Search results for: simulation training

Authors: San-Yih Lin, Hsien-Hao Teng

Abstract:

In this research, we use commercial software, ANSYS CFX, to carry on the simulation the F16 aerodynamics performance flow field. The flight with a modified Leading Edge Extension (LEX) is proposed to increase the lift/drag ratio. The Shear Stress Transport turbulent model is used. The unstructured grid system is generated by the ICEM CFD. The prism grid around the wall surface is generated to simulate boundary layer viscosity flow field and Tetrahedron Mesh is used for the other computation domain. The lift, drag, and pitch moment are computed. The strong vortex structures upper the wing and vortex bursts under different sweep angle of LEX are investigated.

Keywords: LEX, lift/drag ratio, pitch moment, vortex burst

Procedia PDF Downloads 320

Authors: Soulayma Smirani, Mourad Aidi, Taoufik Aguili

Abstract:

Energy harvesting has arisen as a prominent research area for low power delivery to RF devices. Rectennas have become a key element in this technology. In this paper, electromagnetic modeling of a rectenna system is presented. In our approach, a hybrid technique was demonstrated to associate both the method of auxiliary sources (MAS) and MoM-GEC (the method of moments combined with the generalized equivalent circuit technique). Auxiliary sources were used in order to substitute specific electronic devices. Therefore, a simple and controllable model is obtained. Also, it can easily be interconnected to form different topologies of rectenna arrays for more energy harvesting. At last, simulation results show the feasibility and simplicity of the proposed rectenna model with high precision and computation efficiency.

Keywords: computational electromagnetics, MoM-GEC method, rectennas, RF energy harvesting

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Authors: Fred Mageto

Abstract:

A great number of children in mainstream schools across Kenya are currently living with emotional, behavioural difficulties. This study aims to explore teachers’ perceptions of children’s emotional and behavioural difficulties (EBD) and their attributions of the causes of EBD. The relevance of this area of study to current educational practice is illustrated in the fact that primary school teachers in Kenya find classroom behaviour problems one of the major difficulties they face. The information presented in this study was gathered from 182 teachers that responded back to the survey, of whom 27 teachers were later interviewed. In general, teachers’ perceptions of EBD reflect personal experience, training, and attitudes. Teachers appear from this study to use words such as indifferent, frightened, withdrawn, aggressive, disobedient, hyperactive, less ambitious, lacking concentration, and academically weak to describe pupils with emotional and behavioural difficulties (EBD). The implications of this study are envisaged as being extremely important to support teachers addressing children’s EBD and shed light on the contributing factors to EBD for a successful teaching-learning process in Libyan primary schools.

Keywords: teachers, children, learning, emotional and behaviour difficulties

Procedia PDF Downloads 154

Authors: Suparman

Abstract:

This paper addresses the problem of the signal segmentation within a Bayesian framework by using reversible jump MCMC algorithm. The signal is modelled by piecewise constant Moving-Average (MA) model where the numbers of segments, the position of change-point, the order and the coefficient of the MA model for each segment are unknown. The reversible jump MCMC algorithm is then used to generate samples distributed according to the joint posterior distribution of the unknown parameters. These samples allow calculating some interesting features of the posterior distribution. The performance of the methodology is illustrated via several simulation results.

Keywords: piecewise, moving-average model, reversible jump MCMC, signal segmentation

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Authors: Hee-Guk Chae, Bo-Bae Song, Kyoung-Il Do, Jeong-Yun Seo, Yong-Seo Koo

Abstract:

In this study, an improved Electrostatic Discharge (ESD) protection circuit with low trigger voltage and high holding voltage is proposed. ESD has become a serious problem in the semiconductor process because the semiconductor density has become very high these days. Therefore, much research has been done to prevent ESD. The proposed circuit is a stacked structure of the new unit structure combined by the Zener Triggering (SCR ZTSCR) and the High Holding Voltage SCR (HHVSCR). The simulation results show that the proposed circuit has low trigger voltage and high holding voltage. And the stack technology is applied to adjust the various operating voltage. As the results, the holding voltage is 7.7 V for 2-stack and 10.7 V for 3-stack.

Keywords: ESD, SCR, latch-up, power clamp, holding voltage

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Authors: Encarnación Álvarez, Rosa M. García-Fernández, Juan F. Muñoz, Francisco J. Blanco-Encomienda

Abstract:

The problem of estimating a proportion has important applications in the field of economics, and in general, in many areas such as social sciences. A common application in economics is the estimation of the headcount index. In this paper, we define the general headcount index as a proportion. Furthermore, we introduce a new quantitative method for estimating the headcount index. In particular, we suggest to use the logistic regression estimator for the problem of estimating the headcount index. Assuming a real data set, results derived from Monte Carlo simulation studies indicate that the logistic regression estimator can be more accurate than the traditional estimator of the headcount index.

Keywords: poverty line, poor, risk of poverty, Monte Carlo simulations, sample

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Authors: Elmo Thiago Lins Cöuras Ford, Valentina Alessandra Carvalho do Vale

Abstract:

The simulation in wind tunnel is used thoroughly to model real situations of drainages of air. Besides the automotive industry, a great number of applications can be numbered: dispersion of pollutant, studies of pedestrians comfort and dispersion of particles. This work had the objective of visualizing the characteristics aerodynamics of two automobiles in different ways. To accomplish that drainage of air a fan that generated a speed exists (measured with anemometer of hot thread) of 4,1m/s and 4,95m/s. To visualize the path of the air through the cars, in the wind tunnel, smoke was used, obtained with it burns of vegetable oil. For “to do smoke” vegetable oil was used, that was burned for a tension of 20 V generated by a thread of 2,5 mm. The cars were placed inside of the wind tunnel with the drainage of “air-smoke” and photographed, registering like this the path lines around them, in the 3 different speeds.

Keywords: aerodynamics, vehicle drag, vegetable oil, wind tunnel

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Authors: Nicholas Milburn, Yu Liang, Dalei Wu

Abstract:

Athlete performance scoring within the climbing do-main presents interesting challenges as the sport does not have an objective way to assign skill. Assessing skill levels within any sport is valuable as it can be used to mark progress while training, and it can help an athlete choose appropriate climbs to attempt. Machine learning-based methods are popular for complex problems like this. The dataset available was composed of dynamic force data recorded during climbing; however, this dataset came with challenges such as data scarcity, imbalance, and it was temporally heterogeneous. Investigated solutions to these challenges include data augmentation, temporal normalization, conversion of time series to the spectral domain, and cross validation strategies. The investigated solutions to the classification problem included light weight machine classifiers KNN and SVM as well as the deep learning with CNN. The best performing model had an 80% accuracy. In conclusion, there seems to be enough information within climbing force data to accurately categorize climbers by skill.

Keywords: classification, climbing, data imbalance, data scarcity, machine learning, time sequence

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Authors: Rachel Nir, Tina Mckee

Abstract:

In the UK, reliance has been placed on the professions to ‘heal themselves’ in improving equality and diversity. This approach has faltered, in part due to the global economic climate, and stimulus is needed to make faster equality progress. Recent empirical evidence has identified specific diversity barriers, namely: the cost of training; the use of high school grades as a primary selection criteria; the significance of prior work experience in recruitment decisions; and recruitment from elite universities. Students from majority groups and affluent backgrounds are advantaged over their counterparts. We as educators are passionate about resisting this. We believe that education can be a key agent of change. As part of this belief, the presenters have recently designed learning and teaching materials for the 2015/16 academic year. These are aimed at undergraduate law students for the purpose of 1) educating them on career barriers; 2) helping them to develop personal strategies to overcome them; and 3) encouraging them to address their own biases, both conscious and implicit, so that they, themselves, may be fairer employers and managers in the future.

Keywords: career barriers, challenging professional bias, education, elitism, personal student strategies

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Authors: Han Bing, Yin Zhenjie

Abstract:

In order to analyze the status of a diesel engine, as well as conduct fault prediction, a new prediction model based on a gray system is proposed in this paper, which takes advantage of the neural network and the genetic algorithm. The proposed GBPGA prediction model builds on the GM (1.5) model and uses a neural network, which is optimized by a genetic algorithm to construct the error compensator. We verify our proposed model on the diesel faulty simulation data and the experimental results show that GBPGA has the potential to employ fault prediction on diesel.

Keywords: fault prediction, neural network, GM(1, 5) genetic algorithm, GBPGA

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Authors: Xiaoyun Li, Suoang Longzhou

Abstract:

This paper derives the analytical solution of stellar distance according to its redshift based on the photon-dominated universe expansion model. Firstly, it calculates stellar separation speed and the farthest distance of observable stars via simulation. Then the analytical solution of stellar distance according to its redshift is derived. It shows that when the redshift is large, the stellar distance (and its separation speed) is not proportional to its redshift due to the relativity effect. It also reveals the relationship between stellar age and its redshift. The correctness of the analytical solution is verified by the latest astronomic observations of Ia supernovas in 2020.

Keywords: redshift, cosmic expansion model, analytical solution, stellar distance

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Authors: Ararat Khachatryan, Davit Mirzoyan

Abstract:

In sub micrometer technology, the aging phenomenon starts to have a significant impact on the reliability of integrated circuits by bringing performance degradation. For that reason, it is important to have a capability to evaluate the aging effects accurately. This paper presents an accurate aging measurement approach based on phase-locked loop (PLL) and voltage-controlled oscillator (VCO) circuit. The architecture is rejecting the circuit self-aging effect from the characteristics of PLL, which is generating the frequency without any aging phenomena affects. The aging monitor is implemented in low power 32 nm CMOS technology, and occupies a pretty small area. Aging simulation results show that the proposed aging measurement circuit improves accuracy by about 2.8% at high temperature and 19.6% at high voltage.

Keywords: aging effect, HCI, NBTI, nanoscale

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Authors: Selma Bouasria, Mahi Abdelkader, Abbès Azzi, Herouz Keltoum

Abstract:

This paper reports concept was used into the computational fluid dynamics (CFD) code cfx through user-defined functions to assess ventilation efficiency inside (forced-ventilation local). CFX is a simulation tool which uses powerful computer and applied mathematics, to model fluid flow situations for the prediction of heat, mass and momentum transfer and optimal design in various heat transfer and fluid flow processes to evaluate thermal comfort in a room ventilated (highly-glazed). The quality of the solutions obtained from CFD simulations is an effective tool for predicting the behavior and performance indoor thermo-aéraulique comfort.

Keywords: ventilation, thermal comfort, CFD, indoor environment, solar air heater

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Authors: Isa Muhammad Zumo, Musa Lawan

Abstract:

This study analyzes the land use/land cover changes in Damaturu metropolis from 1986 to 2005. LandSat TM Images of 1986, 1999, and 2005 were used. Built-up lands, agric lands, water body and other lands were created as themes within ILWIS 3.4 software. The images were displayed in False Colour Composite (FCC) for a better visualization and identification of the themes created. Training sample sets were collected based on the ground truth data during field the checks. Statistical data were then extracted from the classified sample set. Area in hectares for each theme was calculated for each year and the result for each land use/land cover types for each study year was compared. From the result, it was found out that built-up areas have a considerable increase from 37.71 hectares in 1986 to 1062.72 hectares in 2005. It has an annual increase rate of approximately 0.34%. The results also reveal that there is a decrease of 5829.66 hectares of other lands (vacant lands) from 1986 to 2005.

Keywords: land use, changes, analysis, environmental pollution

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Authors: Khader S. Al-Aidmat, Venus W. Samawi

Abstract:

Blurred vision in the misty atmosphere is essential problem which needs to be resolved. To solve this problem, we developed a technique to restore foggy degraded image from its original version using Back-propagation neural network (BP-NN). The suggested technique is based on mapping between foggy scene and its corresponding original scene. Seven different approaches are suggested based on type of features used in image restoration. Features are extracted from spatial and spatial-frequency domain (using DCT). Each of these approaches comes with its own BP-NN architecture depending on type and number of used features. The weight matrix resulted from training each BP-NN represents a fog filter. The performance of these filters are evaluated empirically (using PSNR), and perceptually. By comparing the performance of these filters, the effective features that suits BP-NN technique for restoring foggy images is recognized. This system proved its effectiveness and success in restoring moderate foggy images.

Keywords: artificial neural network, discrete cosine transform, feed forward neural network, foggy image restoration

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Authors: Rana I. Abdulghani, Amera I. Melhum

Abstract:

With the abnormal growth of the usage of computers over networks and under the consideration or agreement of most of the computer security experts who said that the goal of building a secure system is never achieved effectively, all these points led to the design of the intrusion detection systems(IDS). This research adopts a comparison between two techniques for network intrusion detection, The first one used the (Particles Swarm Optimization) that fall within the field (Swarm Intelligence). In this Act, the algorithm Enhanced for the purpose of obtaining the minimum error rate by amending the cluster centers when better fitness function is found through the training stages. Results show that this modification gives more efficient exploration of the original algorithm. The second algorithm used a (Back propagation NN) algorithm. Finally a comparison between the results of two methods used were based on (NSL_KDD) data sets for the construction and evaluation of intrusion detection systems. This research is only interested in clustering the two categories (Normal and Abnormal) for the given connection records. Practices experiments result in intrude detection rate (99.183818%) for EPSO and intrude detection rate (69.446416%) for BP neural network.

Keywords: IDS, SI, BP, NSL_KDD, PSO

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Authors: Bruno Biagianti, Angela Tseng, Kathy Wannaviroj, Allison Corlett, Megan DuBois, Kyu Lee, Suma Jacob

Abstract:

Background: ASD is characterized by pervasive Sensory Processing Abnormalities (SPA) and social cognitive deficits that persist throughout the course of the illness and have been linked to functional abnormalities in specific neural systems that underlie the perception, processing, and representation of sensory information. SPA and social cognitive deficits are associated with difficulties in interpersonal relationships, poor development of social skills, reduced social interactions and lower academic performance. Importantly, they can hamper the effects of established evidence-based psychological treatments—including PEERS (Program for the Education and Enrichment of Relationship Skills), a parent/caregiver-assisted, 16-weeks social skills intervention—which nonetheless requires a functional brain capable of assimilating and retaining information and skills. As a matter of fact, some adolescents benefit from PEERS more than others, calling for strategies to increase treatment response rates. Objective: We will present interim data on CICADAS (Care Improving Cognition for ADolescents on the Autism Spectrum)—a clinician-assisted, closed-loop technology mobile application for adolescents with ASD. Via ten mobile assessments, CICADAS captures data on sensory processing abnormalities and associated cognitive deficits. These data populate a machine learning algorithm that tailors the delivery of ten neuroplasticity-based social cognitive training (NB-SCT) exercises targeting sensory processing abnormalities. Methods: In collaboration with the Autism Spectrum and Neurodevelopmental Disorders Clinic at the University of Minnesota, we conducted a fully remote, three-arm, randomized crossover trial with adolescents with ASD to document the acceptability of CICADAS and evaluate its potential as a stand-alone treatment or as a treatment enhancer of PEERS. Twenty-four adolescents with ASD (ages 11-18) have been initially randomized to 16 weeks of PEERS + CICADAS (Arm A) vs. 16 weeks of PEERS + computer games vs. 16 weeks of CICADAS alone (Arm C). After 16 weeks, the full battery of assessments has been remotely administered. Results: We have evaluated the acceptability of CICADAS by examining adherence rates, engagement patterns, and exit survey data. We found that: 1) CICADAS is able to serve as a treatment enhancer for PEERS, inducing greater improvements in sensory processing, cognition, symptom reduction, social skills and behaviors, as well as the quality of life compared to computer games; 2) the concurrent delivery of PEERS and CICADAS induces greater improvements in study outcomes compared to CICADAS only. Conclusion: While preliminary, our results indicate that the individualized assessment and treatment approach designed in CICADAS seems effective in inducing adaptive long-term learning about social-emotional events. CICADAS-induced enhancement of processing and cognition facilitates the application of PEERS skills in the environment of adolescents with ASD, thus improving their real-world functioning.

Keywords: ASD, social skills, cognitive training, mobile app

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Authors: Yoonsuh Jung, Steven N. MacEachern

Abstract:

Check loss function is used to define quantile regression. In the prospect of cross validation, it is also employed as a validation function when underlying truth is unknown. However, our empirical study indicates that the validation with check loss often leads to choosing an over estimated fits. In this work, we suggest a modified or L2-adjusted check loss which rounds the sharp corner in the middle of check loss. It has a large effect of guarding against over fitted model in some extent. Through various simulation settings of linear and non-linear regressions, the improvement of check loss by L2 adjustment is empirically examined. This adjustment is devised to shrink to zero as sample size grows.

Keywords: cross-validation, model selection, quantile regression, tuning parameter selection

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Authors: Rabbia Tariq, Hannan Sajjad, Mehreen Sirshar

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Design patterns are widely used to make the process of development easier as they greatly help the developers to develop the software. Different design patterns have been introduced till now but the behavior of same design pattern may differ in different domains that can lead to the wrong selection of the design pattern. The paper aims to discover the design patterns that suits best with respect to their domain thereby helping the developers to choose an effective design pattern. It presents the comprehensive analysis of design patterns based on different methodologies that include simulation, case study and comparison of various algorithms. Due to the difference of the domain the methodology used in one domain may be inapplicable to the other domain. The paper draws a conclusion based on strength and limitation of each design pattern in their respective domain.

Keywords: design patterns, evaluation, quality assurance, software domains

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Authors: Sandra Mendes

Abstract:

Social Work has encountered over time with multivariate requests in the field of its action, provisioning frameworks of knowledge and praxis. Over the years, we have observed a transformation of society and, consequently, of the public who deals with the social work practitioners. Both, training and profession have had need to adapt and readapt the ways of doing, bailing up theories to action, while action unfolds emancipation of new theories. The theoretical questioning of this subject lies on classical authors from social sciences, and contemporary authors of Social Work. In fact, both enhance, in the design of social work, an integration and social cohesion function, creating a culture of action and theory, attributing to its method a relevant function, which shall be promoter of social changes in various dimensions of both individual and collective life, as well as scientific knowledge. On the other hand, it is assumed that Social Work, through its professionalism and through the academy, is now closer to distinguish itself from other Social Sciences as an autonomous scientific field, being, however, in the center of power struggles. This paper seeks to fill the gap in social work literature about the study of the scientific field of this area of knowledge.

Keywords: field theory, knowledge, science, social work

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Authors: Omran Alharbi, Victor Lally

Abstract:

The advantages of the adoption of new technology such as learning management systems (LMSs) in education and teaching methods have been widely recognised. This has led a large number of universities to integrate this type of technology into their daily learning and teaching activities in order to facilitate the education process for both learners and teachers. On the other hand, in some developing countries such as Saudi Arabia, educators have seldom used this technology. As a result, this study was conducted in order to investigate the factors that impede teachers’ use of technology (LMSs) in their teaching in Saudi Arabian institutions. This study used a qualitative approach. Eight participants were invited to take part in this study, and they were asked to give their opinions about the most significant factors that prevented them from integrating technology into their daily activities. The results revealed that a lack of LMS skills, interest in and knowledge about the LMS among teachers were the most significant factors impeding them from using technology in their lessons. The participants suggested that incentive training should be provided to reduce these challenges.

Keywords: LMS, factors, KSA, teachers

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Authors: Merve Begum Terzi, Orhan Arikan, Adnan Abaci, Mustafa Candemir

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Acute myocardial infarction is a major cause of death in the world. Therefore, its fast and reliable diagnosis is a major clinical need. ECG is the most important diagnostic methodology which is used to make decisions about the management of the cardiovascular diseases. In patients with acute myocardial ischemia, temporary chest pains together with changes in ST segment and T wave of ECG occur shortly before the start of myocardial infarction. In this study, a technique which detects changes in ST/T sections of ECG is developed for the early diagnosis of acute myocardial ischemia. For this purpose, a database of real ECG recordings that contains a set of records from 75 patients presenting symptoms of chest pain who underwent elective percutaneous coronary intervention (PCI) is constituted. 12-lead ECG’s of the patients were recorded before and during the PCI procedure. Two ECG epochs, which are the pre-inflation ECG which is acquired before any catheter insertion and the occlusion ECG which is acquired during balloon inflation, are analyzed for each patient. By using pre-inflation and occlusion recordings, ECG features that are critical in the detection of acute myocardial ischemia are identified and the most discriminative features for the detection of acute myocardial ischemia are extracted. A classification technique based on support vector machine (SVM) approach operating with linear and radial basis function (RBF) kernels to detect ischemic events by using ST-T derived joint features from non-ischemic and ischemic states of the patients is developed. The dataset is randomly divided into training and testing sets and the training set is used to optimize SVM hyperparameters by using grid-search method and 10fold cross-validation. SVMs are designed specifically for each patient by tuning the kernel parameters in order to obtain the optimal classification performance results. As a result of implementing the developed classification technique to real ECG recordings, it is shown that the proposed technique provides highly reliable detections of the anomalies in ECG signals. Furthermore, to develop a detection technique that can be used in the absence of ECG recording obtained during healthy stage, the detection of acute myocardial ischemia based on ECG recordings of the patients obtained during ischemia is also investigated. For this purpose, a Gaussian mixture model (GMM) is used to represent the joint pdf of the most discriminating ECG features of myocardial ischemia. Then, a Neyman-Pearson type of approach is developed to provide detection of outliers that would correspond to acute myocardial ischemia. Neyman – Pearson decision strategy is used by computing the average log likelihood values of ECG segments and comparing them with a range of different threshold values. For different discrimination threshold values and number of ECG segments, probability of detection and probability of false alarm values are computed, and the corresponding ROC curves are obtained. The results indicate that increasing number of ECG segments provide higher performance for GMM based classification. Moreover, the comparison between the performances of SVM and GMM based classification showed that SVM provides higher classification performance results over ECG recordings of considerable number of patients.

Keywords: ECG classification, Gaussian mixture model, Neyman–Pearson approach, support vector machine

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Authors: Ping-Ben Liu, Chien-Chou Tseng

Abstract:

The objective of this study is to investigate the relationship between the lift coefficient and dynamic behaviors of cavitating flow around a two-dimensional Clark Y hydrofoil at 8° angle of attack, cavitation number of 0.8, and Reynolds number of 7.10⁵. The flow field is investigated numerically by using a vapor transfer equation and a modified turbulence model which applies the filter and local density correction. The results including time-averaged lift/drag coefficient and shedding frequency agree well with experimental observations, which confirmed the reliability of this simulation. According to the variation of lift coefficient, the cycle which consists of growth and shedding of cavitation can be divided into three stages, and the lift coefficient at each stage behaves similarly due to the formation and shedding of the cavity around the trailing edge.

Keywords: Computational Fluid Dynamics, cavitation, turbulence, lift coefficient

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Authors: Zhao Gao, Eran Edirisinghe

Abstract:

The production of an accurate sketch of a suspect based on a verbal description obtained from a witness is an essential task for most criminal investigations. The criminal investigation system employs specifically trained professional artists to manually draw a facial image of the suspect according to the descriptions of an eyewitness for subsequent identification. Within the advancement of Deep Learning, Recurrent Neural Networks (RNN) have shown great promise in Natural Language Processing (NLP) tasks. Additionally, Generative Adversarial Networks (GAN) have also proven to be very effective in image generation. In this study, a trained GAN conditioned on textual features such as keywords automatically encoded from a verbal description of a human face using an RNN is used to generate photo-realistic facial images for criminal investigations. The intention of the proposed system is to map corresponding features into text generated from verbal descriptions. With this, it becomes possible to generate many reasonably accurate alternatives to which the witness can use to hopefully identify a suspect from. This reduces subjectivity in decision making both by the eyewitness and the artist while giving an opportunity for the witness to evaluate and reconsider decisions. Furthermore, the proposed approach benefits law enforcement agencies by reducing the time taken to physically draw each potential sketch, thus increasing response times and mitigating potentially malicious human intervention. With publically available 'CelebFaces Attributes Dataset' (CelebA) and additionally providing verbal description as training data, the proposed architecture is able to effectively produce facial structures from given text. Word Embeddings are learnt by applying the RNN architecture in order to perform semantic parsing, the output of which is fed into the GAN for synthesizing photo-realistic images. Rather than the grid search method, a metaheuristic search based on genetic algorithms is applied to evolve the network with the intent of achieving optimal hyperparameters in a fraction the time of a typical brute force approach. With the exception of the ‘CelebA’ training database, further novel test cases are supplied to the network for evaluation. Witness reports detailing criminals from Interpol or other law enforcement agencies are sampled on the network. Using the descriptions provided, samples are generated and compared with the ground truth images of a criminal in order to calculate the similarities. Two factors are used for performance evaluation: The Structural Similarity Index (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). A high percentile output from this performance matrix should attribute to demonstrating the accuracy, in hope of proving that the proposed approach can be an effective tool for law enforcement agencies. The proposed approach to criminal facial image generation has potential to increase the ratio of criminal cases that can be ultimately resolved using eyewitness information gathering.

Keywords: RNN, GAN, NLP, facial composition, criminal investigation

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Authors: Abdelghafour Galadi

Abstract:

In this paper, accurate power MOSFET models including quasi-saturation effect are presented. These models have no internal node voltages determined by the circuit simulator and use one JFET or one depletion mode MOSFET transistors controlled by an “effective” gate voltage taking into account the quasi-saturation effect. The proposed models achieve accurate simulation results with an average error percentage less than 9%, which is an improvement of 21 percentage points compared to the commonly used standard power MOSFET model. In addition, the models can be integrated in any available commercial circuit simulators by using their analytical equations. A description of the models will be provided along with the parameter extraction procedure.

Keywords: power MOSFET, drift layer, quasi-saturation effect, SPICE model

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Authors: Hamed Alipour-Banaei, Farhad Mehdizadeh

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We report the design and characterization of ultra high quality factor filter based on one-dimensional photonic-crystal Thue-Morse sequence structure. The behavior of aperiodic array of photonic crystal structure is numerically investigated and we show that by changing the angle of incident wave, desired wavelengths could be tuned and a tunable filter is realized. Also it is shown that high quality factor filter be achieved in the telecommunication window around 1550 nm, with a device based on Thue-Morse structure. Simulation results show that the proposed structure has a quality factor more than 100000 and it is suitable for DWDM communication applications.

Keywords: Thue-Morse, filter, quality factor, photonic

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Authors: Dominika Collett

Abstract:

AI applications, such as machine learning, require access to a vast amount of data in the training phase, which can often be the subject of copyright protection. During later usage, the various content with which the application works can be recorded or made available on the basis of which it produces the resulting output. The EU has recently adopted new legislation to secure machine access to protected works under the DSM Directive; but, the issue of machine use of copyright works is not clearly addressed. However, such clarity is needed regarding the increasing importance of AI and its development. Therefore, this paper provides a basic background of the technology used in the development of applications in the field of computer creativity. The second part of the paper then will focus on a legal analysis of machine use of the authors' works from the perspective of existing European and Czech legislation. The main results of the paper discuss the potential collision of existing legislation in regards to machine use of works with special focus on exceptions and limitations. The legal regulation of machine use of copyright work will impact the development of AI technology.

Keywords: copyright, artificial intelligence, legal use, infringement, Czech law, EU law, text and data mining

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Authors: Abdelbasit Gadour

Abstract:

A great number of children in mainstream schools across Libya are currently living with emotional, behavioral difficulties. This study aims to explore teachers’ perceptions of children’s emotional and behavioral difficulties (EBD) and their attributions of the causes of EBD. The relevance of this area of study to current educational practice is illustrated in the fact that primary school teachers in Libya find classroom behavior problems one of the major difficulties they face. The information presented in this study was gathered from 182 teachers that responded back to the survey, of whom 27 teachers were later interviewed. In general, teachers’ perceptions of EBD reflect personal experience, training, and attitudes. Teachers appear from this study to use words such as indifferent, frightened, withdrawn, aggressive, disobedient, hyperactive, less ambitious, lacking concentration, and academically weak to describe pupils with emotional and behavioral difficulties (EBD). The implications of this study are envisaged as being extremely important to support teachers addressing children’s EBD and shed light on the contributing factors to EBD for a successful teaching-learning process in Libyan primary schools.

Keywords: children, emotional and behavior difficulties, learning, teachers'

Procedia PDF Downloads 138

Authors: Radesh Palakurthi

Abstract:

The purpose of this study was to determine the impact of experiential learning on learning outcomes in hospitality education. This paper presents the results of an online survey of students from the U.S. studying abroad and their self-reported change in learning outcomes as assessed using the Core Competencies Model for the Hospitality Industry developed by Employment and Training Development Office of the U.S. Department of Labor. The impact of student learning styles on learning outcomes is also evaluated in this study. Kolb’s Learning Styles Inventory Model was used to assess students’ learning style. The results show that students reported significant improvements in their learning outcomes because of engaging in study abroad experiential learning programs. The learning styles of the students had significant effect on one of core learning outcomes- personal effectiveness.

Keywords: hospitality competencies, hospitality education, Kolb’s learning style inventory, learning outcomes, study abroad

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Authors: Pieter van den Berg, Retief Broodryk, Bert Moolman

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Firstly, this study aimed to identify which performance indicators (PIs) discriminate between winning and losing university-level Rugby Union (RU) teams and, secondly, to compare the significant PIs in RU and Rugby Union Sevens (RS) at university level. Understanding the importance of PIs and their effect on match outcomes could assist coaching staff to prioritise specific game aspects during training to increase performance. Twenty randomly selected round-robin matches of the 2018 Varsity Cup (n=20), and Varsity Sports sevens (n=20) tournaments were analysed. A linear mixed model was used to determine statistical significant differences set at p≤0.05 while effect size was reported according to Cohen's d value. Results revealed that various PIs discriminated between winning and losing RU teams and that specific PIs could be observed as significant in both RU and RS. Therefore, specific identified tactical aspects of RU and RS should be prioritised to optimise performance

Keywords: match success, notational analysis, performance analysis, rugby, video analysis

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