Search results for: e-content producing algorithm

Authors: Amarachi Delight Onyemachi, Gregory Ikechukwu Onwuka

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The growing consumer trend for natural ingredients, functional foods with health benefits and the perceived risk of carcinogenesis associated with synthetic antioxidants have forced food manufacturers to look for alternatives for producing healthy and safe food. Herbs and spices are cheap, natural and harmless sources of antioxidants which can delay and prevent lipid oxidation of food products and also confer its unique organoleptic properties and health benefits to food products. The Nigerian climate has been proven to be conducive for the production of spices and herbs and is blessed bountifully with a wide range of them. Five selected Nigerian herbs and spices Piper guieense, Xylopia aethopica, Gongronema latifolium and Ocimum gratissimum were evaluated for their ability to act as radical scavengers. The spices were extracted with 80% ethanol and evaluated using total phenolic capacity (TPC), DPPH (1,1-diph diphenyl-2-picrylhydrazyl radical) ABTS (2,2’azinobis-(3-ethylbenzthiazoline-6-sulfonic acid)), total antioxidant capacity (TAC), reducing power (RP) assays. The TPC ranged from 5.33 µg GAE/mg (in Gongronema latifolium) to 15.55 µg GAE/mg (in Ocimum gratissimum). The DPPH and ABTS scavenging activity of the extracts ranged from 0.23-0.36 IC50 mg/ml and 2.32-7.25 Trolox equivalent % respectively. The TAC and RP of the extract ranged from 6.73-10.64 µg AAE/mg and 3.52-10.19 µg AAE/mg. The result of percentage yield of the extract ranged from as low as 9.94% in Gongronema latifolium and to as high as 23.85% in Xylopia aethopica. A very strong positive relationship existed between the total antioxidant capacity and total phenolic content of the tested herbs and spices (R2=0.96). All of the extracts exhibited different extent of strong antioxidant activity, high antioxidant activity was found in Ocimum gratissimum and Gongronema latifolium with the least. However, Gongronema latifolium possessed the highest total antioxidant capacity. These data confirm the appreciable antioxidant potentials and high phenolic content of Nigerian herbs and spices, thereby providing justification for their use in dishes and functional foods, prevention of cellular damage caused by free radicals and use as natural antioxidants in the food industry for prevention of lipid oxidation in food products. However, to utilize these natural antioxidants in food products, further analysis and studies of their behaviour in food systems at varying temperature, pH conditions and ionic concentrations should be carried out to displace the use of synthetic antioxidants like BHT and BHA.

Keywords: Antioxidant, free radicals, herbs, phenolic, spices

Procedia PDF Downloads 256

Authors: Şeyma Saliha Fidan, Ozgur Serin, Ata Mugan

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While performing verification analyses under static and dynamic loads that composite structures used in aviation are exposed to, it is necessary to obtain the bearing strength limit value for mechanically connected composite structures. For this purpose, various tests are carried out in accordance with aviation standards. There are many companies in the world that perform these tests in accordance with aviation standards, but the test costs are very high. In addition, due to the necessity of producing coupons, the high cost of coupon materials, and the long test times, it is necessary to simulate these tests on the computer. For this purpose, various test coupons were produced by using reinforcement and alignment angles of the composite radomes, which were integrated into the aircraft. Glass fiber reinforced and Quartz prepreg is used in the production of the coupons. The simulations of the tests performed according to the American Society for Testing and Materials (ASTM) D5961 Procedure C standard were performed on the computer. The analysis model was created in three dimensions for the purpose of modeling the bolt-hole contact surface realistically and obtaining the exact bearing strength value. The finite element model was carried out with the Analysis System (ANSYS). Since a physical break cannot be made in the analysis studies carried out in the virtual environment, a hypothetical break is realized by reducing the material properties. The material properties reduction coefficient was determined as 10%, which is stated to give the most realistic approach in the literature. There are various theories in this method, which is called progressive failure analysis. Because the hashin theory does not match our experimental results, the puck progressive damage method was used in all coupon analyses. When the experimental and numerical results are compared, the initial damage and the resulting force drop points, the maximum damage load values ​​, and the bearing strength value are very close. Furthermore, low error rates and similar damage patterns were obtained in both test and simulation models. In addition, the effects of various parameters such as pre-stress, use of bushing, the ratio of the distance between the bolt hole center and the plate edge to the hole diameter (E/D), the ratio of plate width to hole diameter (W/D), hot-wet environment conditions were investigated on the bearing strength of the composite structure.

Keywords: puck, finite element, bolted joint, composite

Procedia PDF Downloads 103

Authors: Xiao-Dong Wang, Wei-Mon Yan

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The flow field design in the bipolar plates affects the performance of the proton exchange membrane (PEM) fuel cell. This work adopted a combined optimization procedure, including a simplified conjugate-gradient method and a completely three-dimensional, two-phase, non-isothermal fuel cell model, to look for optimal flow field design for a single serpentine fuel cell of size 9×9 mm with five channels. For the direct solution, the two-fluid method was adopted to incorporate the heat effects using energy equations for entire cells. The model assumes that the system is steady; the inlet reactants are ideal gases; the flow is laminar; and the porous layers such as the diffusion layer, catalyst layer and PEM are isotropic. The model includes continuity, momentum and species equations for gaseous species, liquid water transport equations in the channels, gas diffusion layers, and catalyst layers, water transport equation in the membrane, electron and proton transport equations. The Bulter-Volumer equation was used to describe electrochemical reactions in the catalyst layers. The cell output power density Pcell is maximized subjected to an optimal set of channel heights, H1-H5, and channel widths, W2-W5. The basic case with all channel heights and widths set at 1 mm yields a Pcell=7260 Wm-2. The optimal design displays a tapered characteristic for channels 1, 3 and 4, and a diverging characteristic in height for channels 2 and 5, producing a Pcell=8894 Wm-2, about 22.5% increment. The reduced channel heights of channels 2-4 significantly increase the sub-rib convection and widths for effectively removing liquid water and oxygen transport in gas diffusion layer. The final diverging channel minimizes the leakage of fuel to outlet via sub-rib convection from channel 4 to channel 5. Near-optimal design without huge loss in cell performance but is easily manufactured is tested. The use of a straight, final channel of 0.1 mm height has led to 7.37% power loss, while the design with all channel widths to be 1 mm with optimal channel heights obtained above yields only 1.68% loss of current density. The presence of a final, diverging channel has greater impact on cell performance than the fine adjustment of channel width at the simulation conditions set herein studied.

Keywords: optimization, flow field design, simplified conjugate-gradient method, serpentine flow field, sub-rib convection

Procedia PDF Downloads 297

Authors: A. Ramalho, L. Romeiro, R. Ventura, A. Suleman

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A real-time path planning framework for Unmanned Air Vehicles, and in particular multi-rotors is proposed. The framework is designed to provide feasible trajectories from the current UAV position to a goal state, taking into account constraints such as obstacle avoidance, problem kinematics, and vehicle limitations such as maximum speed and maximum acceleration. The framework computes feasible paths online, allowing to avoid new, unknown, dynamic obstacles without fully re-computing the trajectory. These features are achieved using an iterative process in which the robot computes and optimizes the trajectory while performing the mission objectives. A first trajectory is computed using a modified Rapidly-Exploring Random Tree (RRT) algorithm, that provides trajectories that respect a maximum curvature constraint. The trajectory optimization is accomplished using the Interior Point Optimizer (IPOPT) as a solver. The framework has proven to be able to compute a trajectory and optimize to a locally optimal with computational efficiency making it feasible for real-time operations.

Keywords: interior point optimization, multi-rotors, online path planning, rapidly exploring random trees, trajectory optimization

Procedia PDF Downloads 136

Authors: Noureddine Henka, Sami Tazi, Mohamad Assaad

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The electrical grid is a highly intricate system designed to transfer electricity from production areas to consumption areas. The Transmission System Operator (TSO) is responsible for ensuring the efficient distribution of electricity and maintaining the grid's safety and quality. However, due to the increasing integration of intermittent renewable energy sources, there is a growing level of uncertainty, which requires a faster responsive approach. A potential solution involves the use of electrical segmentation, which involves creating coherence zones where electrical disturbances mainly remain within the zone. Indeed, by means of coherent electrical zones, it becomes possible to focus solely on the sub-zone, reducing the range of possibilities and aiding in managing uncertainty. It allows faster execution of operational processes and easier learning for supervised machine learning algorithms. Electrical segmentation can be applied to various applications, such as electrical control, minimizing electrical loss, and ensuring voltage stability. Since the electrical grid can be modeled as a graph, where the vertices represent electrical buses and the edges represent electrical lines, identifying coherent electrical zones can be seen as a clustering task on graphs, generally called community detection. Nevertheless, a critical criterion for the zones is their ability to remain resilient to the electrical evolution of the grid over time. This evolution is due to the constant changes in electricity generation and consumption, which are reflected in graph structure variations as well as line flow changes. One approach to creating a resilient segmentation is to design robust zones under various circumstances. This issue can be represented through a multiplex graph, where each layer represents a specific situation that may arise on the grid. Consequently, resilient segmentation can be achieved by conducting community detection on this multiplex graph. The multiplex graph is composed of multiple graphs, and all the layers share the same set of vertices. Our proposal involves a model that utilizes a unified representation to compute a flattening of all layers. This unified situation can be penalized to obtain (K) connected components representing the robust electrical segmentation clusters. We compare our robust segmentation to the segmentation based on a single reference situation. The robust segmentation proves its relevance by producing clusters with high intra-electrical perturbation and low variance of electrical perturbation. We saw through the experiences when robust electrical segmentation has a benefit and in which context.

Keywords: community detection, electrical segmentation, multiplex graph, power grid

Procedia PDF Downloads 79

Authors: Gichun Cha, Byoungjoon Yu, Jihwan Park, Minsoo Park, Junghyun Im, Sehwan Park, Sujung Sin, Seunghee Park

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The goal of this research is to estimate a structural shape change using terrestrial laser scanning. This study proceeds with development of data reduction and shape change estimation algorithm for large-capacity scan data. The point cloud of scan data was converted to voxel and sampled. Technique of shape estimation is studied to detect changes in structure patterns, such as skyscrapers, bridges, and tunnels based on large point cloud data. The point cloud analysis applies the octree data structure to speed up the post-processing process for change detection. The point cloud data is the relative representative value of shape information, and it used as a model for detecting point cloud changes in a data structure. Shape estimation model is to develop a technology that can detect not only normal but also immediate structural changes in the event of disasters such as earthquakes, typhoons, and fires, thereby preventing major accidents caused by aging and disasters. The study will be expected to improve the efficiency of structural health monitoring and maintenance.

Keywords: terrestrial laser scanning, point cloud, shape information model, displacement measurement

Procedia PDF Downloads 236

Authors: K. Mondal, S. Talapatra, M. Terrones, S. Pokhrel, C. Frizzel, B. Sumpter, V. Meunier, A. L. Elias

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Worldwide energy independence is reliant on the ability to leverage locally available resources for fuel production. Recently, syngas produced through gasification of carbonaceous materials provided a gateway to a host of processes for the production of various chemicals including transportation fuels. The basis of the production of gasoline and diesel-like fuels is the Fischer Tropsch Synthesis (FTS) process: A catalyzed chemical reaction that converts a mixture of carbon monoxide (CO) and hydrogen (H₂) into long chain hydrocarbons. Until now, it has been argued that only transition metal catalysts (usually Co or Fe) are active toward the CO hydrogenation and subsequent chain growth in the presence of hydrogen. In this paper, we demonstrate that carbon nanotube (CNT) surfaces are also capable of hydro-deoxygenating CO and producing long chain hydrocarbons similar to that obtained through the FTS but with orders of magnitude higher conversion efficiencies than the present state-of-the-art FTS catalysts. We have used advanced experimental tools such as XPS and microscopy techniques to characterize CNTs and identify C-O functional groups as the active sites for the enhanced catalytic activity. Furthermore, we have conducted quantum Density Functional Theory (DFT) calculations to confirm that C-O groups (inherent on CNT surfaces) could indeed be catalytically active towards reduction of CO with H₂, and capable of sustaining chain growth. The DFT calculations have shown that the kinetically and thermodynamically feasible route for CO insertion and hydro-deoxygenation are different from that on transition metal catalysts. Experiments on a continuous flow tubular reactor with various nearly metal-free CNTs have been carried out and the products have been analyzed. CNTs functionalized by various methods were evaluated under different conditions. Reactor tests revealed that the hydrogen pre-treatment reduced the activity of the catalysts to negligible levels. Without the pretreatment, the activity for CO conversion as found to be 7 µmol CO/g CNT/s. The O-functionalized samples showed very activities greater than 85 µmol CO/g CNT/s with nearly 100% conversion. Analyses show that CO hydro-deoxygenation occurred at the C-O/O-H functional groups. It was found that while the products were similar to FT products, differences in selectivities were observed which, in turn, was a result of a different catalytic mechanism. These findings now open a new paradigm for CNT-based hydrogenation catalysts and constitute a defining point for obtaining clean, earth abundant, alternative fuels through the use of efficient and renewable catalyst.

Keywords: CNT, CO Hydrodeoxygenation, DFT, liquid fuels, XPS, XTL

Procedia PDF Downloads 348

Authors: Khaled Nagaty, Heba Nagaty, Gerard McKee

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A signature is a behavioral biometric that is used for authenticating users in most financial and legal transactions. Signatures can be easily forged by skilled forgers. Therefore, it is essential to verify whether a signature is genuine or forged. The aim of any signature verification algorithm is to accommodate the differences between signatures of the same person and increase the ability to discriminate between signatures of different persons. This work presented in this paper proposes an automatic signature verification system to indicate whether a signature is genuine or not. The system comprises four phases: (1) The pre-processing phase in which image scaling, binarization, image rotation, dilation, thinning, and connecting ridge breaks are applied. (2) The feature extraction phase in which global and local features are extracted. The local features are minutiae points, curvature orientation, and curve plateau. The global features are signature area, signature aspect ratio, and Hu moments. (3) The post-processing phase, in which false minutiae are removed. (4) The classification phase in which features are enhanced before feeding it into the classifier. k-nearest neighbors and support vector machines are used. The classifier was trained on a benchmark dataset to compare the performance of the proposed offline signature verification system against the state-of-the-art. The accuracy of the proposed system is 92.3%.

Keywords: signature, ridge breaks, minutiae, orientation

Procedia PDF Downloads 148

Authors: Zahra Abdolkarimi, Naser Zourikalatehsamad,

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Up to 40 years ago, after recognition of epilepsy, it was generally believed that these attacks occurred randomly and suddenly. However, thanks to the advance of mathematics and engineering, such attacks can be predicted within a few minutes or hours. In this way, various algorithms for long-term prediction of the time and frequency of the first attack are presented. In this paper, by considering the nonlinear nature of brain signals and dynamic recorded brain signals, ANFIS model is presented to predict the brain signals, since according to physiologic structure of the onset of attacks, more complex neural structures can better model the signal during attacks. Contribution of this work is the co-evolution algorithm for optimization of ANFIS network parameters. Our objective is to predict brain signals based on time series obtained from brain signals of the people suffering from epilepsy using ANFIS. Results reveal that compared to other methods, this method has less sensitivity to uncertainties such as presence of noise and interruption in recorded signals of the brain as well as more accuracy. Long-term prediction capacity of the model illustrates the usage of planted systems for warning medication and preventing brain signals.

Keywords: co-evolution algorithms, brain signals, time series, neural networks, ANFIS model, physiologic structure, time prediction, epilepsy suffering, illustrates model

Procedia PDF Downloads 284

Authors: Karthiyayini Nagarajan, P. V. RamaKrishna

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Radar Imaging techniques provides extensive applications in the field of remote sensing, majorly Synthetic Aperture Radar(SAR) that provide high resolution target images. This paper work puts forward the effective and realizable signal generation and processing for SAR images. The major units in the system include camera, signal generation unit, signal processing unit and display screen. The real radio channel is replaced by its mathematical model based on optical image to calculate a reflected signal model in real time. Signal generation realizes the algorithm and forms the radar reflection model. Signal processing unit provides range and azimuth resolution through matched filtering and spectrum analysis procedure to form radar image on the display screen. The restored image has the same quality as that of the optical image. This SAR imaging system has been designed and implemented using MATLAB and Quartus II tools on Stratix III device as a System(lab bench) that works in real time to study/investigate on radar imaging rudiments and signal processing scheme for educational and research purposes.

Keywords: synthetic aperture radar, radio reflection model, lab bench

Procedia PDF Downloads 468

Authors: Mo-Se Lee, Cheol-Hwi Ahn, Kee-Young Kwahk, Hyunchul Ahn

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Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market.

Keywords: convolutional neural network, deep learning, Korean stock market, stock market prediction

Procedia PDF Downloads 425

Authors: Wai Ho Darrell Kwok

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Building entities are valuable assets of a society, however, all of them are suffered from the ravages of weather and time. Facilitating onerous maintenance activities is the only way to either maintain or enhance the value and contemporary standard of the premises. By the way, maintenance budget is always bounded by the corresponding threshold limit. In order to optimize the limited resources allocation in carrying out maintenance, there is a substantial need to prioritize maintenance work. This paper reveals the application of Fuzzy AHP in a Facilities Management Company determining the maintenance priorities on the basis of predetermined criteria, viz., Building Status (BS), Effects on Fabrics (EF), Effects on Sustainability (ES), Effects on Users (EU), Importance of Usage (IU) and Physical Condition (PC) in dealing with categorized 8 predominant building components maintenance aspects for building premises. From the case study, it is found that ‘building exterior repainting or re-tiling’, ‘spalling concrete repair works among exterior area’ and ‘lobby renovation’ are the top three maintenance priorities from facilities manager and maintenance expertise personnel. Through the application of the Fuzzy AHP for maintenance priorities decision algorithm, a more systemic and easier comparing scalar linearity factors being explored even in considering other multiple criteria decision scenarios of building maintenance issue.

Keywords: building maintenance, fuzzy AHP, maintenance priority, multi-criteria decision making

Procedia PDF Downloads 243

Authors: Cheng-Jhe Lee, Chiun-Chieh Hsu

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Due to the rapid development of the Internet and the increased availability of digital documents, the excessive information on the Internet has led to information overflow problem. In order to solve these problems for effective information retrieval, document clustering in text mining becomes a popular research topic. Clustering is the unsupervised classification of data items into groups without the need of training data. Many conventional document clustering methods perform inefficiently for large document collections because they were originally designed for relational database. Therefore they are impractical in real-world document clustering and require special handling for high dimensionality and high volume. We propose the FIHC (Frequent Itemset-based Hierarchical Clustering) method, which is a hierarchical clustering method developed for document clustering, where the intuition of FIHC is that there exist some common words for each cluster. FIHC uses such words to cluster documents and builds hierarchical topic tree. In this paper, we combine FIHC algorithm with ontology to solve the semantic problem and mine the meaning behind the words in documents. Furthermore, we use the closed frequent itemsets instead of only use frequent itemsets, which increases efficiency and scalability. The experimental results show that our method is more accurate than those of well-known document clustering algorithms.

Keywords: FIHC, documents clustering, ontology, closed frequent itemset

Procedia PDF Downloads 399

Authors: Marek Dlapa

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The algebraic approach is applied to the control of the HiMAT (Highly Maneuverable Aircraft Technology). The objective is to find a robust controller which guarantees robust stability and decoupled control of longitudinal model of a scaled remotely controlled vehicle version of the advanced fighter HiMAT. Control design is performed by decoupling the nominal MIMO (multi-input multi-output) system into two identical SISO (single-input single-output) plants which are approximated by a 4th order transfer function. The algebraic approach is then used for pole placement design, and the nominal closed-loop poles are tuned so that the peak of the µ-function is minimal. As an optimization tool, evolutionary algorithm Differential Migration is used in order to overcome the multimodality of the cost function yielding simple controller with decoupling for nominal plant which is compared with the D-K iteration through simulations of standard longitudinal manoeuvres documenting decoupled control obtained from algebraic approach for nominal plant as well as worst case perturbation.

Keywords: algebraic approach, evolutionary computation, genetic algorithms, HiMAT, robust control, structured singular value

Procedia PDF Downloads 141

Authors: Sahar Jahani, Meryem Ayse Yucel, David Boas, Seyed Kamaledin Setarehdan

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Near-infrared spectroscopy allows monitoring of oxy- and deoxy-hemoglobin concentration changes associated with hemodynamic response function (HRF). HRF is usually affected by natural physiological hemodynamic (systemic interferences) which occur in all body tissues including brain tissue. This makes HRF extraction a very challenging task. In this study, we used Kalman filter based on a general linear model (GLM) of brain activity to define the proportion of systemic interference in the brain hemodynamic. The performance of the proposed algorithm is evaluated in terms of the peak to peak error (Ep), mean square error (MSE), and Pearson’s correlation coefficient (R2) criteria between the estimated and the simulated hemodynamic responses. This technique also has the ability of real time estimation of single trial functional activations as it was applied to classify finger tapping versus resting state. The average real-time classification accuracy of 74% over 11 subjects demonstrates the feasibility of developing an effective functional near infrared spectroscopy for brain computer interface purposes (fNIRS-BCI).

Keywords: hemodynamic response function, functional near-infrared spectroscopy, adaptive filter, Kalman filter

Procedia PDF Downloads 169

Authors: Tong He, Long Chen, Irag Mantegh, Wen-Fang Xie

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This paper proposes an image-based obstacle avoidance and tracking target identification strategy in GPS-degraded or GPS-denied environment for an Unmanned Aerial Vehicle (UAV). The traditional force algorithm for obstacle avoidance could produce local minima area, in which UAV cannot get away obstacle effectively. In order to eliminate it, an artificial potential approach based on harmonic potential is proposed to guide the UAV to avoid the obstacle by using the vision system. And image-based visual servoing scheme (IBVS) has been adopted to implement the proposed obstacle avoidance approach. In IBVS, the pixel accuracy is a key factor to realize the obstacle avoidance. In this paper, the deep reinforcement learning framework has been applied by reducing pixel errors through constant interaction between the environment and the agent. In addition, the combination of OpenTLD and Tensorflow based on neural network is used to identify the type of tracking target. Numerical simulation in Matlab and ROS GAZEBO show the satisfactory result in target identification and obstacle avoidance.

Keywords: image-based visual servoing, obstacle avoidance, tracking target identification, deep reinforcement learning, artificial potential approach, neural network

Procedia PDF Downloads 143

Authors: Partha P. Gopmandal, Somnath Bhattacharyya, Naren Bag

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In this article, we have studied the effect of pH-regulated surface charge on the electroosmotic flow (EOF) through nanochannel filled with binary symmetric electrolyte solution. The channel wall possesses either an acidic or a basic functional group. Going beyond the widely employed Debye-Huckel linearization, we develop a mathematical model based on Nernst-Planck equation for the charged species, Poisson equation for the induced potential, Stokes equation for fluid flow. A finite volume based numerical algorithm is adopted to study the effect of key parameters on the EOF. We have computed the coupled governing equations through the finite volume method and our results found to be in good agreement with the analytical solution obtained from the corresponding linear model based on low surface charge condition or strong electrolyte solution. The influence of the surface charge density, reaction constant of the functional groups, bulk pH, and concentration of the electrolyte solution on the overall flow rate is studied extensively. We find the effect of surface charge diminishes with the increase in electrolyte concentration. In addition for strong electrolyte, the surface charge becomes independent of pH due to complete dissociation of the functional groups.

Keywords: electroosmosis, finite volume method, functional group, surface charge

Procedia PDF Downloads 421

Authors: Ariful Islam, Showkat Ahmad Lone

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Statistical distributions have long been employed in the assessment of semiconductor devices and product reliability. The power function-distribution is one of the most important distributions in the modern reliability practice and can be frequently preferred over mathematically more complex distributions, such as the Weibull and the lognormal, because of its simplicity. Moreover, it may exhibit a better fit for failure data and provide more appropriate information about reliability and hazard rates in some circumstances. This study deals with estimating information about failure times of items under step-stress partially accelerated life tests for competing risk based on adoptive type-I progressive hybrid censoring criteria. The life data of the units under test is assumed to follow Mukherjee-Islam distribution. The point and interval maximum-likelihood estimations are obtained for distribution parameters and tampering coefficient. The performances of the resulting estimators of the developed model parameters are evaluated and investigated by using a simulation algorithm.

Keywords: adoptive progressive hybrid censoring, competing risk, mukherjee-islam distribution, partially accelerated life testing, simulation study

Procedia PDF Downloads 347

Authors: Violeta Damjanovic-Behrendt

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This paper presents an approach for optimal cyber security decisions to protect instances of a federated Internet of Things (IoT) platform in the cloud. The presented solution implements the repeated Stackelberg Security Game (SSG) and a model called Stochastic Human behaviour model with AttRactiveness and Probability weighting (SHARP). SHARP employs the Subjective Utility Quantal Response (SUQR) for formulating a subjective utility function, which is based on the evaluations of alternative solutions during decision-making. We augment the repeated SSG (including SHARP and SUQR) with a reinforced learning algorithm called Naïve Q-Learning. Naïve Q-Learning belongs to the category of active and model-free Machine Learning (ML) techniques in which the agent (either the defender or the attacker) attempts to find an optimal security solution. In this way, we combine GT and ML algorithms for discovering optimal cyber security policies. The proposed security optimization components will be validated in a collaborative cloud platform that is based on the Industrial Internet Reference Architecture (IIRA) and its recently published security model.

Keywords: security, internet of things, cloud computing, stackelberg game, machine learning, naive q-learning

Procedia PDF Downloads 355

Authors: Fernandez liporace, Mercedes Uriel Fabiana

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Academic achievement in high school and college students is currently a matter of concern. National and international assessments show high schoolers as low achievers, and local statistics indicate alarming dropout percentages in this educational level. Even so, 80% of those students intend attending higher education. On the other hand, applications to Public National Universities are free and non-selective by examination procedures. Though initial registrations are massive (307.894 students), only 50% of freshmen pass their first year classes, and 23% achieves a degree. Low performances use to be a common problem. Hence, freshmen adaptation, their adjustment, dropout and low academic achievement arise as topics of agenda. Besides, the hinge between high school and college must be examined in depth, in order to get an integrated and successful path from one educational stratum to the other. Psychology aims at developing two main research lines to analyse the situation. One regarding psychometric scales, designing and/or adapting tests, examining their technical properties and their theoretical validity (e.g., academic motivation, learning strategies, learning styles, coping, perceived social support, parenting styles and parental consistency, paradoxical personality as correlated to creative skills, psychopathological symptomatology). The second research line emphasizes relationships within the variables measured by the former scales, facing the formulation and testing of predictive models of academic achievement, establishing differences by sex, age, educational level (high school vs college), and career. Pursuing these goals, several studies were carried out in recent years, reporting findings and producing assessment technology useful to detect students academically at risk as well as good achievers. Multiple samples were analysed totalizing more than 3500 participants (2500 from college and 1000 from high school), including descriptive, correlational, group differences and explicative designs. A brief on the most relevant results is presented. Providing information to design specific interventions according to every learner’s features and his/her educational environment comes up as a mid-term accomplishment. Furthermore, that information might be helpful to adapt curricula by career, as well as for implementing special didactic strategies differentiated by sex and personal characteristics.

Keywords: academic achievement, higher education, high school, psychological assessment

Procedia PDF Downloads 370

Authors: Taufik Rizkiandi, Teuku Yuri M. Zagloel, Andri Dwi Setiawan

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Distribution and growth in the transportation and warehousing business sector decreased by 15,04%. There was a decrease in Gross Domestic Product (GDP) contribution level from rank 7 of 4,41% in 2019 to 3,81% in rank 8 in 2020. A decline in the transportation and warehousing business sector contributes to GDP, resulting in oil and gas companies implementing an efficient supply chain strategy to ensure the availability of goods, especially lubricants. Fluctuating demand for lubricants and warehouse service time limits are essential things that are taken into account in determining an efficient route. Add depots points as a solution so that demand for lubricants is fulfilled (not stock out). However, adding a depot will increase operating costs and storage costs. Therefore, it is necessary to optimize the addition of depots using the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). This research case study was conducted at an oil and gas company that produces lubricants from 2019 to 2021. The study results obtained the optimal route and the addition of a depot with a minimum additional cost. The total cost remains efficient with the addition of a depot when compared to one depot from Jakarta.

Keywords: CVRPTW, optimal route, depot, tabu search algorithm

Procedia PDF Downloads 136

Authors: Bernard Tan

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The paper presents a conceptual framework which serves as an art model of Singapore artistic identity. Specifically, the study examines Singapore's artistic identity through the artworks of the country’s significant artists covering the period 1950s to the present. Literature review will discuss the challenges of favouring or choosing one artist over the other. Methodology provides an overview of the perspectives of local artists and surveys Singapore’s artistic histories through qualitative interviews and case studies. Analysis from qualitative data reveals that producing works of accrued visual significance for the country which captures it zeitgeist further strengthens artist’s artistic identity, and consequently, their works remembered by future generations. The paper presents a conceptual framework for Singapore’s artistic identity by categorising it into distinctive categories or Periods: Colonial Period (pre-1965); Nation Building Period (1965-1988); Globalisation Period (1989-2000); Paternal Production Period (2001-2015); and A New Era (2015-present). Godalisation, coined from God and Globalisation – by artist and art collector, Teng Jee Hum – is a direct reference to the godlike influence on Singapore by its founding Father, Mr Lee Kuan Yew, the country’s first Prime Minister who steered the city state “from Third World to First” for close to half a century, from 1965 to his passing in 2015. A detailed schema showing important factors in different art categories: key global geopolitics, key local social-politics, and significant events will be analysed in depth. Main artist groups or artist initiatives which evolved in Singapore during the different Periods from pre-1965 to the present will be categorized and discussed. Taken as a whole, all these periods collectively add up to the Godalisation Era; impacted by the social-political events and historical period of the nation, and captured through the visual representation of the country’s significant artists in their attempt at either visualizing or mythologizing the Singapore Story. The author posits a co-relation between a nation’s economic success and the value or price appreciation of the country’s artist of significance artworks. The paper posed a rhetorical question: “Which Singapore’s artist will historian of the future – and by extension, the people of the country from future generations – remember? Who will remain popular? Whilst which artists will be forgotten.” The searching question: “Who will survive, be remembered in the annals of history and, above all, how to ensure the survival of one’s nation artistic identity? The art that last will probably be determined by the future, in the future, where art historians pontificate from a later vantage point.

Keywords: artistic identity, art collection, godalisation, singapore

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Authors: Andrew R. Winters, Gregor J. Gassner

Abstract:

A high-order numerical magentohydrodynamics (MHD) solver built upon a non-linear entropy stable numerical flux function that supports eight traveling wave solutions will be described. The method is designed to treat the divergence-free constraint on the magnetic field in a similar fashion to a hyperbolic divergence cleaning technique. The solver is especially well-suited for flows involving strong discontinuities due to its strong stability without the need to enforce artificial low density or energy limits. Furthermore, a new formulation of the numerical algorithm to guarantee positivity of the pressure during the simulation is described and presented. By construction, the solver conserves mass, momentum, and energy and is entropy stable. High spatial order is obtained through the use of a third order limiting technique. High temporal order is achieved by utilizing the family of strong stability preserving (SSP) Runge-Kutta methods. Main attributes of the solver are presented as well as details on an implementation of the new solver into the multi-physics, multi-scale simulation code FLASH. The accuracy, robustness, and computational efficiency is demonstrated with a variety of numerical tests. Comparisons are also made between the new solver and existing methods already present in FLASH framework.

Keywords: entropy stability, finite volume scheme, magnetohydrodynamics, pressure positivity

Procedia PDF Downloads 343

Authors: Seyyed Masoud Seyyedoshohadaei, Borhanuddin Mohd Ali

Abstract:

To support group mobility, the NEMO Basic Support Protocol has been standardized as an extension of Mobile IP that enables an entire network to change its point of attachment to the Internet. Using NEMO in IEEE 802.16e (WiMax) networks causes latency in handover procedure and affects seamless communication of real-time applications. To decrease handover latency and service disruption time, an integrated scheme named Optimized Fast NEMO (OFNEMO) was introduced by authors of this paper. In OFNEMO a pre-establish multi tunnels concept, cross function optimization and cross layer design are used. In this paper, an analytical model is developed to evaluate total cost consisting of signaling and packet delivery costs of the OFNEMO compared with RFC3963. Results show that OFNEMO increases probability of predictive mode compared with RFC3963 due to smaller handover latency. Even though OFNEMO needs extra signalling to pre-establish multi tunnel, it has less total cost thanks to its optimized algorithm. OFNEMO can minimize handover latency for supporting real time application in moving networks.

Keywords: fast mobile IPv6, handover latency, IEEE802.16e, network mobility

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Authors: Ruzanna Muradyan

Abstract:

Medical data mining is a crucial field in contemporary healthcare that offers cutting-edge tactics with enormous potential to transform patient care. This abstract examines how sophisticated data mining techniques could transform the healthcare industry, with a special focus on how they might improve patient outcomes. Healthcare data repositories have dynamically evolved, producing a rich tapestry of different, multi-dimensional information that includes genetic profiles, lifestyle markers, electronic health records, and more. By utilizing data mining techniques inside this vast library, a variety of prospects for precision medicine, predictive analytics, and insight production become visible. Predictive modeling for illness prediction, risk stratification, and therapy efficacy evaluations are important points of focus. Healthcare providers may use this abundance of data to tailor treatment plans, identify high-risk patient populations, and forecast disease trajectories by applying machine learning algorithms and predictive analytics. Better patient outcomes, more efficient use of resources, and early treatments are made possible by this proactive strategy. Furthermore, data mining techniques act as catalysts to reveal complex relationships between apparently unrelated data pieces, providing enhanced insights into the cause of disease, genetic susceptibilities, and environmental factors. Healthcare practitioners can get practical insights that guide disease prevention, customized patient counseling, and focused therapies by analyzing these associations. The abstract explores the problems and ethical issues that come with using data mining techniques in the healthcare industry. In order to properly use these approaches, it is essential to find a balance between data privacy, security issues, and the interpretability of complex models. Finally, this abstract demonstrates the revolutionary power of modern data mining methodologies in transforming the healthcare sector. Healthcare practitioners and researchers can uncover unique insights, enhance clinical decision-making, and ultimately elevate patient care to unprecedented levels of precision and efficacy by employing cutting-edge methodologies.

Keywords: data mining, healthcare, patient care, predictive analytics, precision medicine, electronic health records, machine learning, predictive modeling, disease prognosis, risk stratification, treatment efficacy, genetic profiles, precision health

Procedia PDF Downloads 63

Authors: Cameron Hamilton, Walter Potter, Gerrit Hoogenboom, Ronald McClendon, Will Hobbs

Abstract:

A model was constructed to predict the amount of solar radiation that will make contact with the surface of the earth in a given location an hour into the future. This project was supported by the Southern Company to determine at what specific times during a given day of the year solar panels could be relied upon to produce energy in sufficient quantities. Due to their ability as universal function approximators, an artificial neural network was used to estimate the nonlinear pattern of solar radiation, which utilized measurements of weather conditions collected at the Griffin, Georgia weather station as inputs. A number of network configurations and training strategies were utilized, though a multilayer perceptron with a variety of hidden nodes trained with the resilient propagation algorithm consistently yielded the most accurate predictions. In addition, a modeled DNI field and adjacent weather station data were used to bolster prediction accuracy. In later trials, the solar radiation field was preprocessed with a discrete wavelet transform with the aim of removing noise from the measurements. The current model provides predictions of solar radiation with a mean square error of 0.0042, though ongoing efforts are being made to further improve the model’s accuracy.

Keywords: artificial neural networks, resilient propagation, solar radiation, time series forecasting

Procedia PDF Downloads 385

Authors: Kritiyaporn Kunsook

Abstract:

Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), decision tree, support vector machines (SVMs), Naïve Bayes, and ensemble classifier by voting are powerful data driven methods that are relatively less widely used in the mapping of technique of system, and thus have not been comparatively evaluated together thoroughly in this field. The performances of a series of MLAs, ANNs, decision tree, SVMs, Naïve Bayes, and ensemble classifier by voting in technique of hydroponic systems prospectively modeling are compared based on the accuracy of each model. Classification of hydroponic systems only covers the test samples from vegetables grown with Nutrient film technique (NFT) and Deep flow technique (DFT). The feature, which are the characteristics of vegetables compose harvesting height width, temperature, require light and color. The results indicate that the classification performance of the ANNs is 98%, decision tree is 98%, SVMs is 97.33%, Naïve Bayes is 96.67%, and ensemble classifier by voting is 98.96% algorithm respectively.

Keywords: artificial neural networks, decision tree, support vector machines, naïve Bayes, ensemble classifier by voting

Procedia PDF Downloads 375

Authors: Nuri Caglayan, H. Kursat Celik

Abstract:

There are many factors that influence the health and productivity of the animals in livestock production fields, including temperature, humidity, carbon dioxide (CO2), ammonia (NH3), hydrogen sulfide (H2S), physical activity and particulate matter. High NH3 concentrations reduce feed consumption and cause daily weight gain. At high concentrations, H2S causes respiratory problems and CO2 displace oxygen, which can cause suffocation or asphyxiation. Good air quality in livestock facilities can have an impact on the health and well-being of animals and humans. Air quality assessment basically depends on strictly given limits without taking into account specific local conditions between harmful gases and other meteorological factors. The stated limitations may be eliminated. using controlling systems based on neural networks and fuzzy logic. This paper describes a fuzzy logic based ventilation algorithm, which can calculate different fan speeds under pre-defined boundary conditions, for removing harmful gases from the production environment. In the paper, a fuzzy logic model has been developed based on a Mamedani’s fuzzy method. The model has been built on MATLAB software. As the result, optimum fan speeds under pre-defined boundary conditions have been presented.

Keywords: air quality, fuzzy logic model, livestock housing, fan speed

Procedia PDF Downloads 374

Authors: Rizwan Rizwan

Abstract:

This paper provides an overview of Federated Learning (FL) and its application in enhancing data security, privacy, and efficiency. FL utilizes three distinct architectures to ensure privacy is never compromised. It involves training individual edge devices and aggregating their models on a server without sharing raw data. This approach not only provides secure models without data sharing but also offers a highly efficient privacy--preserving solution with improved security and data access. Also we discusses various frameworks used in FL and its integration with machine learning, deep learning, and data mining. In order to address the challenges of multi--party collaborative modeling scenarios, a brief review FL scheme combined with an adaptive gradient descent strategy and differential privacy mechanism. The adaptive learning rate algorithm adjusts the gradient descent process to avoid issues such as model overfitting and fluctuations, thereby enhancing modeling efficiency and performance in multi-party computation scenarios. Additionally, to cater to ultra-large-scale distributed secure computing, the research introduces a differential privacy mechanism that defends against various background knowledge attacks.

Keywords: federated learning, differential privacy, gradient descent strategy, convergence, stability, threats

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Authors: Reena Aggarwal

Abstract:

The use of textile chemicals in the various pre and post-textile manufacturing processes has made the textile industry conscious of its negative contribution to environmental pollution. Popular environmentally friendly fibers such as recycled polyester and organic cotton have been now increasingly used by fabrics and apparel manufacturers. However, after these textiles or the finished apparel are manufactured, they have to be dyed in the same chemical dyes that are harmful and toxic to the environment. Dyeing is a major area of concern for the environment as well as for people who have chemical sensitivities as it may cause nausea, breathing difficulties, seizures, etc. Ayurvastra or herbal medical textiles are one step ahead of the organic lifestyle, which supports the core concept of holistic well-being and also eliminates the impact of harmful chemicals and pesticides. There is a wide range of herbs that can be used not only for dyeing but also for providing medicinal properties to the textiles like antibacterial, antifungal, antiseptic, antidepressant and for treating insomnia, skin diseases, etc. The concept of herbal dyeing of fabric is to manifest herbal essence in every aspect of clothing, i.e., from production to end-use, additionally to eliminate the impact of harmful chemical dyes and chemicals which are known to result in problems like skin rashes, headache, trouble concentrating, nausea, diarrhea, fatigue, muscle and joint pain, dizziness, difficulty breathing, irregular heartbeat and seizures. Herbal dyeing or finishing on textiles will give an extra edge to the textiles as it adds an extra function to the fabric. The herbal extracts can be applied to the textiles by a simple process like the pad dry cure method and mainly acts on the human body through the skin for aiding in the treatment of disease or managing the medical condition through its herbal properties. This paper, therefore, delves into producing Ayurvastra, which is a perfect amalgamation of cloth and wellness. The aim of the paper is to design and create herbal disposable and non-disposable medical textile products acting mainly topically (through the skin) for providing medicinal properties/managing medical conditions. Keeping that in mind, a range of antifungal socks and antibacterial napkins treated with turmeric and aloe vera were developed, which are recommended for the treatment of fungal and bacterial infections, respectively. Both Herbal Antifungal socks and Antibacterial napkins have proved to be efficient enough in managing and treating fungal and bacterial infections of the skin, respectively.

Keywords: ayurvastra, ayurveda, herbal, pandemic, sustainable

Procedia PDF Downloads 131