Search results for: algorithmic sonic computation

Authors: Priya Kedia, Kiranmoy Das

Abstract:

There is a rich literature on variable selection in regression setting. However, most of these methods assume normality for the response variable under consideration for implementing the methodology and establishing the statistical properties of the estimates. In many real applications, the distribution for the response variable may be non-Gaussian, and one might be interested in finding the best subset of covariates at some predetermined quantile level. We develop dynamic Bayesian approach for variable selection in quantile regression framework. We use a zero-inflated mixture prior for the regression coefficients, and consider the asymmetric Laplace distribution for the response variable for modeling different quantiles of its distribution. An efficient Gibbs sampler is developed for our computation. Our proposed approach is assessed through extensive simulation studies, and real application of the proposed approach is also illustrated. We consider the data from health and retirement study conducted by the University of Michigan, and select the important predictors when the outcome of interest is out-of-pocket medical cost, which is considered as an important measure for financial risk. Our analysis finds important predictors at different quantiles of the outcome, and thus enhance our understanding on the effects of different predictors on the out-of-pocket medical cost.

Keywords: variable selection, quantile regression, Gibbs sampler, asymmetric Laplace distribution

Procedia PDF Downloads 128

Authors: Sudip Kumar Kundu, Charu Singh

Abstract:

The climatic condition over Indian region is highly dependent on monsoon. India receives maximum amount of rainfall during southwest monsoon. Indian economy is highly dependent on agriculture. The presence of flood and drought years influenced the total cultivation system as well as the economy of the country as Indian agricultural systems is still highly dependent on the monsoon rainfall. The present study has been planned to investigate the flood and drought years for the north-west Himalayan region from 1951 to 2014 by using area average Indian Meteorological Department (IMD) rainfall data. For this investigation the Normalized index (NI) has been utilized to find out whether the particular year is drought or flood. The data have been extracted for the north-west Himalayan (NWH) region states namely Uttarakhand (UK), Himachal Pradesh (HP) and Jammu and Kashmir (J&K) to find out the rainy season average rainfall for each year, climatological mean and the standard deviation. After calculation it has been plotted by the diagrams (or graphs) to show the results- some of the years associated with drought years, some are flood years and rest are neutral. The flood and drought years can also relate with the large-scale phenomena El-Nino and La-Lina.

Keywords: IMD, rainfall, normalized index, flood, drought, NWH

Procedia PDF Downloads 268

Authors: Aisha Adam

Abstract:

This study critically examines the transformative impact of automated decision-making (ADM) and artificial intelligence (AI) systems on South African labour law. As AI technologies increasingly infiltrate workplaces, existing liability frameworks face challenges in addressing the unique complexities presented by these innovations. This article explores the necessity of redefining liability to accommodate the nuanced landscape of ADM and AI within South African labour law. It emphasises the importance of ensuring responsible deployment and safeguarding the rights of workers amid evolving technological dynamics. This research investigates the central concern of fairness, bias, and discrimination in ADM and AI decision-making. Focusing on algorithmic bias and discriminatory outcomes, the paper advocates for the integration of mechanisms within the South African legal framework, particularly under the Promotion of Equality and Prevention of Unfair Discrimination Act (PEPUDA) and the Employment Equity Act (EEA). The study scrutinises the shifting dynamics of the employment relationship, calling for clear guidelines on the responsibilities and liabilities of employers, employees, and technology providers. Furthermore, the article analyses legal and policy responses to ADM and AI within South African labour law, exploring potential amendments to legislation, guidelines, and codes of practice. It assesses the role of regulatory bodies, specifically the Commission for Conciliation, Mediation, and Arbitration (CCMA), in overseeing and enforcing responsible practices in the workplace. Lastly, the research evaluates the impact of ADM and AI on human and social rights in the South African context. Emphasising the protection of constitutional rights, including fair labour practices, privacy, and equality, the study proposes remedies and safeguards. It advocates for a multidisciplinary approach involving legal, technological, and ethical considerations to redefine liability in South African labour law effectively. The article contends that a shift from accountability to responsibility is crucial for promoting fairness, antidiscrimination, and the protection of human and social rights in the age of automated decision-making. It calls for collaborative efforts among stakeholders to shape responsible practices and redefine liability in this evolving technological landscape.

Keywords: automated decision-making, artificial intelligence, labour law, vicarious liability

Procedia PDF Downloads 47

Authors: Belkacem Soundes, Guezouli Larbi

Abstract:

This paper presents a robust and accurate method for text detection and localization over lecture videos. Frame regions are classified into text or background based on visual feature analysis. However, lecture video shows significant degradation mainly related to acquisition conditions, camera motion and environmental changes resulting in low quality videos. Hence, affecting feature extraction and description efficiency. Moreover, traditional text detection methods cannot be directly applied to lecture videos. Therefore, robust feature extraction methods dedicated to this specific video genre are required for robust and accurate text detection and extraction. Method consists of a three-step process: Slide region detection and segmentation; Feature extraction and non-text filtering. For robust and effective features extraction moment functions are used. Two distinct types of moments are used: orthogonal and non-orthogonal. For orthogonal Zernike Moments, both Pseudo Zernike moments are used, whereas for non-orthogonal ones Hu moments are used. Expressivity and description efficiency are given and discussed. Proposed approach shows that in general, orthogonal moments show high accuracy in comparison to the non-orthogonal one. Pseudo Zernike moments are more effective than Zernike with better computation time.

Keywords: text detection, text localization, lecture videos, pseudo zernike moments

Procedia PDF Downloads 125

Authors: Dhawal Ladani

Abstract:

Shell and tube type heat exchangers are predominantly used in heat exchange between two fluids and other applications. This paper projects the optimal design of the pipe used in the heat exchanger in such a way to minimize the vibration occurring in the pipe. Paper also consists of the comparison of the different design of the pipe to get the maximize the heat transfer rate by converting laminar flow into the turbulent flow. By the updated design the vibration in the pipe due to the flow is also decreased. Computational Fluid Dynamics and Thermal Heat Transfer analysis are done to justifying the result. Currently, the straight pipe is used in the shell and tube type of heat exchanger where as per the paper the pipe consists of the curvature along with the pipe. Hence, the heat transfer area is also increased and result in the increasing in heat transfer rate. Curvature type design is useful to create turbulence and minimizing the vibration, also. The result will give the output comparison of the effect of laminar flow and the turbulent flow in the heat exchange mechanism, as well as, inverse effect of the boundary layer in heat exchanger is also justified.

Keywords: heat exchanger, heat transfer rate, laminar and turbulent effect, shell and tube

Procedia PDF Downloads 284

Authors: Taylor Kolody, Farkhund Iqbal, Rabia Batool, Benjamin Fung, Mohammed Hussaeni, Saiqa Aleem

Abstract:

The rising threat of climate change has led to an increase in public awareness and care about our collective and individual environmental impact. A key component of this impact is our use of cars and other polluting forms of transportation, but it is often difficult for an individual to know how severe this impact is. While there are applications that offer this feedback, they require manual entry of what transportation mode was used for a given trip, which can be burdensome. In order to alleviate this shortcoming, a data from the 2016 TRIPlab datasets has been used to train a variety of machine learning models to automatically recognize the mode of transportation. The accuracy of 89.6% is achieved using single deep neural network model with Gated Recurrent Unit (GRU) architecture applied directly to trip data points over 4 primary classes, namely walking, public transit, car, and bike. These results are comparable in accuracy to results achieved by others using ensemble methods and require far less computation when classifying new trips. The lack of trip context data, e.g., bus routes, bike paths, etc., and the need for only a single set of weights make this an appropriate methodology for applications hoping to reach a broad demographic and have responsive feedback.

Keywords: classification, gated recurrent unit, recurrent neural network, transportation

Procedia PDF Downloads 105

Authors: Rita Magdalena, N. K. Caecar Pratiwi, Yunendah Nur Fuadah, Sofia Saidah, Bima Sakti

Abstract:

Vessel segmentation of retinal fundus is important for biomedical sciences in diagnosing ailments related to the eye. Segmentation can simplify medical experts in diagnosing retinal fundus image state. Therefore, in this study, we designed a software using MATLAB which enables the segmentation of the retinal blood vessels on retinal fundus images. There are two main steps in the process of segmentation. The first step is image preprocessing that aims to improve the quality of the image to be optimum segmented. The second step is the image segmentation in order to perform the extraction process to retrieve the retina’s blood vessel from the eye fundus image. The image segmentation methods that will be analyzed in this study are Morphology Operation, Discrete Wavelet Transform and combination of both. The amount of data that used in this project is 40 for the retinal image and 40 for manually segmentation image. After doing some testing scenarios, the average accuracy for Morphology Operation method is 88.46 % while for Discrete Wavelet Transform is 89.28 %. By combining the two methods mentioned in later, the average accuracy was increased to 89.53 %. The result of this study is an image processing system that can segment the blood vessels in retinal fundus with high accuracy and low computation time.

Keywords: discrete wavelet transform, fundus retina, morphology operation, segmentation, vessel

Procedia PDF Downloads 169

Authors: M. Moslehpour, S. Khorsandi

Abstract:

Due to shortage in IPv4 addresses, transition to IPv6 has gained significant momentum in recent years. Like Address Resolution Protocol (ARP) in IPv4, Neighbor Discovery Protocol (NDP) provides some functions like address resolution in IPv6. Besides functionality of NDP, it is vulnerable to some attacks. To mitigate these attacks, Internet Protocol Security (IPsec) was introduced, but it was not efficient due to its limitation. Therefore, SEND protocol is proposed to automatic protection of auto-configuration process. It is secure neighbor discovery and address resolution process. To defend against threats on NDP’s integrity and identity, Cryptographically Generated Address (CGA) and asymmetric cryptography are used by SEND. Besides advantages of SEND, its disadvantages like the computation process of CGA algorithm and sequentially of CGA generation algorithm are considerable. In this paper, we parallel this process between network resources in order to improve it. In addition, we compare the CGA generation time in self-computing and distributed-computing process. We focus on the impact of the malicious nodes on the CGA generation time in the network. According to the result, although malicious nodes participate in the generation process, CGA generation time is less than when it is computed in a one-way. By Trust Management System, detecting and insulating malicious nodes is easier.

Keywords: NDP, IPsec, SEND, CGA, modifier, malicious node, self-computing, distributed-computing

Procedia PDF Downloads 259

Authors: Assem M. F. Sallam, Ah. El-S. Makled

Abstract:

This paper presents a study on the trajectory of a two stage launch vehicle. The study includes dynamic responses of motion parameters as well as the variation of angles affecting the orientation of the launch vehicle (LV). LV dynamic characteristics including state vector variation with corresponding altitude and velocity for the different LV stages separation, as well as the angle of attack and flight path angles are also discussed. A flight trajectory study for the drop zone of first stage and the jettisoning of fairing are introduced in the mathematical modeling to study their effect. To increase the accuracy of the LV model, atmospheric model is used taking into consideration geographical location and the values of solar flux related to the date and time of launch, accurate atmospheric model leads to enhancement of the calculation of Mach number, which affects the drag force over the LV. The mathematical model is implemented on MATLAB based software (Simulink). The real available experimental data are compared with results obtained from the theoretical computation model. The comparison shows good agreement, which proves the validity of the developed simulation model; the maximum error noticed was generally less than 10%, which is a result that can lead to future works and enhancement to decrease this level of error.

Keywords: launch vehicle modeling, launch vehicle trajectory, mathematical modeling, Matlab- Simulink

Procedia PDF Downloads 256

Authors: Nihat Dipova, Bulent Cangir

Abstract:

This article presents the results of probabilistic seismic hazard analysis (PSHA) for Antalya (SW Turkey). South west of Turkey is characterized by large earthquakes resulting from the continental collision between the African, Arabian and Eurasian plates and crustal faults. Earthquakes around the study area are grouped into two; crustal earthquakes (D=0-50 km) and subduction zone earthquakes (50-140 km). Maximum observed magnitude of subduction earthquakes is Mw=6.0. Maximum magnitude of crustal earthquakes is Mw=6.6. Sources for crustal earthquakes are faults which are related with Isparta Angle and Cyprus Arc tectonic structures. A new earthquake catalogue for Antalya, with unified moment magnitude scale has been prepared and seismicity of the area around Antalya city has been evaluated by defining ‘a’ and ‘b’ parameters of the Gutenberg-Richter recurrence relationship. The Standard Cornell-McGuire method has been used for hazard computation utilizing CRISIS2007 software. Attenuation relationships proposed by Chiou and Youngs (2008) has been used for 0-50 km earthquakes and Youngs et. al (1997) for deep subduction earthquakes. Finally, Seismic hazard map for peak horizontal acceleration on a uniform site condition of firm rock (average shear wave velocity of about 1130 m/s) at a hazard level of 10% probability of exceedance in 50 years has been prepared.

Keywords: Antalya, peak ground acceleration, seismic hazard assessment, subduction

Procedia PDF Downloads 348

Authors: Muhammad Waqar Aziz, Syed Abdul Baqi Shah

Abstract:

The execution time analysis is fundamental to the successful design and execution of real-time embedded software. In such analysis, the Worst-Case Execution Time (WCET) of a program is a key measure, on the basis of which system tasks are scheduled. The WCET analysis of embedded software is also needed for system understanding and to guarantee its behavior. WCET analysis can be performed statically (without executing the program) or dynamically (through measurement). Traditionally, research on the WCET analysis assumes sequential code running on single-core platforms. However, as computation is steadily moving towards using a combination of parallel programs and multi-core hardware, new challenges in WCET analysis need to be addressed. In this article, we report our experiences of performing the WCET analysis of Parallel Embedded Software (PES) running on multi-core platform. The primary purpose was to investigate how WCET estimates of PES can be computed statically, and how they can be derived dynamically. Our experiences, as reported in this article, include the challenges we faced, possible suggestions to these challenges and the workarounds that were developed. This article also provides observations on the benefits and drawbacks of deriving the WCET estimates using the said methods and provides useful recommendations for further research in this area.

Keywords: embedded software, worst-case execution-time analysis, static flow analysis, measurement-based analysis, parallel computing

Procedia PDF Downloads 299

Authors: Yavuz Unal, Kemal Polat, H. Erdinc Kocer

Abstract:

In this study, a decision support system comprising three stages has been proposed to detect the disc abnormalities of the lumbar region. In the first stage named the feature extraction, T2-weighted sagittal and axial Magnetic Resonance Images (MRI) were taken from 55 people and then 27 appearance and shape features were acquired from both sagittal and transverse images. In the second stage named the feature weighting process, k-means clustering based feature weighting (KMCBFW) proposed by Gunes et al. Finally, in the third stage named the classification process, the classifier algorithms including multi-layer perceptron (MLP- neural network), support vector machine (SVM), Naïve Bayes, and decision tree have been used to classify whether the subject has lumbar disc or not. In order to test the performance of the proposed method, the classification accuracy (%), sensitivity, specificity, precision, recall, f-measure, kappa value, and computation times have been used. The best hybrid model is the combination of k-means clustering based feature weighting and decision tree in the detecting of lumbar disc disease based on both sagittal and axial MR images.

Keywords: lumbar disc abnormality, lumbar MRI, lumbar spine, hybrid models, hybrid features, k-means clustering based feature weighting

Procedia PDF Downloads 499

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

Abstract:

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

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

Procedia PDF Downloads 271

Authors: Yeonjune Kang

Abstract:

A triangulation of a planar region is a partition of that region into triangles. In the finite element method, triangulations are often used as the grid underlying a computation. In order to be suitable as a finite element mesh, a triangulation must have well-shaped triangles, according to criteria that depend on the details of the particular problem. For instance, most methods require that all triangles be small and as close to the equilateral shape as possible. Stated differently, one wants to avoid having either thin or flat triangles in the triangulation. There are many triangulation procedures, a particular one being the one known as the longest edge bisection algorithm described below. Starting with a given triangle, locate the midpoint of the longest edge and join it to the opposite vertex of the triangle. Two smaller triangles are formed; apply the same bisection procedure to each of these triangles. Continuing in this manner after n steps one obtains a triangulation of the initial triangle into 2n smaller triangles. The longest edge algorithm was first considered in the late 70’s. It was shown by various authors that this triangulation has the desirable properties for the finite element method: independently of the number of iterations the angles of these triangles cannot get too small; moreover, the size of the triangles decays exponentially. In the present paper we consider a related triangulation algorithm we refer to as the largest angle bisection procedure. As the name suggests, rather than bisecting the longest edge, at each step we bisect the largest angle. We study the properties of the resulting triangulation and prove that, while the general behavior resembles the one in the longest edge bisection algorithm, there are several notable differences as well.

Keywords: angle bisectors, geometry, triangulation, applied mathematics

Procedia PDF Downloads 368

Authors: Chen Guo, Heng Tang, Ben Niu

Abstract:

Clustering splits objects into different groups based on similarity, making the objects have higher similarity in the same group and lower similarity in different groups. Thus, clustering can be treated as an optimization problem to maximize the intra-cluster similarity or inter-cluster dissimilarity. In real-world applications, the datasets often have some complex characteristics: sparse, overlap, high dimensionality, etc. When facing these datasets, simultaneously optimizing two or more objectives can obtain better clustering results than optimizing one objective. However, except for the objectives weighting methods, traditional clustering approaches have difficulty in solving multi-objective data clustering problems. Due to this, evolutionary multi-objective optimization algorithms are investigated by researchers to optimize multiple clustering objectives. In this paper, the Data Clustering algorithm based on Multi-objective Periodic Bacterial Foraging Optimization with two Learning Archives (DC-MPBFOLA) is proposed. Specifically, first, to reduce the high computing complexity of the original BFO, periodic BFO is employed as the basic algorithmic framework. Then transfer the periodic BFO into a multi-objective type. Second, two learning strategies are proposed based on the two learning archives to guide the bacterial swarm to move in a better direction. On the one hand, the global best is selected from the global learning archive according to the convergence index and diversity index. On the other hand, the personal best is selected from the personal learning archive according to the sum of weighted objectives. According to the aforementioned learning strategies, a chemotaxis operation is designed. Third, an elite learning strategy is designed to provide fresh power to the objects in two learning archives. When the objects in these two archives do not change for two consecutive times, randomly initializing one dimension of objects can prevent the proposed algorithm from falling into local optima. Fourth, to validate the performance of the proposed algorithm, DC-MPBFOLA is compared with four state-of-art evolutionary multi-objective optimization algorithms and one classical clustering algorithm on evaluation indexes of datasets. To further verify the effectiveness and feasibility of designed strategies in DC-MPBFOLA, variants of DC-MPBFOLA are also proposed. Experimental results demonstrate that DC-MPBFOLA outperforms its competitors regarding all evaluation indexes and clustering partitions. These results also indicate that the designed strategies positively influence the performance improvement of the original BFO.

Keywords: data clustering, multi-objective optimization, bacterial foraging optimization, learning archives

Procedia PDF Downloads 113

Authors: Roudane Mohamed

Abstract:

In this study we are interested in improving the efficiency of a steam boiler to 4.5T/h and minimize fume discharge temperature by the addition of a heat exchanger against the current in the energy system, the output of the boiler. The mathematical approach to the problem is based on the use of heat transfer by convection and conduction equations. These equations have been chosen because of their extensive use in a wide range of application. A software and developed for solving the equations governing these phenomena and the estimation of the thermal characteristics of boiler through the study of the thermal characteristics of the heat exchanger by both LMTD and NUT methods. Subsequently, an analysis of the thermal performance of the steam boiler by studying the influence of different operating parameters on heat flux densities, temperatures, exchanged power and performance was carried out. The study showed that the behavior of the boiler is largely influenced. In the first regime (P = 3.5 bar), the boiler efficiency has improved significantly from 93.03 to 99.43 at the rate of 6.47% and 4.5%. For maximum speed, the change is less important, it is of the order of 1.06%. The results obtained in this study of great interest to industrial utilities equipped with smoke tube boilers for the preheating air temperature intervene to calculate the actual temperature of the gas so the heat exchanged will be increased and minimize temperature smoke discharge. On the other hand, this work could be used as a model of computation in the design process.

Keywords: numerical simulation, efficiency, fire tube, heat exchanger, convection and conduction

Procedia PDF Downloads 196

Authors: Barenten Suciu

Abstract:

An electrical generator able to harness energy from the water waves and designed as a double-cone geared motor-generator (DCGMG), is proposed and theoretically investigated. Similar to a differential gear mechanism, used in the transmission system of the auto vehicle wheels, an angular speed differential is created between the cones rolling on two concentric circular rails. Water wave acting on the floating DCGMG produces and a gear-box amplifies the speed differential to gain sufficient torque for power generation. A model that allows computation of the speed differential, torque, and power of the DCGMG is suggested. Influence of various parameters, regarding the construction of the DCGMG, as well as the contact between the double-cone and rails, on the electro-mechanical output, is emphasized. Results obtained indicate that the generated electrical power can be increased by augmenting the mass of the double-cone, the span of the rails, the apex angle of the cones, the friction between cones and rails, the amplification factor of the gear-box, and the efficiency of the motor-generator. Such findings are useful to formulate a design methodology for the proposed wave-powered generator.

Keywords: amplification of angular speed differential, circular concentric rails, double-cone, wave-powered electrical generator

Procedia PDF Downloads 128

Authors: Justyna Rybicka, Ashutosh Tiwari, Shane Enticott

Abstract:

In the age of automation and computation aiding manufacturing, it is clear that manufacturing systems have become more complex than ever before. Although technological advances provide the capability to gain more value with fewer resources, sometimes utilisation of the manufacturing capabilities available to organisations is difficult to achieve. Flexible manufacturing systems (FMS) provide a unique capability to manufacturing organisations where there is a need for product range diversification by providing line efficiency through production flexibility. This is very valuable in trend driven production set-ups or niche volume production requirements. Although FMS provides flexible and efficient facilities, its optimal set-up is key in achieving production performance. As many variables are interlinked due to the flexibility provided by the FMS, analytical calculations are not always sufficient to predict the FMS’ performance. Simulation modelling is capable of capturing the complexity and constraints associated with FMS. This paper demonstrates how discrete event simulation (DES) can address complexity in an FMS to optimise the production line performance. A case study of an automotive FMS is presented. The DES model demonstrates different configuration options depending on prioritising objectives: utilisation and throughput. Additionally, this paper provides insight into understanding the impact of system set-up constraints on the FMS performance and demonstrates the exploration into the optimal production set-up.

Keywords: discrete event simulation, flexible manufacturing system, capacity performance, automotive

Procedia PDF Downloads 308

Authors: Arian Hosseini, Mahmudul Hasan

Abstract:

To address the increasing need for efficient and accurate content moderation, we propose an efficient and lightweight deep classification ensemble structure. Our approach is based on a combination of simple visual features, designed for high-accuracy classification of violent content with low false positives. Our ensemble architecture utilizes a set of lightweight models with narrowed-down color features, and we apply it to both images and videos. We evaluated our approach using a large dataset of explosion and blast contents and compared its performance to popular deep learning models such as ResNet-50. Our evaluation results demonstrate significant improvements in prediction accuracy, while benefiting from 7.64x faster inference and lower computation cost. While our approach is tailored to explosion detection, it can be applied to other similar content moderation and violence detection use cases as well. Based on our experiments, we propose a "think small, think many" philosophy in classification scenarios. We argue that transforming a single, large, monolithic deep model into a verification-based step model ensemble of multiple small, simple, and lightweight models with narrowed-down visual features can possibly lead to predictions with higher accuracy.

Keywords: deep classification, content moderation, ensemble learning, explosion detection, video processing

Procedia PDF Downloads 23

Authors: Farshid Fathinia

Abstract:

Numerical computation of unsteady laminar three-dimensional natural convection and entropy generation in an inclined cubical trapezoidal air-filled cavity is performed for the first time in this work. The vertical right and left sidewalls of the cavity are maintained at constant cold temperatures. The lower wall is subjected to a constant hot temperature, while the upper one is considered insulated. Computations are performed for Rayleigh numbers varied as 103 ≤ Ra ≤ 105, while the trapezoidal cavity inclination angle is varied as 0° ≤ ϕ ≤ 180°. Prandtl number is considered constant at Pr = 0.71. The second law of thermodynamics is applied to obtain thermodynamic losses inside the cavity due to both heat transfer and fluid friction irreversibilities. The variation of local and average Nusselt numbers are presented and discussed.While, streamlines, isotherms and entropy contours are presented in both two and three-dimensional pattern. The results show that when the Rayleigh number increases, the flow patterns are changed especially in three-dimensional results and the flow circulation increases. Also, the inclination angle effect on the total entropy generation becomes insignificant when the Rayleigh number is low.Moreover, when the Rayleigh number increases the average Nusselt number increases.

Keywords: transient natural convection, trapezoidal cavity, three-dimensional flow, entropy generation, second law

Procedia PDF Downloads 326

Authors: Federico Garrido, Mostafa El Hayani, Ahmed Shams

Abstract:

The exploration of the potential of artificial intelligence (AI) in architecture is still embryonic, however, its latent capacity to change design disciplines is significant. 'Synthetic Classism' is a research project that questions the underlying aspects of classically organized architecture not just in aesthetic terms but also from a geometrical and morphological point of view, intending to generate new architectural information using historical examples as source material. The main aim of this paper is to explore the uses of artificial intelligence and machine learning algorithms in architectural design while creating a coherent narrative to be contained within a design process. The purpose is twofold: on one hand, to develop and train machine learning algorithms to produce architectural information of small pavilions and on the other, to synthesize new information from previous architectural drawings. These algorithms intend to 'interpret' graphical information from each pavilion and then generate new information from it. The procedure, once these algorithms are trained, is the following: parting from a line profile, a synthetic 'front view' of a pavilion is generated, then using it as a source material, an isometric view is created from it, and finally, a top view is produced. Thanks to GAN algorithms, it is also possible to generate Front and Isometric views without any graphical input as well. The final intention of the research is to produce isometric views out of historical information, such as the pavilions from Sebastiano Serlio, James Gibbs, or John Soane. The idea is to create and interpret new information not just in terms of historical reconstruction but also to explore AI as a novel tool in the narrative of a creative design process. This research also challenges the idea of the role of algorithmic design associated with efficiency or fitness while embracing the possibility of a creative collaboration between artificial intelligence and a human designer. Hence the double feature of this research, both analytical and creative, first by synthesizing images based on a given dataset and then by generating new architectural information from historical references. We find that the possibility of creatively understand and manipulate historic (and synthetic) information will be a key feature in future innovative design processes. Finally, the main question that we propose is whether an AI could be used not just to create an original and innovative group of simple buildings but also to explore the possibility of fostering a novel architectural sensibility grounded on the specificities on the architectural dataset, either historic, human-made or synthetic.

Keywords: architecture, central pavilions, classicism, machine learning

Procedia PDF Downloads 115

Authors: Benson Ade Eniola Afere

Abstract:

This paper introduces a family of fourth-order hybrid beta polynomial kernels developed for statistical analysis. The assessment of these kernels' performance centers on two critical metrics: asymptotic mean integrated squared error (AMISE) and kernel efficiency. Through the utilization of both simulated and real-world datasets, a comprehensive evaluation was conducted, facilitating a thorough comparison with conventional fourth-order polynomial kernels. The evaluation procedure encompassed the computation of AMISE and efficiency values for both the proposed hybrid kernels and the established classical kernels. The consistently observed trend was the superior performance of the hybrid kernels when compared to their classical counterparts. This trend persisted across diverse datasets, underscoring the resilience and efficacy of the hybrid approach. By leveraging these performance metrics and conducting evaluations on both simulated and real-world data, this study furnishes compelling evidence in favour of the superiority of the proposed hybrid beta polynomial kernels. The discernible enhancement in performance, as indicated by lower AMISE values and higher efficiency scores, strongly suggests that the proposed kernels offer heightened suitability for statistical analysis tasks when compared to traditional kernels.

Keywords: AMISE, efficiency, fourth-order Kernels, hybrid Kernels, Kernel density estimation

Procedia PDF Downloads 47

Authors: P. S. Jagadeesh Kumar, Yang Yung, Wenli Hu

Abstract:

Speech emotion classification is a dominant research field in finding a sturdy and profligate classifier appropriate for different real-life applications. This effort accentuates on classifying different emotions from speech signal quarried from the features related to pitch, formants, energy contours, jitter, shimmer, spectral, perceptual and temporal features. Tensor deep stacking neural networks were supported to examine the factors that influence the classification success rate. Facial electromyography signals were composed of several forms of focuses in a controlled atmosphere by means of audio-visual stimuli. Proficient facial electromyography signals were pre-processed using moving average filter, and a set of arithmetical features were excavated. Extracted features were mapped into consistent emotions using bilinear mapping. With facial electromyography signals, a database comprising diverse emotions will be exposed with a suitable fine-tuning of features and training data. A success rate of 92% can be attained deprived of increasing the system connivance and the computation time for sorting diverse emotional states.

Keywords: speech emotion classification, tensor deep stacking neural networks, facial electromyography, bilinear mapping, audio-visual stimuli

Procedia PDF Downloads 223

Authors: Mohammad Hossein Modarressi, Mozhgan Mondeali, Khabat Barkhordari, Ali Etemadi

Abstract:

The COVID-19 pandemic, caused by SARS-CoV-2, has led to significant concerns worldwide due to its catastrophic effects on public health. The SARS-CoV-2 infection is initiated with the binding of the receptor-binding domain (RBD) in its spike protein to the ACE2 receptor in the host cell membrane. Due to the error-prone entity of the viral RNA-dependent polymerase complex, the virus genome, including the coding region for the RBD, acquires new mutations, leading to the appearance of multiple variants. These variants can potentially impact transmission, virulence, antigenicity and evasive immune properties. S477N mutation located in the RBD has been observed in the SARS-CoV-2 omicron (B.1.1. 529) variant. In this study, we investigated the consequences of S477N mutation at the molecular level using computational approaches such as molecular dynamics simulation, protein-protein interaction analysis, immunoinformatics and free energy computation. We showed that displacement of Ser with Asn increases the stability of the spike protein and its affinity to ACE2 and thus increases the transmission potential of the virus. This mutation changes the folding and secondary structure of the spike protein. Also, it reduces antibody neutralization, raising concern about re-infection, vaccine breakthrough and therapeutic values.

Keywords: S477N, COVID-19, molecular dynamic, SARS-COV2 mutations

Procedia PDF Downloads 145

Authors: Ehsan Gholamalizadeh, Jae Dong Chung

Abstract:

Solar chimney power plant is a feasible solar thermal system which produces electricity from the Sun. The objective of this study is to investigate buoyancy-driven flow and heat transfer through a built pilot solar chimney system called 'Kerman Project'. The system has a chimney with the height and diameter of 60 m and 3 m, respectively, and the average radius of its solar collector is about 20 m, and also its average collector height is about 2 m. A three-dimensional simulation was conducted to analyze the system, using computational fluid dynamics (CFD). In this model, radiative transfer equation was solved using the discrete ordinates (DO) radiation model taking into account a non-gray radiation behavior. In order to modelling solar irradiation from the sun’s rays, the solar ray tracing algorithm was coupled to the computation via a source term in the energy equation. The model was validated with comparing to the experimental data of the Manzanares prototype and also the performance of the built pilot system. Then, based on the numerical simulations, velocity and temperature distributions through the system, the temperature profile of the ground surface and the system performance were presented. The analysis accurately shows the flow and heat transfer characteristics through the pilot system and predicts its performance.

Keywords: buoyancy-driven flow, computational fluid dynamics, heat transfer, renewable energy, solar chimney power plant

Procedia PDF Downloads 228

Authors: Arnab Adhikari, Adrija Majumdar, Gaurav Gupta, Arnab Bisi

Abstract:

Team formation plays an instrumental role in the sports like cricket. Existing literature reveals that most of the works on player selection focus only on the players’ efficiency and ignore the consistency. It motivates us to design an improved player selection method based on both player’s efficiency and consistency. To measure the players’ efficiency measurement, we employ a modified data envelopment analysis (DEA) technique namely ‘super-efficient DEA model’. We design a modified consistency index based on semi variance approach. Here, we introduce a new parameter called ‘fitness index’ for consistency computation to assess a player’s fitness level. Finally, we devise a single performance score using both efficiency score and consistency score with the help of a linear programming model. To test the robustness of our method, we perform a rigorous numerical analysis to determine the all-time best One Day International (ODI) Cricket XI. Next, we conduct extensive comparative studies regarding efficiency scores, consistency scores, selected team between the existing methods and the proposed method and explain the rationale behind the improvement.

Keywords: decision support systems, sports, super-efficient data envelopment analysis, semi variance approach

Procedia PDF Downloads 375

Authors: Jenifer Alam, Rima Chatterjee

Abstract:

Upper Assam sedimentary basin is one of the oldest commercially producing basins of India. Being in a tectonically active zone, estimation of tectonic strain and stress magnitudes has vast application in hydrocarbon exploration and exploitation. This East North East –West South West trending shelf-slope basin encompasses the Bramhaputra valley extending from Mikir Hills in the southwest to the Naga foothills in the northeast. Assam Shelf lying between the Main Boundary Thrust (MBT) and Naga Thrust area is comparatively free from thrust tectonics and depicts normal faulting mechanism. The study area is bounded by the MBT and Main Central Thrust in the northwest. The Belt of Schuppen in the southeast, is bordered by Naga and Disang thrust marking the lower limit of the study area. The entire Assam basin shows low-level seismicity compared to other regions of northeast India. Pore pressure (PP), vertical stress magnitude (SV) and horizontal stress magnitudes have been estimated from two wells - N1 and T1 located in Upper Assam. N1 is located in the Assam gap below the Bramhaputra river while T1, lies in the Belt of Schuppen. N1 penetrates geological formations from top Alluvial through Dhekiajuli, Girujan, Tipam, Barail, Kopili, Sylhet and Langpur to the granitic basement while T1 in trusted zone crosses through Girujan Suprathrust, Tipam Suprathrust, Barail Suprathrust to reach Naga Thrust. Normal compaction trend is drawn through shale points through both wells for estimation of PP using the conventional Eaton sonic equation with an exponent of 1.0 which is validated with Modular Dynamic Tester and mud weight. Observed pore pressure gradient ranges from 10.3 MPa/km to 11.1 MPa/km. The SV has a gradient from 22.20 to 23.80 MPa/km. Minimum and maximum horizontal principal stress (Sh and SH) magnitudes under isotropic conditions are determined using poroelastic model. This approach determines biaxial tectonic strain utilizing static Young’s Modulus, Poisson’s Ratio, SV, PP, leak off test (LOT) and SH derived from breakouts using prior information on unconfined compressive strength. Breakout derived SH information is used for obtaining tectonic strain due to lack of measured SH data from minifrac or hydrofracturing. Tectonic strain varies from 0.00055 to 0.00096 along x direction and from -0.0010 to 0.00042 along y direction. After obtaining tectonic strains at each well, the principal horizontal stress magnitudes are calculated from linear poroelastic model. The magnitude of Sh and SH gradient in normal faulting region are 12.5 and 16.0 MPa/km while in thrust faulted region the gradients are 17.4 and 20.2 MPa/km respectively. Model predicted Sh and SH matches well with the LOT data and breakout derived SH data in both wells. It is observed from this study that the stresses SV>SH>Sh prevailing in the shelf region while near the Naga foothills the regime changes to SH≈SV>Sh area corresponds to normal faulting regime. Hence this model is a reliable tool for predicting stress magnitudes from well logs under active tectonic regime in Upper Assam Basin.

Keywords: Eaton, strain, stress, poroelastic model

Procedia PDF Downloads 182

Authors: Nanjiang Chen

Abstract:

In the backdrop of expanding urban landscapes, accurately assessing spatial openness is critical. Traditional visibility analysis methods grapple with discretization errors and inefficiencies, creating a gap in truly capturing the human experi-ence of space. Addressing these gaps, this paper introduces a distinct continuous visibility algorithm, a leap in measuring urban spaces from a human-centric per-spective. This study presents a methodological breakthrough by applying this algorithm to urban visibility analysis. Unlike conventional approaches, this tech-nique allows for a continuous range of visibility assessment, closely mirroring hu-man visual perception. By eliminating the need for predefined subdivisions in ray casting, it offers a more accurate and efficient tool for urban planners and architects. The proposed algorithm not only reduces computational errors but also demonstrates faster processing capabilities, validated through a case study in Bei-jing's urban setting. Its key distinction lies in its potential to benefit a broad spec-trum of stakeholders, ranging from urban developers to public policymakers, aid-ing in the creation of urban spaces that prioritize visual openness and quality of life. This advancement in urban analysis methods could lead to more inclusive, comfortable, and well-integrated urban environments, enhancing the spatial experience for communities worldwide.

Keywords: visual openness, spatial continuity, ray-tracing algorithms, urban computation

Procedia PDF Downloads 7

Authors: Peter Egri, Tamas Kis

Abstract:

Scheduling problems have been studied by the algorithmic mechanism design research from the beginning. This paper is focusing on a practically important, but theoretically rather neglected field: the project scheduling problem where the jobs connected by precedence constraints compete for various nonrenewable resources, such as materials. Although the centralized problem can be solved in polynomial-time by applying the algorithm of Carlier and Rinnooy Kan from the Eighties, obtaining materials in a decentralized environment is usually far from optimal. It can be observed in practical production scheduling situations that project managers tend to cache the required materials as soon as possible in order to avoid later delays due to material shortages. This greedy practice usually leads both to excess stocks for some projects and materials, and simultaneously, to shortages for others. The aim of this study is to develop a model for the material allocation problem of a production plant, where a central decision maker—the inventory—should assign the resources arriving at different points in time to the jobs. Since the actual due dates are not known by the inventory, the mechanism design approach is applied with the projects as the self-interested agents. The goal of the mechanism is to elicit the required information and allocate the available materials such that it minimizes the maximal tardiness among the projects. It is assumed that except the due dates, the inventory is familiar with every other parameters of the problem. A further requirement is that due to practical considerations monetary transfer is not allowed. Therefore a mechanism without money is sought which excludes some widely applied solutions such as the Vickrey–Clarke–Groves scheme. In this work, a type of Serial Dictatorship Mechanism (SDM) is presented for the studied problem, including a polynomial-time algorithm for computing the material allocation. The resulted mechanism is both truthful and Pareto optimal. Thus the randomization over the possible priority orderings of the projects results in a universally truthful and Pareto optimal randomized mechanism. However, it is shown that in contrast to problems like the many-to-many matching market, not every Pareto optimal solution can be generated with an SDM. In addition, no performance guarantee can be given compared to the optimal solution, therefore this approximation characteristic is investigated with experimental study. All in all, the current work studies a practically relevant scheduling problem and presents a novel truthful material allocation mechanism which eliminates the potential benefit of the greedy behavior that negatively influences the outcome. The resulted allocation is also shown to be Pareto optimal, which is the most widely used criteria describing a necessary condition for a reasonable solution.

Keywords: material allocation, mechanism without money, polynomial-time mechanism, project scheduling

Procedia PDF Downloads 300

Authors: N. K. Caecar Pratiwi, Yunendah Nur Fuadah, Rita Magdalena, R. D. Atmaja, Sofia Saidah, Ocky Tiaramukti

Abstract:

Colon cancer or colorectal cancer is a type of cancer that attacks the last part of the human digestive system. Lymphoma and carcinoma are types of cancer that attack human’s colon. Colon cancer causes deaths about half a million people every year. In Indonesia, colon cancer is the third largest cancer case for women and second in men. Unhealthy lifestyles such as minimum consumption of fiber, rarely exercising and lack of awareness for early detection are factors that cause high cases of colon cancer. The aim of this project is to produce a system that can detect and classify images into type of colon cancer lymphoma, carcinoma, or normal. The designed system used 198 data colon cancer tissue pathology, consist of 66 images for Lymphoma cancer, 66 images for carcinoma cancer and 66 for normal / healthy colon condition. This system will classify colon cancer starting from image preprocessing, feature extraction using Principal Component Analysis (PCA) and classification using K-Nearest Neighbor (K-NN) method. Several stages in preprocessing are resize, convert RGB image to grayscale, edge detection and last, histogram equalization. Tests will be done by trying some K-NN input parameter setting. The result of this project is an image processing system that can detect and classify the type of colon cancer with high accuracy and low computation time.

Keywords: carcinoma, colorectal cancer, k-nearest neighbor, lymphoma, principle component analysis

Procedia PDF Downloads 181