optimization routine, where approximately 1000 simulations were needed. The results show that the complex multiphase phenomena governing wet scrubbers can be resolved in a single model. The optimization routine was able to tune the model to accurately predict the performance of an existing installation. Furthermore, the study shows that a coupling between OpenFOAM and MATLAB is realizable, where the data and source term exchange increases the computational requirements by approximately 5%. This allows for exploiting the benefits of both software programs.

Keywords: desulfurization, discrete phase, scrubber, wall film

Procedia PDF Downloads 265

Authors: Andreas Lind, Aitor Iriondo Pascual, Dan Hogberg, Lars Hanson

Abstract:

Factory setup lifecycles are most often described and prepared in CAD environments; the preparation is based on experience and inputs from several cross-disciplinary processes. Early in the factory setup preparation, a so-called block layout is created. The intention is to describe a high-level view of the intended factory setup and to claim area reservations and allocations. Factory areas are then blocked, i.e., targeted to be used for specific intended resources and processes, later redefined with detailed factory setup layouts. Each detailed layout is based on the block layout and inputs from cross-disciplinary preparation processes, such as manufacturing sequence, productivity, workers’ workplace requirements, and resource setup preparation. However, this activity is often not carried out with all variables considered simultaneously, which might entail a risk of sub-optimizing the detailed layout based on manual decisions. Therefore, this work aims to realize a digital method for assembly manufacturing layout planning where productivity, area utilization, and ergonomics can be considered simultaneously in a cross-disciplinary manner. The purpose of the digital method is to support engineers in finding optimized designs of detailed layouts for assembly manufacturing factories, thereby facilitating better decisions regarding setups of future factories. Input datasets are company-specific descriptions of required dimensions for specific area reservations, such as defined dimensions of a worker’s workplace, material façades, aisles, and the sequence to realize the product assembly manufacturing process. To test and iteratively develop the digital method, a demonstrator has been developed with an adaptation of existing software that simulates and proposes optimized designs of detailed layouts. Since the method is to consider productivity, ergonomics, area utilization, and constraints from the automatically generated block layout, a multi-objective optimization approach is utilized. In the demonstrator, the input data are sent to the simulation software industrial path solutions (IPS). Based on the input and Lua scripts, the IPS software generates a block layout in compliance with the company’s defined dimensions of area reservations. Communication is then established between the IPS and the software EPP (Ergonomics in Productivity Platform), including intended resource descriptions, assembly manufacturing process, and manikin (digital human) resources. Using multi-objective optimization approaches, the EPP software then calculates layout proposals that are sent iteratively and simulated and rendered in IPS, following the rules and regulations defined in the block layout as well as productivity and ergonomics constraints and objectives. The software demonstrator is promising. The software can handle several parameters to optimize the detailed layout simultaneously and can put forward several proposals. It can optimize multiple parameters or weight the parameters to fine-tune the optimal result of the detailed layout. The intention of the demonstrator is to make the preparation between cross-disciplinary silos transparent and achieve a common preparation of the assembly manufacturing factory setup, thereby facilitating better decisions.

Keywords: factory setup, multi-objective, optimization, simulation

Procedia PDF Downloads 152

Authors: Thamir K. Ibrahim, M. M. Rahman, Marwah Noori Mohammed

Abstract:

This study deals with modeling and performance enhancements of a gas-turbine combined cycle power plant. A clean and safe energy is the greatest challenges to meet the requirements of the green environment. These requirements have given way the long-time governing authority of steam turbine (ST) in the world power generation, and the gas turbine (GT) will replace it. Therefore, it is necessary to predict the characteristics of the GT system and optimize its operating strategy by developing a simulation system. The integrated model and simulation code for exploiting the performance of gas turbine power plant are developed utilizing MATLAB code. The performance code for heavy-duty GT and CCGT power plants are validated with the real power plant of Baiji GT and MARAFIQ CCGT plants the results have been satisfactory. A new technology of correlation was considered for all types of simulation data; whose coefficient of determination (R2) was calculated as 0.9825. Some of the latest launched correlations were checked on the Baiji GT plant and apply error analysis. The GT performance was judged by particular parameters opted from the simulation model and also utilized Adaptive Neuro-Fuzzy System (ANFIS) an advanced new optimization technology. The best thermal efficiency and power output attained were about 56% and 345MW respectively. Thus, the operation conditions and ambient temperature are strongly influenced on the overall performance of the GT. The optimum efficiency and power are found at higher turbine inlet temperatures. It can be comprehended that the developed models are powerful tools for estimating the overall performance of the GT plants.

Keywords: gas turbine, optimization, ANFIS, performance, operating conditions

Procedia PDF Downloads 425

Authors: Yaseri Dahlia Apritasari

Abstract:

Visual comfort is important for the building occupants to need. Visual comfort can be fulfilled through natural lighting (daylighting) and artificial lighting. One strategy to optimize natural lighting can be achieved through the facade second skin design. This strategy can reduce glare, and fulfill visual comfort need. However, the design strategy cannot achieve light intensity for visual comfort. Because the materials, design and opening percentage of the facade of second skin blocked sunlight. This paper discusses aluminum material for the facade second skin design that can fulfill the optimal visual comfort with the case studies Multi Media Tower building. The methodology of the research is combination quantitative and qualitative through field study observed, lighting measurement and visual comfort questionnaire. Then it used too simulation modeling (DIALUX 4.13, 2016) for three facades second skin design model. Through following steps; (1) Measuring visual comfort factor: light intensity indoor and outdoor; (2) Taking visual comfort data from building occupants; (3) Making models with different facade second skin design; (3) Simulating and analyzing the light intensity value for each models that meet occupants visual comfort standard: 350 lux (Indonesia National Standard, 2010). The result shows that optimization of aluminum material for the facade second skin design can meet optimal visual comfort for building occupants. The result can give recommendation aluminum opening percentage of the facade second skin can meet optimal visual comfort for building occupants.

Keywords: aluminium material, Facade, second skin, visual comfort

Procedia PDF Downloads 352

Authors: Saqib Aziz, Brad Alexander, Christoph Gengnagel, Stefan Weinzierl

Abstract:

This paper explores a generative design, simulation, and optimization workflow for the integration of acoustical diffuser and/or absorber geometry with embedded coupled Helmholtz-resonators for full-scale 3D printed building components. Large-scale additive manufacturing in conjunction with algorithmic CAD design tools enables a vast amount of control when creating geometry. This is advantageous regarding the increasing demands of comfort standards for indoor spaces and the use of more resourceful and sustainable construction methods and materials. The presented methodology highlights these new technological advancements and offers a multimodal and integrative design solution with the potential for an immediate application in the AEC-Industry. In principle, the methodology can be applied to a wide range of structural elements that can be manufactured by additive manufacturing processes. The current paper focuses on a case study of an application for a biaxial load-bearing beam grillage made of reinforced concrete, which allows for a variety of applications through the combination of additive prefabricated semi-finished parts and in-situ concrete supplementation. The semi-prefabricated parts or formwork bodies form the basic framework of the supporting structure and at the same time have acoustic absorption and diffusion properties that are precisely acoustically programmed for the space underneath the structure. To this end, a hybrid validation strategy is being explored using a digital and cross-platform simulation environment, verified with physical prototyping. The iterative workflow starts with the generation of a parametric design model for the acoustical geometry using the algorithmic visual scripting editor Grasshopper3D inside the building information modeling (BIM) software Revit. Various geometric attributes (i.e., bottleneck and cavity dimensions) of the resonator are parameterized and fed to a numerical optimization algorithm which can modify the geometry with the goal of increasing absorption at resonance and increasing the bandwidth of the effective absorption range. Using Rhino.Inside and LiveLink for Revit, the generative model was imported directly into the Multiphysics simulation environment COMSOL. The geometry was further modified and prepared for simulation in a semi-automated process. The incident and scattered pressure fields were simulated from which the surface normal absorption coefficients were calculated. This reciprocal process was repeated to further optimize the geometric parameters. Subsequently the numerical models were compared to a set of 3D concrete printed physical twin models, which were tested in a .25 m x .25 m impedance tube. The empirical results served to improve the starting parameter settings of the initial numerical model. The geometry resulting from the numerical optimization was finally returned to grasshopper for further implementation in an interdisciplinary study.

Keywords: acoustical design, additive manufacturing, computational design, multimodal optimization

Procedia PDF Downloads 159

Authors: Kamyar Tolouei, Ehsan Moosavi

Abstract:

In open-pit mines, the long-term production scheduling optimization problem (LTPSOP) is a complicated problem that contains constraints, large datasets, and uncertainties. Uncertainty in the output is caused by several geological, economic, or technical factors. Due to its dimensions and NP-hard nature, it is usually difficult to find an ideal solution to the LTPSOP. The optimal schedule generally restricts the ore, metal, and waste tonnages, average grades, and cash flows of each period. Past decades have witnessed important measurements of long-term production scheduling and optimal algorithms since researchers have become highly cognizant of the issue. In fact, it is not possible to consider LTPSOP as a well-solved problem. Traditional production scheduling methods in open-pit mines apply an estimated orebody model to produce optimal schedules. The smoothing result of some geostatistical estimation procedures causes most of the mine schedules and production predictions to be unrealistic and imperfect. With the expansion of simulation procedures, the risks from grade uncertainty in ore reserves can be evaluated and organized through a set of equally probable orebody realizations. In this paper, to synthesize grade uncertainty into the strategic mine schedule, a stochastic integer programming framework is presented to LTPSOP. The objective function of the model is to maximize the net present value and minimize the risk of deviation from the production targets considering grade uncertainty simultaneously while satisfying all technical constraints and operational requirements. Instead of applying one estimated orebody model as input to optimize the production schedule, a set of equally probable orebody realizations are applied to synthesize grade uncertainty in the strategic mine schedule and to produce a more profitable and risk-based production schedule. A mixture of metaheuristic procedures and mathematical methods paves the way to achieve an appropriate solution. This paper introduced a hybrid model between the augmented Lagrangian relaxation (ALR) method and the metaheuristic algorithm, the Harris Hawks optimization (HHO), to solve the LTPSOP under grade uncertainty conditions. In this study, the HHO is experienced to update Lagrange coefficients. Besides, a machine learning method called Random Forest is applied to estimate gold grade in a mineral deposit. The Monte Carlo method is used as the simulation method with 20 realizations. The results specify that the progressive versions have been considerably developed in comparison with the traditional methods. The outcomes were also compared with the ALR-genetic algorithm and ALR-sub-gradient. To indicate the applicability of the model, a case study on an open-pit gold mining operation is implemented. The framework displays the capability to minimize risk and improvement in the expected net present value and financial profitability for LTPSOP. The framework could control geological risk more effectively than the traditional procedure considering grade uncertainty in the hybrid model framework.

Keywords: grade uncertainty, metaheuristic algorithms, open-pit mine, production scheduling optimization

Procedia PDF Downloads 105

Authors: Nabilah Filzah Mohd Radzuan, Andi Putra, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

Abstract:

Precipitation forecast is important to avoid natural disaster incident which can cause losses in the involved area. This paper reviews three techniques logistic regression, decision tree, and random forest which are used in making precipitation forecast. These combination techniques through the vector auto-regression (VAR) model help in finding the advantages and strengths of each technique in the forecast process. The data-set contains variables of the rain’s domain. Adaptation of artificial intelligence techniques involved in rain domain enables the forecast process to be easier and systematic for precipitation forecast.

Keywords: logistic regression, decisions tree, random forest, VAR model

Procedia PDF Downloads 446

Authors: Daleesha M. Viswanathan, Sumam Mary Idicula

Abstract:

Dynamic hand gestures are an intrinsic component in sign language communication. Extracting spatial temporal features of the hand gesture trajectory plays an important role in a dynamic gesture recognition system. Finding a discrete feature descriptor for the motion trajectory based on the orientation feature is the main concern of this paper. Kalman filter algorithm and Hidden Markov Models (HMM) models are incorporated with this recognition system for hand trajectory tracking and for spatial temporal classification, respectively.

Keywords: orientation features, discrete feature vector, HMM., Indian sign language

Procedia PDF Downloads 372

Authors: Ahmad FAROOQ, Xinfeng Zhang, Fahad Sabah, Raheem Sarwar

Abstract:

In Traditional Chinese Medicine, tongue diagnosis is a vital inspection tool (TCM). In this study, we explore the potential of machine learning in tongue diagnosis. It begins with the cataloguing of the various classifications and characteristics of the human tongue. We infer 24 kinds of tongues from the material and coating of the tongue, and we identify 21 attributes of the tongue. The next step is to apply machine learning methods to the tongue dataset. We use the Weka machine learning platform to conduct the experiment for performance analysis. The 457 instances of the tongue dataset are used to test the performance of five different machine learning methods, including SVM, Random Forests, Decision Trees, and Naive Bayes. Based on accuracy and Area under the ROC Curve, the Support Vector Machine algorithm was shown to be the most effective for tongue diagnosis (AUC).

Keywords: medical imaging, image retrieval, machine learning, tongue

Procedia PDF Downloads 81

Authors: Yuan Huang, Xiangping Chen, Xiaonan Luo

Abstract:

Current software are continuously updating. The change between two versions usually involves multiple program entities (e.g., packages, classes, methods, attributes) with multiple purposes (e.g., changed requirements, bug fixing). It is hard for developers to understand which changes are made for the same purpose. Whether two changes are related is not decided by the relationship between this two entities in the program. In this paper, we summarized 4 coupling rules(16 instances) and 4 state-combination types at the class, method and attribute levels for software change. Related Change Vector (RCV) are defined based on coupling rules and state-combination types, and applied to classify related software changes by using Probabilistic Neural Network during a software updating.

Keywords: PNN, related change, state-combination, logical coupling, software entity

Procedia PDF Downloads 437

Authors: Farid Khosravikia, Patricia Clayton

Abstract:

The main objective of this study is to evaluate the advantages and disadvantages of various machine learning techniques in forecasting ground-motion intensity measures given source characteristics, source-to-site distance, and local site condition. Intensity measures such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Estimating these variables for future earthquake events is a key step in seismic hazard assessment and potentially subsequent risk assessment of different types of structures. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as a statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The algorithms are adjusted to quantify event-to-event and site-to-site variability of the ground motions by implementing them as random effects in the proposed models to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4,528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4 to 500 km in Oklahoma, Kansas, and Texas since 2005. The main reason of the considered database stems from the recent increase in the seismicity rate of these states attributed to petroleum production and wastewater disposal activities, which necessities further investigation in the ground motion models developed for these states. Accuracy of the models in predicting intensity measures, generalization capability of the models for future data, as well as usability of the models are discussed in the evaluation process. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available.

Keywords: artificial neural network, ground-motion models, machine learning, random forest, support vector machine

Procedia PDF Downloads 122

Authors: Muhamad Sani Buang, Shahrul Azam Abdullah, Juri Saedon

Abstract:

There is not much effective guideline on development of design parameters selection on springback for advanced high strength steel sheet metal in U-channel process during cold forming process. This paper presents the development of predictive model for springback in U-channel process on advanced high strength steel sheet employing Response Surface Methodology (RSM). The experimental was performed on dual phase steel sheet, DP590 in U-channel forming process while design of experiment (DoE) approach was used to investigates the effects of four factors namely blank holder force (BHF), clearance (C) and punch travel (Tp) and rolling direction (R) were used as input parameters using two level values by applying Full Factorial design (24). From a statistical analysis of variant (ANOVA), result showed that blank holder force (BHF), clearance (C) and punch travel (Tp) displayed significant effect on springback of flange angle (β2) and wall opening angle (β1), while rolling direction (R) factor is insignificant. The significant parameters are optimized in order to reduce the springback behavior using Central Composite Design (CCD) in RSM and the optimum parameters were determined. A regression model for springback was developed. The effect of individual parameters and their response was also evaluated. The results obtained from optimum model are in agreement with the experimental values

Keywords: advance high strength steel, u-channel process, springback, design of experiment, optimization, response surface methodology (rsm)

Procedia PDF Downloads 541

Authors: Pierluigi Gallo, Francesco Randazzo, Ignazio Gallo

Abstract:

In recent years, many new and interesting models of successful online business have been developed. Many of these are based on the competition between users, such as online auctions, where the product price is not fixed and tends to rise. Other models, including group-buying, are based on cooperation between users, characterized by a dynamic price of the product that tends to go down. There is not yet a business model in which both sellers and buyers are grouped in order to negotiate on a specific product or service. The present study investigates a new extension of the group-buying model, called fair, which allows aggregation of demand and supply for price optimization, in a cooperative manner. Additionally, our system also aggregates products and destinations for shipping optimization. We introduced the following new relevant input parameters in order to implement a double-side aggregation: (a) price-quantity curves provided by the seller; (b) waiting time, that is, the longer buyers wait, the greater discount they get; (c) payment time, which determines if the buyer pays before, during or after receiving the product; (d) the distance between the place where products are available and the place of shipment, provided in advance by the buyer or dynamically suggested by the system. To analyze the proposed model we implemented a system prototype and a simulator that allows studying effects of changing some input parameters. We analyzed the dynamic price model in fairs having one single seller and a combination of selected sellers. The results are very encouraging and motivate further investigation on this topic.

Keywords: auction, aggregation, fair, group buying, social buying

Procedia PDF Downloads 294

Authors: Fariba Tadayon, Nika Gharahgolooyan, Ateke Tadayon, Mostafa Jafarian

Abstract:

Carotenoid pigments are a various group of lipophilic compounds that generate the yellow to red colors of many plants, foods and flowers. A well-known type of carotenoids which is pro-vitamin A is β-carotene. Due to the color of citrus fruit’s peel, the peel can be a good source of different carotenoids. Ostrich oil is one of the most valuable foundations in many branches of industry, medicine, cosmetics and nutrition. The animal-based ostrich oil could be considered as an alternative and green solvent. Following this study, wastes of citrus peel will recycle by a simple method and extracted carotenoids can increase properties of ostrich oil. In this work, a simple and efficient method for extraction of carotenoids from tangerine peel was designed. Ultrasound-assisted extraction (UAE) showed significant effect on the extraction rate by increasing the mass transfer rate. Ostrich oil can be used as a green solvent in many studies to eliminate petroleum-based solvents. Since tangerine peel is a complex source of different carotenoids separation and determination was performed by high-performance liquid chromatography (HPLC). In addition, the ability of ostrich oil and sunflower oil in carotenoid extraction from tangerine peel and carrot was compared. The highest yield of β-carotene extracted from tangerine peel using sunflower oil and ostrich oil were 75.741 and 88.110 (mg/L), respectively. Optimization of the process was achieved by response surface methodology (RSM) and the optimal extraction conditions were tangerine peel powder particle size of 0.180 mm, ultrasonic intensity of 19 W/cm2 and sonication time of 30 minutes.

Keywords: β-carotene, carotenoids, citrus peel, ostrich oil, response surface methodology, ultrasound-assisted extraction

Procedia PDF Downloads 316

Authors: Sachi Nandan Mohanty, Shreya Sinha, Sweeti Sah, Shweta Sharma

Abstract:

The advancement of natural language processing has majorly influenced the field of medical transcript classification, providing a robust framework for enhancing the accuracy of clinical data processing. It has enormous potential to transform healthcare and improve people's livelihoods. This research focuses on improving the accuracy of medical transcript categorization using Bidirectional Encoder Representations from Transformers (BERT) and its specialized variants, including BioBERT, ClinicalBERT, SciBERT, and BlueBERT. The experimental work employs Optuna, an optimization framework, for hyperparameter tuning to identify the most effective variant, concluding that BioBERT yields the best performance. Furthermore, various optimizers, including Adam, RMSprop, and Layerwise adaptive large batch optimization (LAMB), were evaluated alongside BERT's default AdamW optimizer. The findings show that the LAMB optimizer achieves equally good performance as AdamW. Synthetic data generation techniques from Gretel were utilized to augment the dataset, expanding the original dataset from 5,000 to 10,000 rows. Subsequent evaluations demonstrated that the model maintained its performance with synthetic data, with the LAMB optimizer showing marginally better results. The enhanced dataset and optimized model configurations improved classification accuracy, showcasing the efficacy of the BioBERT variant and the LAMB optimizer. It resulted in an accuracy of up to 98.2% and 90.8% for the original and combined datasets, respectively.

Keywords: BioBERT, clinical data, healthcare AI, transformer models

Procedia PDF Downloads 2

Authors: Tudor Barbu

Abstract:

An automatic moment-based texture segmentation approach is proposed in this paper. First, we describe the related work in this computer vision domain. Our texture feature extraction, the first part of the texture recognition process, produces a set of moment-based feature vectors. For each image pixel, a texture feature vector is computed as a sequence of area moments. Second, an automatic pixel classification approach is proposed. The feature vectors are clustered using some unsupervised classification algorithm, the optimal number of clusters being determined using a measure based on validation indexes. From the resulted pixel classes one determines easily the desired texture regions of the image.

Keywords: image segmentation, moment-based, texture analysis, automatic classification, validation indexes

Procedia PDF Downloads 416

Authors: Mohd Khairul Bazli Mohd Aziz, Fadhilah Yusof, Zulkifli Yusop, Zalina Mohd Daud, Mohammad Afif Kasno

Abstract:

Recent developed rainfall network design techniques have been discussed and compared by many researchers worldwide due to the demand of acquiring higher levels of accuracy from collected data. In many studies, rain-gauge networks are designed to provide good estimation for areal rainfall and for flood modelling and prediction. In a certain study, even using lumped models for flood forecasting, a proper gauge network can significantly improve the results. Therefore existing rainfall network in Johor must be optimized and redesigned in order to meet the required level of accuracy preset by rainfall data users. The well-known geostatistics method (variance-reduction method) that is combined with simulated annealing was used as an algorithm of optimization in this study to obtain the optimal number and locations of the rain gauges. Rain gauge network structure is not only dependent on the station density; station location also plays an important role in determining whether information is acquired accurately. The existing network of 84 rain gauges in Johor is optimized and redesigned by using rainfall, humidity, solar radiation, temperature and wind speed data during monsoon season (November – February) for the period of 1975 – 2008. Three different semivariogram models which are Spherical, Gaussian and Exponential were used and their performances were also compared in this study. Cross validation technique was applied to compute the errors and the result showed that exponential model is the best semivariogram. It was found that the proposed method was satisfied by a network of 64 rain gauges with the minimum estimated variance and 20 of the existing ones were removed and relocated. An existing network may consist of redundant stations that may make little or no contribution to the network performance for providing quality data. Therefore, two different cases were considered in this study. The first case considered the removed stations that were optimally relocated into new locations to investigate their influence in the calculated estimated variance and the second case explored the possibility to relocate all 84 existing stations into new locations to determine the optimal position. The relocations of the stations in both cases have shown that the new optimal locations have managed to reduce the estimated variance and it has proven that locations played an important role in determining the optimal network.

Keywords: geostatistics, simulated annealing, semivariogram, optimization

Procedia PDF Downloads 303

Authors: Nilubon Kurubanjerdjit, Nattakarn Iam-On, Ka-Lok Ng

Abstract:

MicroRNAs are small non-coding RNA found in many different species. They play crucial roles in cancer such as biological processes of apoptosis and proliferation. The identification of microRNA-target genes can be an essential first step towards to reveal the role of microRNA in various cancer types. In this paper, we predict miRNA-target genes for lung cancer by integrating prediction scores from miRanda and PITA algorithms used as a feature vector of miRNA-target interaction. Then, machine-learning algorithms were implemented for making a final prediction. The approach developed in this study should be of value for future studies into understanding the role of miRNAs in molecular mechanisms enabling lung cancer formation.

Keywords: microRNA, miRNAs, lung cancer, machine learning, Naïve Bayes, SVM

Procedia PDF Downloads 400

Authors: T. Thein, S. Kalyar Myo

Abstract:

Human uses visual information for understanding the speech contents in noisy conditions or in situations where the audio signal is not available. The primary advantage of visual information is that it is not affected by the acoustic noise and cross talk among speakers. Using visual information from the lip movements can improve the accuracy and robustness of automatic speech recognition. However, a major challenge with most automatic lip reading system is to find a robust and efficient method for extracting the linguistically relevant speech information from a lip image sequence. This is a difficult task due to variation caused by different speakers, illumination, camera setting and the inherent low luminance and chrominance contrast between lip and non-lip region. Several researchers have been developing methods to overcome these problems; the one is lip reading. Moreover, it is well known that visual information about speech through lip reading is very useful for human speech recognition system. Lip reading is the technique of a comprehensive understanding of underlying speech by processing on the movement of lips. Therefore, lip reading system is one of the different supportive technologies for hearing impaired or elderly people, and it is an active research area. The need for lip reading system is ever increasing for every language. This research aims to develop a visual teaching method system for the hearing impaired persons in Myanmar, how to pronounce words precisely by identifying the features of lip movement. The proposed research will work a lip reading system for Myanmar Consonants, one syllable consonants (င (Nga)၊ ည (Nya)၊ မ (Ma)၊ လ (La)၊ ၀ (Wa)၊ သ (Tha)၊ ဟ (Ha)၊ အ (Ah) ) and two syllable consonants ( က(Ka Gyi)၊ ခ (Kha Gway)၊ ဂ (Ga Nge)၊ ဃ (Ga Gyi)၊ စ (Sa Lone)၊ ဆ (Sa Lain)၊ ဇ (Za Gwe) ၊ ဒ (Da Dway)၊ ဏ (Na Gyi)၊ န (Na Nge)၊ ပ (Pa Saug)၊ ဘ (Ba Gone)၊ ရ (Ya Gaug)၊ ဠ (La Gyi) ). In the proposed system, there are three subsystems, the first one is the lip localization system, which localizes the lips in the digital inputs. The next one is the feature extraction system, which extracts features of lip movement suitable for visual speech recognition. And the final one is the classification system. In the proposed research, Two Dimensional Discrete Cosine Transform (2D-DCT) and Linear Discriminant Analysis (LDA) with Active Contour Model (ACM) will be used for lip movement features extraction. Support Vector Machine (SVM) classifier is used for finding class parameter and class number in training set and testing set. Then, experiments will be carried out for the recognition accuracy of Myanmar consonants using the only visual information on lip movements which are useful for visual speech of Myanmar languages. The result will show the effectiveness of the lip movement recognition for Myanmar Consonants. This system will help the hearing impaired persons to use as the language learning application. This system can also be useful for normal hearing persons in noisy environments or conditions where they can find out what was said by other people without hearing voice.

Keywords: feature extraction, lip reading, lip localization, Active Contour Model (ACM), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Two Dimensional Discrete Cosine Transform (2D-DCT)

Procedia PDF Downloads 286

Authors: Muhammad Umair Shahid, Abdul Rehman, Mudassir Mukhtar, Muhammad Nauman

Abstract:

The smart antenna is the prominent technology that has become known in recent years to meet the growing demands of wireless communications. In an overcrowded atmosphere, its application is growing gradually. A methodical evaluation of the performance of Fixed Beamforming algorithms for smart antennas such as Multiple Sidelobe Canceller (MSC), Maximum Signal-to-interference ratio (MSIR) and minimum variance (MVDR) has been comprehensively presented in this paper. Simulation results show that beamforming is helpful in providing optimized response towards desired directions. MVDR beamformer provides the most optimal solution.

Keywords: fixed weight beamforming, array pattern, signal to interference ratio, power efficiency, element spacing, array elements, optimum weight vector

Procedia PDF Downloads 184

Authors: S. M. Hamidi, M. Zamani

Abstract:

Magneto-plasmonic (MP) structures have turned into essential tools for the amplification of magneto-optical (MO) responses via the combination of MO activity and surface Plasmon resonance (SPR). Both the plasmonic and the MO properties of the resulting MP structure become interrelated because the SPR of the metallic medium. This interconnection can be modified the wave vector of surface plasmon polariton (SPP) in MP multilayer [1] or enhanced the MO activity [2- 3] and also modified the sensor responses [4]. There are several types of MP structures which are studied to enhance MO response in miniaturized configuration. In this paper, we propose a new MP structure based on the nano-metal grating and we investigate the MO and optical properties of this new structure. Our new MP structure fabricate by DC magnetron sputtering method and our home made MO experimental setup use for characterization of the structure.

Keywords: Magneto-plasmonic structures, magneto-optical effect, nano-garting

Procedia PDF Downloads 563

Authors: Guanglin Song

Abstract:

(Objective) Situated within the context of China's tiered medical treatment system, this study aims to analyze spatial causes of urban healthcare access difficulties from the perspective of the configuration of healthcare facilities. (Methods) A social network analysis approach is employed to construct a healthcare demand and supply flow network between major residential clusters and various tiers of hospitals in the city.(Conclusion) The findings reveal that:1.there exists overall maldistribution and over-concentration of healthcare resources in Study Area, characterized by structural imbalance; 2.the low rate of primary care utilization in Study Area is a key factor contributing to congestion at higher-tier hospitals, as excessive reliance on these institutions by neighboring communities exacerbates the problem; 3.gradual optimization of the healthcare facility layout in Study Area, encompassing holistic, local, and individual institutional levels, can enhance systemic efficiency and resource balance.(Prospects) This research proposes a method for evaluating urban healthcare resource distribution structures based on service flows within hierarchical networks. It offers spatially targeted optimization suggestions for promoting the implementation of the tiered healthcare system and alleviating challenges related to accessibility and congestion in seeking medical care. Provide some new ideas for researchers and healthcare managers in countries, cities, and healthcare management around the world with similar challenges.

Keywords: flow of public services, urban networks, healthcare facilities, spatial planning, urban networks

Procedia PDF Downloads 68

Authors: Blaž Likozar, Andraž Pavlišič, Matic Pavlin, Taja Žibert, Aleksandra Zamljen, Sašo Gyergyek, Matej Huš

Abstract:

Ammonia is gaining recognition as a carbon-free fuel for energy-intensive applications, particularly transportation, industry, and power generation. Due to its physical properties, high energy density of 3 kWh kg-1, and high gravimetric hydrogen capacity of 17.6 wt%, ammonia is an efficient energy vector for green hydrogen, capable of mitigating hydrogen’s storage, distribution, and infrastructure deployment limitations. Chemicalstorage in the form of ammonia provides an efficient and affordable solution for energy storage, which is currently a critical step in overcoming the intermittency of abundant renewable energy sources with minimal or no environmental impact. Experiments were carried out to validate the modelling in a packed bed reactor, which proved to be agreeing.

Keywords: hydrogen, ammonia, catalysis, modelling, kinetics

Procedia PDF Downloads 69

Authors: Javier Navarro Garcia, Narciso Vazquez Carretero

Abstract:

Eurocode 1, part 1-4, wind actions, includes in its article 1.5 the possibility of using numerical calculation methods to obtain information on the loads acting on a building. On the other hand, the analysis using computational fluids dynamics (CFD) in aerospace, aeronautical, and industrial applications is already in widespread use. The application of techniques based on CFD analysis on the building to study its aerodynamic behavior now opens a whole alternative field of possibilities for civil engineering and architecture; optimization of the results with respect to those obtained by applying the regulations, the possibility of obtaining information on pressures, speeds at any point of the model for each moment, the analysis of turbulence and the possibility of modeling any geometry or configuration. The present work compares the results obtained on a building, with respect to its aerodynamic behavior, from a mathematical model based on the analysis by CFD with the results obtained by applying Eurocode1, part1-4, wind actions. It is verified that the results obtained by CFD techniques suppose an optimization of the wind action that acts on the building with respect to the wind action obtained by applying the Eurocode1, part 1-4, wind actions. In order to carry out this verification, a 45m high square base truncated pyramid building has been taken. The mathematical model on CFD, based on finite volumes, has been calculated using the FLUENT commercial computer application using a scale-resolving simulation (SRS) type large eddy simulation (LES) turbulence model for an atmospheric boundary layer wind with turbulent component in the direction of the flow.

Keywords: aerodynamic, CFD, computacional fluids dynamics, computational mechanics

Procedia PDF Downloads 137

Authors: Peng Zhang, Cai Liang

Abstract:

The non-biodegradable plastic wastes have posed severe environmental and ecological contaminations. Numerous technologies, such as pyrolysis, incineration, and landfilling, have already been employed for the treatment of plastic waste. Compared with conventional methods, microwave has displayed unique advantages in the rapid production of hydrogen from plastic wastes. Understanding the interaction between microwave radiation and materials would promote the optimization of several parameters for the microwave reaction system. In this work, various carbon materials have been investigated to reveal microwave heating performance and the ensuing catalytic activity. Results showed that the diversity in the heating characteristic was mainly due to the dielectric properties and the individual microstructures. Furthermore, the gaps and steps among the surface of carbon materials would lead to the distortion of the electromagnetic field, which correspondingly induced plasma discharging. The intensity and location of local plasma were also studied. For high-yield H₂ production, iron nanoparticles were selected as the active sites, and a series of iron/carbon bifunctional catalysts were synthesized. Apart from the high catalytic activity, the iron particles in nano-size close to the microwave skin depth would transfer microwave irradiation to the heat, intensifying the decomposition of plastics. Under microwave radiation, iron is supported on activated carbon material with 10wt.% loading exhibited the best catalytic activity for H₂ production. Specifically, the plastics were rapidly heated up and subsequently converted into H₂ with a hydrogen efficiency of 85%. This work demonstrated a deep understanding of microwave reaction systems and provided the optimization for plastic treatment.

Keywords: plastic waste, recycling, hydrogen, microwave

Procedia PDF Downloads 71

Authors: Veronique Jubera, Ka-Young Kim, U-Chan Chung, Amelie Veillere, Jean-Marc Heintz

Abstract:

Emitting materials and all solid state lasers are widely used in the field of optical applications and materials science as a source of excitement, instrumental measurements, medical applications, metal shaping etc. Recently promising optical efficiencies were recorded on ceramics which result from a cheaper and faster ways to obtain crystallized materials. The choice and optimization of the sintering process is the key point to fabricate transparent ceramics. It includes a high control on the preparation of the powder with the choice of an adequate synthesis, a pre-heat-treatment, the reproducibility of the sintering cycle, the polishing and post-annealing of the ceramic. The densification is the main factor needed to reach a satisfying transparency, and many technologies are now available. The symmetry of the unit cell plays a crucial role in the diffusion rate of the material. Therefore, the cubic symmetry compounds having an isotropic refractive index is preferred. The cubic Y3NbO7 matrix is an interesting host which can accept a high concentration of rare earth doping element and it has been demonstrated that SPS is an efficient way to sinter this material. The optimization of diffusion losses requires a microstructure of fine ceramics, generally less than one hundred nanometers. In this case, grain growth is not an obstacle to transparency. The ceramics properties are then isotropic thereby to free-shaping step by orienting the ceramics as this is the case for the compounds of lower symmetry. After optimization of the synthesis route, several SPS parameters as heating rate, holding, dwell time and pressure were adjusted in order to increase the densification of the Eu3+ doped Y3NbO7 pellets. The luminescence data coupled with X-Ray diffraction analysis and electronic diffraction microscopy highlight the existence of several distorted environments of the doping element in the studied defective fluorite-type host lattice. Indeed, the fast and high crystallization rate obtained to put in evidence a lack of miscibility in the phase diagram, being the final composition of the pellet driven by the ratio between niobium and yttrium elements. By following the luminescence properties, we demonstrate a direct impact on the SPS process on this material.

Keywords: emission, niobate of rare earth, Spark plasma sintering, lack of miscibility

Procedia PDF Downloads 268