Search results for: crow search algorithm

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

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Authors: Seyran Naghdi

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Background: Orphan drug development is limited by very high costs attributed to the research and development and small size market. How health policymakers address this challenge to consider both supply and demand sides need to be explored for directing the policies and plans in the right way. The price is an important signal for pharmaceutical companies’ profitability and the patients’ accessibility as well. Objective: This study aims to find out the orphan drugs' price-setting patterns and approaches in health systems through a systematic review of the available evidence. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) approach was used. MedLine, Embase, and Web of Sciences were searched via appropriate search strategies. Through Medical Subject Headings (MeSH), the appropriate terms for pricing were 'cost and cost analysis', and it was 'orphan drug production', and 'orphan drug', for orphan drugs. The critical appraisal was performed by the Joanna-Briggs tool. A Cochrane data extraction form was used to obtain the data about the studies' characteristics, results, and conclusions. Results: Totally, 1,197 records were found. It included 640 hits from Embase, 327 from Web of Sciences, and 230 MedLine. After removing the duplicates, 1,056 studies remained. Of them, 924 studies were removed in the primary screening phase. Of them, 26 studies were included for data extraction. The majority of the studies (>75%) are from developed countries, among them, approximately 80% of the studies are from European countries. Approximately 85% of evidence has been produced in the recent decade. Conclusions: There is a huge variation of price-setting among countries, and this is related to the specific pharmacological market structure and the thresholds that governments want to intervene in the process of pricing. On the other hand, there is some evidence on the availability of spaces to reduce the very high costs of orphan drugs development through an early agreement between pharmacological firms and governments. Further studies need to focus on how the governments could incentivize the companies to agree on providing the drugs at lower prices.

Keywords: orphan drugs, orphan drug production, pricing, costs, cost analysis

Procedia PDF Downloads 163

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

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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

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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

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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

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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

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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

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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

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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

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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

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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 354

Authors: Clare Maxwell, Lorna Porcellato, Valerie Fleming, Kate Fleming

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A review of global online forums and social media reveals large numbers of mothers experiencing bottle refusal by their breastfed baby. It is difficult to determine precise numbers due to a lack of data, however, established virtual communities illustrate thousands of posts in relation to the issue. Mothers report various negative consequences of bottle refusal including delaying their return to work, time and financial outlay spent on methods to overcome it and experiencing stress, anxiety, and resentment of breastfeeding. A search of the literature revealed no studies being identified, and due to a lack of epidemiological data, a study investigating mother’s experiences of bottle refusal by their breastfed baby was undertaken. The aim of the study was to investigate UK mothers’ experiences of bottle refusal by their breastfed baby. Data were collected using an online questionnaire collecting quantitative and qualitative data. 841 UK mothers who had experienced or were experiencing bottle refusal by their breastfed baby completed the questionnaire. Data were analyzed using descriptive statistics and non-parametric testing. The results showed 61% (516/840) of mothers reported their breastfed baby was still refusing/had never accepted a bottle, with 39% (324/840) reporting their baby had eventually accepted. The most frequently reported reason to introduce a bottle was so partner/family could feed the baby 59% (499/839). 75% (634/841) of mothers intended their baby to feed on a bottle ‘occasionally’. Babies who accepted a bottle were more likely to be older at 1st attempt to introduce one than those babies who refused (Mdn = 12 weeks v 8 weeks, n = 286) (p = <0.001). Length of time taken to acceptance was 9 weeks (Mdn = 9, IQR = 18, R = 103.9, n = 306) with the older the baby was at 1st attempt to introduce a bottle being associated with a shorter length of time to acceptance (p = < 0.002). 60% (500/841) of mothers stated that none of the methods they used had worked. 26% (222/841) of mothers reported bottle refusal had had a negative impact upon their overall breastfeeding experience. 47% (303/604) reported they would have tried to introduce a bottle earlier to prevent refusal. This study provides a unique insight into the scenario of bottle refusal by breastfed babies. It highlights that bottle refusal by breastfed babies is a significant issue, which requires recognition from those communicating breastfeeding information to mothers.

Keywords: bottle feeding, bottle refusal, breastfeeding, infant feeding

Procedia PDF Downloads 164

Authors: Shaun G. Abbott, Rebecca C. Reynolds, James B. Etter, John B. F. de Wit

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The benefits of physical activity (PA) on health are well documented. Low socioeconomic status (SES) is associated with poor health, with PA a suggested mediator. Pedometers and accelerometers offer an effective behavior change tool to increase PA levels. While the role of pedometer and accelerometer use in increasing PA is recognized in many populations, little is known in low-SES groups. We are aiming to assess the effectiveness of pedometer- and accelerometer-based interventions for increasing PA step count and improving subsequent health outcomes among low-SES groups of high-income countries. Medline, Embase, PsycINFO, CENTRAL and SportDiscus databases were searched to identify articles published before 10th July, 2015; using search terms developed from previous systematic reviews. Inclusion criteria are: low-SES participants classified by income, geography, education, occupation or ethnicity; study duration minimum 4 weeks; an intervention and control group; wearing of an unsealed pedometer or accelerometer to objectively measure PA as step counts per day for the duration of the study. We retrieved 2,142 articles from our database searches, after removal of duplicates. Two investigators independently reviewed titles and abstracts of these articles (50% each) and a combined 20% sample were reviewed to account for inter-assessor variation. We are currently verifying the full texts of 430 articles. Included studies will be critically appraised for risk of bias using guidelines suggested by the Cochrane Public Health Group. Two investigators will extract data concerning the intervention; study design; comparators; steps per day; participants; context and presence or absence of obesity and/or chronic disease. Heterogeneity amongst studies is anticipated, thus a narrative synthesis of data will be conducted with the simplification of selected results into percentage increases from baseline to allow for between-study comparison. Results will be presented at the conference in December if selected.

Keywords: accelerometer, pedometer, physical activity, socioeconomic, step count

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Authors: Andrew R. Winters, Gregor J. Gassner

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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

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Authors: Seyyed Masoud Seyyedoshohadaei, Borhanuddin Mohd Ali

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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: Erwin Geroleo, Higinio Mappala

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Introduction Overt Hepatic Encephalopathy (OHE) is the most dreaded outcome of liver cirrhosis. Aside from the triggering factors which are already known to precipitate OHE, there is growing evidence that an altered gut microbiota profile (dysbiosis) can also trigger OHE. MHE is the mildest form of hepatic encephalopathy(HE), affecting about one-third of patients with cirrhosis, and close 80% of patients with cirrhosis and manifests as abnormalities in central nervous system function. Since these symptoms are subclinical most patients are not being treated to prevent OHE. The gut microbiota have been evaluated by several studies as a therapeutic option for MHE, especially in decreasing the levels of ammonia, thus preventing progression to OHE Objectives This study aims to evaluate the efficacy of probiotics in terms of reduction of ammonia levels in patient with minimal hepatic encephalopathies and to determine if Probiotics has role in the prevention of progression to overt hepatic encephalopathy in adult patients with minimal hepatic encephalopathy (MHE) Methods and Analysis The literature search strategy was restricted to human studies in adults subjects from 2004 to 2022. The Jadad Score Calculation was utilized in the assessment of the final studies included in this study. Eight (8) studies were included. Cochrane’s Revman Web, the Fixed Effects model and the Ztest were all used in the overall analysis of the outcomes. A p value of less than 0.0005 was statistically significant. Results. These results show that Probiotics significantly lowers the level of Ammonia in Cirrhotic patients with OHE. It also shows that the use of Probiotics significantly prevents the progression of MHE to OHE. The overall risk of bias graph indicates low risk of publication bias among the studies included in the meta-analysis. Main findings This research found that plasma ammonia concentration was lower among participants treated with probiotics (p<0.00001).) Ammonia level of the probiotics group is lower by 13.96 μmol/ on the average. Overall risk of developing overt hepatic encephalopathy in the probiotics group is shown to be decreased by 15% as compared to the placebo group Conclusion The analysis showed that compared with placebo, probiotics can decrease serum ammonia, may improve MHE and may prevent OHE.

Keywords: minimal hepatic encephalopathy, probiotics, liver cirrhosis, overt hepatic encephalopathy

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

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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 384

Authors: Kritiyaporn Kunsook

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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 372

Authors: Mohammadreza Heydariazad

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Productivity growth of wind energy as a clean source needed to achieve improved strategy in production and transmission and management of wind resources in order to increase quality of power and reduce costs. New technologies based on power converters that cause changing turbine speed to suit the wind speed blowing turbine improve extraction efficiency power from wind. This article introduces variable speed wind turbines and optimization of power, and presented methods to use superconducting inductor in the composition of power converter and is proposed the dc measurement for the wind farm and especially is considered techniques available to them. In fact, this article reviews mechanisms and function, changes of wind speed turbine according to speed control strategies of various types of wind turbines and examines power possible transmission and ac from producing location to suitable location for a strong connection integrating wind farm generators, without additional cost or equipment. It also covers main objectives of the dynamic control of wind turbines, and the methods of exploitation and the ways of using it that includes the unique process of these components. Effective algorithm is presented for power control in order to extract maximum active power and maintains power factor at the desired value.

Keywords: wind energy, generator, superconducting inductor, wind turbine power

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Authors: Nuri Caglayan, H. Kursat Celik

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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

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Authors: Rizwan Rizwan

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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: Sabah Khan

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The idea of universal Islam tends to exaggerate the extent of homogeneity in Islamic beliefs and practices across Muslim communities. In the age of migration, various Muslim communities are in diaspora. The immediate implication of this is what happens to Islam in diaspora? How Islam gets represented in new forms? Such pertinent questions need to be dealt with. This paper shall draw on the idea of religious transnationalism, primarily transnational Islam. There are multiple ways to conceptualize transnational phenomenon with reference to Islam in terms of flow of people, transnational organizations and networks; Ummah oriented solidarity and the new Muslim public sphere. This paper specifically deals with the new Muslim public sphere. It primarily refers to the space and networks enabled by new media and communication technologies, whereby Muslim identity and Islamic normativity are rehearsed, debated by people in different locales. A new sense of public is emerging across Muslim communities, which needs to be contextualized. This paper uses both primary and secondary data. Primary data elicited through content analysis of audio-visuals on social media and secondary sources of information ranging from books, articles, journals, etc. The basic aim of the paper is to focus on the emerging Muslim public sphere and the role of media in expanding public spheres of Islam. It also explores how Muslims in diaspora negotiate Islam and Islamic practices through media and the new Muslim public sphere. This paper cogently weaves in discussions firstly, of re-intellectualization of Islamic discourse in the public sphere. In other words, how Muslims have come to reimagine their collective identity and critically look at fundamental principles and authoritative tradition. Secondly, the emerging alternative forms of Islam by young Muslims in diaspora. In other words, how young Muslims search for unorthodox ways and media for religious articulation, including music, clothing and TV. This includes transmission and distribution of Islam in diaspora in terms of emerging ‘media Islam’ or ‘soundbite Islam’. The new Muslim public sphere has offered an arena to a large number of participants to critically engage with Islam, which leads not only to a critical engagement with traditional forms of Islamic authority but also emerging alternative forms of Islam and Islamic practices.

Keywords: Islam, media, Muslims, public sphere

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Authors: Maliha Ahmed Suleman, Sidra Ahmed Suleman

Abstract:

Introduction: The use of pacifiers is common amongst infants and young children as a comforting behavior. These non-nutritive sucking habits can be detrimental to the developing occlusion should they persist while the permanent dentition is established. Orthodontic pacifiers have been recommended as an alternative to conventional pacifiers as they are considered to have less interference with orofacial development. However, there is a lack of consensus on whether this is true. Aim and objectives: To review the prevalence of malocclusion associated with the use of orthodontic pacifiers. Methodology: Literature was identified through a rigorous search of the Embase, Pubmed, CINAHL, and Cochrane Library databases. Articles published from 2000 onwards were included. In total, 5 suitable papers were identified. Results: One study showed that the use of orthodontic pacifiers increased the risk of malocclusion, as seen through a greater prevalence of accentuated overjet, posterior crossbites, and anterior open bites in comparison to individuals who did not use pacifiers. However, this study found that there was a clinically significant reduction in the prevalence of anterior open bites amongst orthodontic pacifier users in comparison to conventional pacifier users. Another study found that both types of pacifiers lead to malocclusion; however, they found no difference in the mean overjet and prevalence of anterior open bites amongst conventional and orthodontic pacifier users. In contrast, one study suggested that orthodontic pacifiers do not seem to be related to the development of malocclusions in the primary dentitions, and using them between the ages of 0-3 months was actually beneficial as it prevents thumb-sucking habits. One of the systemic reviews concluded that orthodontic pacifiers do not seem to reduce the occurrence of posterior crossbites; however, they could reduce the development of open bites by virtue of their thin neck design. Whereas another systematic review concluded that there were no differences as to the effects on the stomatognathic system when comparing conventional and orthodontic pacifiers. Conclusion: There is limited and conflicting evidence to support the notion that orthodontic pacifiers can reduce the prevalence of malocclusion when compared to conventional pacifiers. Well-designed randomized controlled trials are required in the future in order to thoroughly assess the effects of orthodontic pacifiers on the developing occlusion and orofacial structures.

Keywords: orthodontics, pacifier, malocclusion, review

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Authors: Sadia Munir

Abstract:

The aim of this study is to investigate changes in placental development and vascular dysfunction which subsequently affect feto-maternal health in pregnancies complicated by gestational diabetes mellitus (GDM). Fetal and postnatal adverse health outcomes of GDM are shown to be associated with disturbances in placental structure and function. Children of women with GDM are more likely to be obese and diabetic in childhood and adulthood. GDM also increases the risk of adverse pregnancy outcomes, including preeclampsia, birth injuries, macrosomia and neonatal hypoglycemia, respiratory distress syndrome, neonatal cardiac dysfunction and stillbirth. Incidences of type 2 diabetes in the MENA region are growing at an alarming rate which is estimated to become more than double by 2030. Five of the top 10 countries for diabetes prevalence in 2010 were in the Gulf region. GDM also increases the risk of development of type 2 diabetes. Interestingly, more than half of the women with GDM develop diabetes later in their life. The human placenta is a temporary organ located at the interface between mother and fetal blood circulation. Placenta has a central role as both a producer as well as a target of several molecules that are involved in placental development and function. We have investigated performed a Pubmed search with key words placenta, GDM, placental villi, vascularization, cytokines, growth factors, inflammation, hypoxia, oxidative stress and pathophysiology. We have investigated differences in the development and vascularization of placenta, their underlying causes and impact on feto-maternal health through literature review. We have also identified gaps in the literature and research questions that need to be answered to completely understand the central role of placenta in the GDM. This study is important in understanding the pathophysiology of placenta due to changes in the vascularization of villi, surface area and diameter of villous capillaries in pregnancies complicated by GDM. It is necessary to understand these mechanisms in order to develop treatments to reverse their effects on placental malfunctioning, which in turn, will result in improved mother and child health.

Keywords: gestational diabetes mellitus, placenta, vasculature, villi

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Authors: Erica Elice Uy, Toshihiro Noda, Kentaro Nakai, Jonathan Dungca

Abstract:

Deformation in a conventional cyclic triaxial test is normally measured by using point-wise measuring device. In this study, non-contact measurement technique was applied to be able to monitor and measure the occurrence of non-homogeneous behavior of the soil under cyclic loading. Non-contact measurement is executed through image processing. Two-dimensional measurements were performed using Lucas and Kanade optical flow algorithm and it was implemented Labview. In this technique, the non-homogeneous deformation was monitored using a mirrorless camera. A mirrorless camera was used because it is economical and it has the capacity to take pictures at a fast rate. The camera was first calibrated to remove the distortion brought about the lens and the testing environment as well. Calibration was divided into 2 phases. The first phase was the calibration of the camera parameters and distortion caused by the lens. The second phase was to for eliminating the distortion brought about the triaxial plexiglass. A correction factor was established from this phase. A series of consolidated undrained cyclic triaxial test was performed using a coarse soil. The results from the non-contact measurement technique were compared to the measured deformation from the linear variable displacement transducer. It was observed that deformation was higher at the area where failure occurs.

Keywords: cyclic loading, non-contact measurement, non-homogeneous, optical flow

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Authors: Soofiyan Atar, Adil Shaikh, Sahil Rajpurkar, Pragnesh Bhalala, Aniket Desai, Irfan Siddavatam

Abstract:

Deep reinforcement learning (deep RL) algorithms leverage the symbolic power of complex controllers by automating it by mapping sensory inputs to low-level actions. Deep RL eliminates the complex robot dynamics with minimal engineering. Deep RL provides high-risk involvement by directly implementing it in real-world scenarios and also high sensitivity towards hyperparameters. Tuning of hyperparameters on a pneumatic quadruped robot becomes very expensive through trial-and-error learning. This paper presents an automated learning control for a pneumatic quadruped robot using sample efficient deep Q learning, enabling minimal tuning and very few trials to learn the neural network. Long training hours may degrade the pneumatic cylinder due to jerk actions originated through stochastic weights. We applied this method to the pneumatic quadruped robot, which resulted in a hopping gait. In our process, we eliminated the use of a simulator and acquired a stable gait. This approach evolves so that the resultant gait matures more sturdy towards any stochastic changes in the environment. We further show that our algorithm performed very well as compared to programmed gait using robot dynamics.

Keywords: model-based reinforcement learning, gait stability, supervised learning, pneumatic quadruped

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Authors: Kofi Nkonkonya Mpuangnan, Samantha Govender, Hlengiwe Romualda Mhlongo

Abstract:

In recent years, a noticeable and worrisome pattern has emerged in numerous developing nations which is a steady and persistent rise in unemployment rates. This escalation of economic struggles has become a cause of great concern for parents who, having invested significant resources in their children's education, harboured hopes of achieving economic prosperity and stability for their families through secure employment. To effectively tackle this pressing unemployment issue, it is imperative to adopt a holistic approach, and a pivotal aspect of this approach involves incorporating entrepreneurial education seamlessly into the entire educational system. In this light, the authors explored approaches to integrating entrepreneurial education into school curriculum focusing on the following questions. How can an entrepreneurial mindset among learners be promoted in school? And how far have pedagogical approaches improved entrepreneurship in schools? To find answers to these questions, a systematic literature review underpinned by Human Capital Theory was adopted. This method was supported by the three stages of guidelines like planning, conducting, and reporting. The data were specifically sought from publishers with expansive coverage of scholarly literature like Sage, Taylor & Francis, Emirate, and Springer, covering publications from 1965 to 2023. The search was supported by two broad terms such as promoting entrepreneurial mindset in learners and pedagogical strategies for enhancing entrepreneurship. It was found that acquiring an entrepreneurial mindset through an innovative classroom environment, resilience, and guest speakers and industry experts. Also, teachers can promote entrepreneurial education through the adoption of pedagogical approaches such as hands-on learning and experiential activities, role-playing, business simulation games and creative and innovative teaching. It was recommended that the Ministry of Education should develop tailored training programs and workshops aimed at empowering educators with the essential competencies and insights to deliver impactful entrepreneurial education.

Keywords: education, entrepreneurship, school curriculum, pedagogical approaches, integration

Procedia PDF Downloads 97