Search results for: 99.95% IoT data transmission savings

Authors: Rose A. Yemson, Ping Jiang, Oyedeji L. Inumoh

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The deployment of wireless sensor network has rapidly increased, however with the increased capacity and diversity of sensors, and applications ranging from biological, environmental, military etc. generates tremendous volume of data’s where more attention is placed on the distributed sensing and little on how to manage, analyze, retrieve and understand the data generated. This makes it more quite difficult to process live sensor data, run concurrent control and update because sensor data are either heavyweight, complex, and slow. This work will focus on developing a web service platform for automatic detection of sensors, acquisition of sensor data, storage of sensor data into a database, processing of sensor data using reconfigurable software components. This work will also create a web service based sensor data management system to monitor physical movement of an individual wearing wireless network sensor technology (SunSPOT). The sensor will detect movement of that individual by sensing the acceleration in the direction of X, Y and Z axes accordingly and then send the sensed reading to a database that will be interfaced with an internet platform. The collected sensed data will determine the posture of the person such as standing, sitting and lying down. The system is designed using the Unified Modeling Language (UML) and implemented using Java, JavaScript, html and MySQL. This system allows real time monitoring an individual closely and obtain their physical activity details without been physically presence for in-situ measurement which enables you to work remotely instead of the time consuming check of an individual. These details can help in evaluating an individual’s physical activity and generate feedback on medication. It can also help in keeping track of any mandatory physical activities required to be done by the individuals. These evaluations and feedback can help in maintaining a better health status of the individual and providing improved health care.

Keywords: HTML, java, javascript, MySQL, sunspot, UML, web-based, wireless network sensor

Procedia PDF Downloads 212

Authors: Rejab Hajlaoui, Tarek Moulahi, Hervé Guyennet

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In a highway scenario, the vehicle speed can exceed 120 kmph. Therefore, any vehicle can enter or leave the network within a very short time. This mobility adversely affects the network connectivity and decreases the life time of all established links. To ensure an effective stability in vehicular ad hoc networks with minimum broadcasting storm, we have developed a weighted algorithm based on the k-medoids clustering algorithm (WKCA). Indeed, the number of clusters and the initial cluster heads will not be selected randomly as usual, but considering the available transmission range and the environment size. Then, to ensure optimal assignment of nodes to clusters in both k-medoids phases, the combined weight of any node will be computed according to additional metrics including direction, relative speed and proximity. Empirical results prove that in addition to the convergence speed that characterizes the k-medoids algorithm, our proposed model performs well both AODV-Clustering and OLSR-Clustering protocols under different densities and velocities in term of end-to-end delay, packet delivery ratio, and throughput.

Keywords: communication, clustering algorithm, k-medoids, sensor, vehicular ad hoc network

Procedia PDF Downloads 239

Authors: Valentina Marutyan

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Healthcare data mining is a rapidly developing field at the intersection of technology and medicine that has the potential to change our understanding and approach to providing healthcare. Healthcare and data mining is the process of examining huge amounts of data to extract useful information that can be applied in order to improve patient care, treatment effectiveness, and overall healthcare delivery. This field looks for patterns, trends, and correlations in a variety of healthcare datasets, such as electronic health records (EHRs), medical imaging, patient demographics, and treatment histories. To accomplish this, it uses advanced analytical approaches. Predictive analysis using historical patient data is a major area of interest in healthcare data mining. This enables doctors to get involved early to prevent problems or improve results for patients. It also assists in early disease detection and customized treatment planning for every person. Doctors can customize a patient's care by looking at their medical history, genetic profile, current and previous therapies. In this way, treatments can be more effective and have fewer negative consequences. Moreover, helping patients, it improves the efficiency of hospitals. It helps them determine the number of beds or doctors they require in regard to the number of patients they expect. In this project are used models like logistic regression, random forests, and neural networks for predicting diseases and analyzing medical images. Patients were helped by algorithms such as k-means, and connections between treatments and patient responses were identified by association rule mining. Time series techniques helped in resource management by predicting patient admissions. These methods improved healthcare decision-making and personalized treatment. Also, healthcare data mining must deal with difficulties such as bad data quality, privacy challenges, managing large and complicated datasets, ensuring the reliability of models, managing biases, limited data sharing, and regulatory compliance. Finally, secret code of data mining in healthcare helps medical professionals and hospitals make better decisions, treat patients more efficiently, and work more efficiently. It ultimately comes down to using data to improve treatment, make better choices, and simplify hospital operations for all patients.

Keywords: data mining, healthcare, big data, large amounts of data

Procedia PDF Downloads 76

Authors: Hossein Mardani, Neil Buchanan, Robert Cahill, Vincent Fusco

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In this paper, we study the input impedance characteristics of axial mode helical antennas to find an effective way for matching it to 50 Ω. The study is done on the important matching parameters such as like wire diameter and helix to the ground plane gap. It is intended that these parameters control the matching without detrimentally affecting the radiation pattern. Using transmission line theory, a simple broadband technique is proposed, which is applicable for perfect matching of antennas with similar design parameters. We provide design curves to help to choose the proper dimensions of the matching section based on the antenna’s unmatched input impedance. Finally, using the proposed technique, a 4-turn axial mode helix is designed at 2.5 GHz center frequency and the measurement results of the manufactured antenna will be included. This parametric study gives a good insight into the input impedance characteristics of axial mode helical antennas and the proposed impedance matching approach provides a simple, useful method for matching these types of antennas.

Keywords: antenna, helix, helical, axial mode, wireless power transfer, impedance matching

Procedia PDF Downloads 312

Authors: Bijit Kalita, K. V. N. Surendra

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The pulley works under both compressive loading due to contacting belt in tension and central torque due to cause rotation. In a power transmission system, the belt pulley assemblies offer a contact problem in the form of two mating cylindrical parts. In this work, we modeled a pulley as a heavy two-dimensional circular disk. Stress analysis due to contact loading in the pulley mechanism is performed. Finite element analysis (FEA) is conducted for a pulley to investigate the stresses experienced on its inner and outer periphery. In most of the heavy-duty applications, most frequently used mechanisms to transmit power in applications such as automotive engines, industrial machines, etc. is Belt Drive. Usually, very heavy circular disks are used as pulleys. A pulley could be entitled as a drum and may have a groove between two flanges around the circumference. A rope, belt, cable or chain can be the driving element of a pulley system that runs over the pulley inside the groove. A pulley is experienced by normal and shear tractions on its contact region in the process of motion transmission. The region may be belt-pulley contact surface or pulley-shaft contact surface. In 1895, Hertz solved the elastic contact problem for point contact and line contact of an ideal smooth object. Afterward, this hypothesis is generally utilized for computing the actual contact zone. Detailed stress analysis in such contact region of such pulleys is quite necessary to prevent early failure. In this paper, the results of the finite element analyses carried out on the compressed disk of a belt pulley arrangement using fracture mechanics concepts are shown. Based on the literature on contact stress problem induced in the wide field of applications, generated stress distribution on the shaft-pulley and belt-pulley interfaces due to the application of high-tension and torque was evaluated in this study using FEA concepts. Finally, the results obtained from ANSYS (APDL) were compared with the Hertzian contact theory. The study is mainly focused on the fatigue life estimation of a rotating part as a component of an engine assembly using the most famous Paris equation. Digital Image Correlation (DIC) analyses have been performed using the open-source software. From the displacement computed using the images acquired at a minimum and maximum force, displacement field amplitude is computed. From these fields, the crack path is defined and stress intensity factors and crack tip position are extracted. A non-linear least-squares projection is used for the purpose of the estimation of fatigue crack growth. Further study will be extended for the various application of rotating machinery such as rotating flywheel disk, jet engine, compressor disk, roller disk cutter etc., where Stress Intensity Factor (SIF) calculation plays a significant role on the accuracy and reliability of a safe design. Additionally, this study will be progressed to predict crack propagation in the pulley using maximum tangential stress (MTS) criteria for mixed mode fracture.

Keywords: crack-tip deformations, contact stress, stress concentration, stress intensity factor

Procedia PDF Downloads 124

Authors: Bushra Zafar, Usman Qamar

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Large data sample size and dimensions render the effectiveness of conventional data mining methodologies. A data mining technique are important tools for collection of knowledgeable information from variety of databases and provides supervised learning in the form of classification to design models to describe vital data classes while structure of the classifier is based on class attribute. Classification efficiency and accuracy are often influenced to great extent by noisy and undesirable features in real application data sets. The inherent natures of data set greatly masks its quality analysis and leave us with quite few practical approaches to use. To our knowledge first time, we present a new approach for investigation of structure and quality of datasets by providing a targeted analysis of localization of noisy and irrelevant features of data sets. Machine learning is based primarily on feature selection as pre-processing step which offers us to select few features from number of features as a subset by reducing the space according to certain evaluation criterion. The primary objective of this study is to trim down the scope of the given data sample by searching a small set of important features which may results into good classification performance. For this purpose, a heuristic for wrapper-based feature selection using genetic algorithm and for discriminative feature selection an external classifier are used. Selection of feature based on its number of occurrence in the chosen chromosomes. Sample dataset has been used to demonstrate proposed idea effectively. A proposed method has improved average accuracy of different datasets is about 95%. Experimental results illustrate that proposed algorithm increases the accuracy of prediction of different diseases.

Keywords: data mining, generic algorithm, KNN algorithms, wrapper based feature selection

Procedia PDF Downloads 316

Authors: Meity Sompie, Agnes Triasih, Wisje Ponto

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Edible coating is thin layers that act as a barrier to the external factors and protect the food products. The addition of the plasticizer to the edible coating is required to overcome film caused by extensive intermolecular forces. The potential development of pigskin with different ages as a raw material for the manufacture of edible films had not been widely publicized. This research was aimed to determine the influence of gelatin concentration and different type of plasticizer on the edible coating characteristics extracted from pigskin gelatin. This study used Completely Randomized Design (CRD) with two factors and three replicates of treatments. The first factor was consisted of pigskin gelatin concentration ( 10, 20, and 30 %) and the second factor was different type of plasticizer (glycerol, sorbitol and PEG). The results show that the interaction between the use of gelatin concentrations and type of plasticizer had significant effect (P< 0.05) on the thickness, tensile strength, elongation, water vapor transmission rate (WVTR), water content and amino acid profile of edible coating. It was concluded that the edible coating from pigskin gelatin with plasticizer gliserol had the best film characteristics, and it can be applied as an edible coating.

Keywords: edible coating, edible film, pigskin gelatin, plasticizer

Procedia PDF Downloads 215

Authors: Wade Ghribi, Abdelmoty M. Ahmed, Ahmed Said Badawy, Belgacem Bouallegue

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In higher education institutions, the most pressing priority is to improve student performance and retention. Large volumes of student data are used in Educational Data Mining techniques to find new hidden information from students' learning behavior, particularly to uncover the early symptom of at-risk pupils. On the other hand, data with noise, outliers, and irrelevant information may provide incorrect conclusions. By identifying features of students' data that have the potential to improve performance prediction results, comparing and identifying the most appropriate ensemble learning technique after preprocessing the data, and optimizing the hyperparameters, this paper aims to develop a reliable students' performance prediction model for Higher Education Institutions. Data was gathered from two different systems: a student information system and an e-learning system for undergraduate students in the College of Computer Science of a Saudi Arabian State University. The cases of 4413 students were used in this article. The process includes data collection, data integration, data preprocessing (such as cleaning, normalization, and transformation), feature selection, pattern extraction, and, finally, model optimization and assessment. Random Forest, Bagging, Stacking, Majority Vote, and two types of Boosting techniques, AdaBoost and XGBoost, are ensemble learning approaches, whereas Decision Tree, Support Vector Machine, and Artificial Neural Network are supervised learning techniques. Hyperparameters for ensemble learning systems will be fine-tuned to provide enhanced performance and optimal output. The findings imply that combining features of students' behavior from e-learning and students' information systems using Majority Vote produced better outcomes than the other ensemble techniques.

Keywords: educational data mining, student performance prediction, e-learning, classification, ensemble learning, higher education

Procedia PDF Downloads 108

Authors: Hae-Won Seo, Yong-Gu Lee

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Recent progress in the next generation of automobile technology is geared towards incorporating information technology into cars. Collectively called smart cars are bringing intelligence to cars that provides comfort, convenience and safety. A branch of smart cars is connected-car system. The key concept in connected-cars is the sharing of driving information among cars through decentralized manner enabling collective intelligence. This paper proposes a foundation of the information model that is necessary to define the driving information for smart-cars. Road conditions are modeled through a unique data structure that unambiguously represent the time variant traffics in the streets. Additionally, the modeled data structure is exemplified in a navigational scenario and usage using UML. Optimal driving route searching is also discussed using the proposed data structure in a dynamically changing road conditions.

Keywords: connected-car, data modeling, route planning, navigation system

Procedia PDF Downloads 374

Authors: Abdelghani Choucha, Lakhdar Chaib, Salem Arif

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This paper treated the problem of power system stability with the aid of Static Synchronous Series Compensator (SSSC) installed in the transmission line of single machine infinite bus (SMIB) power system. A fractional order PID (FOPID) controller has been applied as a robust controller for optimal SSSC design to control the power system characteristics. Additionally, the SSSC based FOPID parameters are smoothly tuned using Invasive Weed Optimization algorithm (IWO). To verify the strength of the proposed controller, SSSC based FOPID controller is validated in a wide range of operating condition and compared with the conventional scheme SSSC-POD controller. The main purpose of the proposed process is greatly enhanced the dynamic states of the tested system. Simulation results clearly prove the superiority and performance of the proposed controller design.

Keywords: SSSC-FOPID, SSSC-POD, SMIB power system, invasive weed optimization algorithm

Procedia PDF Downloads 188

Authors: Munnujahan Ara

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In this paper, we investigate the effect of friendly jamming power allocation strategies on the achievable average secrecy rate over a bank of parallel fading wiretap channels. We investigate the achievable average secrecy rate in parallel fading wiretap channels subject to Rayleigh and Rician fading. The achievable average secrecy rate, due to the presence of a line-of-sight component in the jammer channel is also evaluated. Moreover, we study the detrimental effect of correlation across the parallel sub-channels, and evaluate the corresponding decrease in the achievable average secrecy rate for the various fading configurations. We also investigate the tradeoff between the transmission power and the jamming power for a fixed total power budget. Our results, which are applicable to current orthogonal frequency division multiplexing (OFDM) communications systems, shed further light on the achievable average secrecy rates over a bank of parallel fading channels in the presence of friendly jammers.

Keywords: fading parallel channels, wire-tap channel, OFDM, secrecy capacity, power allocation

Procedia PDF Downloads 512

Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

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Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction

Procedia PDF Downloads 340

Authors: Georgii Emelianov, Mikhail Polikarpov, Fabian Hübner, Jochen Deuse, Jochen Schiemann

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Recycling refrigerating appliances plays a major role in protecting the Earth's atmosphere from ozone depletion and emissions of greenhouse gases. The performance of refrigerator recycling plants in terms of material retention is the subject of strict environmental certifications and is reviewed periodically through specialized audits. The continuous collection of Refrigerator data required for the input-output analysis is still mostly manual, error-prone, and not digitalized. In this paper, we propose an automated data collection system for recycling plants in order to deduce expected material contents in individual end-of-life refrigerating appliances. The system utilizes laser scanner measurements and optical data to extract attributes of individual refrigerators by applying transfer learning with pre-trained vision models and optical character recognition. Based on Recognized features, the system automatically provides material categories and target values of contained material masses, especially foaming and cooling agents. The presented data collection system paves the way for continuous performance monitoring and efficient control of refrigerator recycling plants.

Keywords: automation, data collection, performance monitoring, recycling, refrigerators

Procedia PDF Downloads 164

Authors: Soojin Kim, Jiwon Yang, Sungzoon Cho

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As a seasonal product is only in demand for a short time, inventory management is critical to profits. Both markdowns and stockouts decrease the return on perishable products; therefore, researchers have been interested in the distribution of seasonal products with the aim of maximizing profits. In this study, we propose a data-driven seasonal product sales pattern analysis method for individual retail outlets based on observed sales data clustering; the proposed method helps in determining distribution strategies.

Keywords: clustering, distribution, sales pattern, seasonal product

Procedia PDF Downloads 597

Authors: Xiao Yihong, Yu Kexin, Burra Venkata Durga Kumar

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With the accelerating process of urbanization, the problem of urban road congestion is becoming more and more serious. Intelligent transportation system combining distributed and artificial intelligence has become a research hotspot. As the core development direction of the intelligent transportation system, Cooperative Intelligent Transportation System (C-ITS) integrates advanced information technology and communication methods and realizes the integration of humans, vehicle, roadside infrastructure, and other elements through the multi-agent distributed system. By analyzing the system architecture and technical characteristics of C-ITS, the report proposes a distributed multi-agent C-ITS. The system consists of Roadside Sub-system, Vehicle Sub-system, and Personal Sub-system. At the same time, we explore the scalability of the C-ITS and put forward incorporating local rewards in the centralized training decentralized execution paradigm, hoping to add a scalable value decomposition method. In addition, we also suggest introducing blockchain to improve the safety of the traffic information transmission process. The system is expected to improve vehicle capacity and traffic safety.

Keywords: distributed system, artificial intelligence, multi-agent, cooperative intelligent transportation system

Procedia PDF Downloads 214

Authors: Damou Mehdi, Nouri Keltoum, Feham Mohammed, Khazini Mohammed, Bouazza Tayb Habibi Chawki

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The Half mode SIW filter is treated by two softwares (HFSS (High Frequency Structure Simulator) and CST (Computer Simulation Technology)). The filter HMSIW has a very simple structure and a very compact size. The simulated results by CST are presented and compared with the results simulated by a high-frequency structure simulator. Good agreement between the simulated CST and simulated results by HFSS is observed. By cascading two of them according to design requirement, a X-band bandpass filter is designed and simulated to meet compact size, low insertion loss, good return loss as well as second harmonic suppression. As an example, we designed the proposed HMSIW filter at X band by HFSS. The filter has a pass-band from 7.3 GHz to 9.8 GHz, and its relative operating fraction bandwidth is 29.5 %. There are one transmission zeros are located at 14.4 GHz.

Keywords: substrate integrated waveguide, filter, HMSIW, defected ground structures (DGS), simulation BPF

Procedia PDF Downloads 587

Authors: Surya R. Niraula, Devendra B Chhetry, Girish K. Singh, S. Nagesh, Frederick A. Connell

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Background: Although random sampling is generally considered to be the gold standard for population-based research, the majority of drug abuse research is based on non-random sampling despite the well-known limitations of this kind of sampling. Method: We compared the statistical properties of two surveys of drug abuse in the same community: one using snowball sampling of drug users who then identified “friend controls” and the other using a random sample of non-drug users (controls) who then identified “friend cases.” Models to predict drug abuse based on risk factors were developed for each data set using conditional logistic regression. We compared the precision of each model using bootstrapping method and the predictive properties of each model using receiver operating characteristics (ROC) curves. Results: Analysis of 100 random bootstrap samples drawn from the snowball-sample data set showed a wide variation in the standard errors of the beta coefficients of the predictive model, none of which achieved statistical significance. One the other hand, bootstrap analysis of the random-sample data set showed less variation, and did not change the significance of the predictors at the 5% level when compared to the non-bootstrap analysis. Comparison of the area under the ROC curves using the model derived from the random-sample data set was similar when fitted to either data set (0.93, for random-sample data vs. 0.91 for snowball-sample data, p=0.35); however, when the model derived from the snowball-sample data set was fitted to each of the data sets, the areas under the curve were significantly different (0.98 vs. 0.83, p < .001). Conclusion: The proposed method of random sampling of controls appears to be superior from a statistical perspective to snowball sampling and may represent a viable alternative to snowball sampling.

Keywords: drug abuse, matched case-control study, non-probability sampling, probability sampling

Procedia PDF Downloads 493

Authors: Maria Belen C. Costa, Liza C. Costa

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Teachers have an important influence on students’ academic achievement. Teachers play a crucial role in educational attainment because they stand in the interface of the transmission of knowledge, values, and skills in the learning process through the instructional strategies they employ in the classroom. The level of achievement of students in school depends on the degree of effectiveness of instructional strategies used by the teacher. Thus, this study was conceptualized and conducted to examine the instructional strategies preferred and used by the Science and Mathematics teachers and the impact of those strategies in their teaching performance and students’ academic achievement in Science and Mathematics. The participants of the study comprised a total enumeration of 61 teachers who were chosen through total enumeration and 610 students who were selected using two-stage random sampling technique. The descriptive correlation design was used in this study with a self-made questionnaire as the main tool in the data gathering procedure. Relationship among variables was tested and analyzed using Spearman Rank Correlation Coefficient and Wilcoxon Signed Rank statistics. The teacher participants under study mainly belonged to the age group of ‘young’ (35 years and below) and most were females having ‘very much experienced’ (16 years and above) in teaching. Teaching performance was found to be ‘very satisfactory’ while academic achievement in Science and Mathematics was found to be ‘satisfactory’. Demographic profile and teaching performance of teacher participants were found to be ‘not significant’ to their instructional strategy preferences. Results implied that age, sex, level of education and length of service of the teachers does not affect their preference on a particular instructional strategy. However, the teacher participants’ extent of use of the different instructional strategies was found to be ‘significant’ to their teaching performance. The instructional strategies being used by the teachers were found to have a direct effect on their teaching performance. Academic achievement of student participants was found to be ‘significant’ to the teacher participants’ instructional strategy preferences. The preference of the teachers on instructional strategies had a significant effect on the students’ academic performance. On the other hand, teacher participants’ extent of use of instructional strategies was showed to be ‘not significant’ to the academic achievement of students in Science and Mathematics. The instructional strategy being used by the teachers did not affect the level of performance of students in Science and Mathematics. The results of the study revealed that there was a significant difference between the teacher participants’ preference of instructional strategy and the student participants’ instructional strategy preference as well as between teacher participants’ extent of use and student participants’ perceived level of use of the different instructional strategies. Findings found a discrepancy between the teaching strategy preferences of students and strategies implemented by teachers.

Keywords: academic achievement, extent of use, instructional strategy, preferences

Procedia PDF Downloads 313

Authors: Javed Mohammed

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Right now, bio-medical infrastructure lags well behind the curve. Our healthcare system is dispersed and disjointed; medical records are a bit of a mess; and we do not yet have the capacity to store and process the crazy amounts of data coming our way from widespread whole-genome sequencing. And then there are privacy issues. Despite these infrastructure challenges, some researchers are plunging into bio medical Big Data now, in hopes of extracting new and actionable knowledge. They are doing delving into molecular-level data to discover bio markers that help classify patients based on their response to existing treatments; and pushing their results out to physicians in novel and creative ways. Computer scientists and bio medical researchers are able to transform data into models and simulations that will enable scientists for the first time to gain a profound under-standing of the deepest biological functions. Solving biological problems may require High-Performance Computing HPC due either to the massive parallel computation required to solve a particular problem or to algorithmic complexity that may range from difficult to intractable. Many problems involve seemingly well-behaved polynomial time algorithms (such as all-to-all comparisons) but have massive computational requirements due to the large data sets that must be analyzed. High-throughput techniques for DNA sequencing and analysis of gene expression have led to exponential growth in the amount of publicly available genomic data. With the increased availability of genomic data traditional database approaches are no longer sufficient for rapidly performing life science queries involving the fusion of data types. Computing systems are now so powerful it is possible for researchers to consider modeling the folding of a protein or even the simulation of an entire human body. This research paper emphasizes the computational biology's growing need for high-performance computing and Big Data. It illustrates this article’s indispensability in meeting the scientific and engineering challenges of the twenty-first century, and how Protein Folding (the structure and function of proteins) and Phylogeny Reconstruction (evolutionary history of a group of genes) can use HPC that provides sufficient capability for evaluating or solving more limited but meaningful instances. This article also indicates solutions to optimization problems, and benefits Big Data and Computational Biology. The article illustrates the Current State-of-the-Art and Future-Generation Biology of HPC Computing with Big Data.

Keywords: high performance, big data, parallel computation, molecular data, computational biology

Procedia PDF Downloads 364

Authors: Sreto Boljevic

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Heating systems based on geothermal energy sources are becoming increasingly popular among commercial/community buildings as management of these buildings looks for a more efficient and environmentally friendly way to manage the heating system. The thermal energy supply of most European commercial/community buildings at present is provided mainly by energy extracted from natural gas. In order to reduce greenhouse gas emissions and achieve climate change targets set by the EU, restructuring in the area of thermal energy supply is essential. At present, heating and cooling account for approx... 50% of the EU primary energy supply. Due to its physical characteristics, thermal energy cannot be distributed or exchange over long distances, contrary to electricity and gas energy carriers. Compared to electricity and the gas sectors, heating remains a generally black box, with large unknowns to a researcher and policymaker. Ain literature number of documents address policies for promoting renewable energy technology to facilitate heating for residential/community/commercial buildings and assess the balance between heat supply and heat savings. Ground source heat pump (GSHP) technology has been an extremely attractive alternative to traditional electric and fossil fuel space heating equipment used to supply thermal energy for residential/community/commercial buildings. The main purpose of this paper is to create an algorithm using an analytical approach that could enable a feasibility study regarding the implementation of GSHP technology in community building with existing fossil-fueled heating systems. The main results obtained by the algorithm will enable building management and GSHP system designers to define the optimal size of the system regarding technical, environmental, and economic impacts of the system implementation, including payback period time. In addition, an algorithm is created to be utilized for a feasibility study for many different types of buildings. The algorithm is tested on a building that was built in 1930 and is used as a church located in Cork city. The heating of the building is currently provided by a 105kW gas boiler.

Keywords: GSHP, greenhouse gas emission, low-enthalpy, renewable energy

Procedia PDF Downloads 220

Authors: A. S. V Polgampala, F. Huang

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This is an overview of what is entailed in an evaluation and issues to be aware of when class observation is being done. This study examined the effects of evaluating teaching practice of a 7-day ‘block teaching’ session in a pre -service science teacher training program at a reputed National College of Education in Sri Lanka. Effects were assessed in three areas: evaluation of the training process, evaluation of the training impact, and evaluation of the training procedure. Data for this study were collected by class observation of 18 teachers during 9th February to 16th of 2017. Prospective teachers of science teaching, the participants of the study were evaluated based on newly introduced format by the NIE. The data collected was analyzed qualitatively using the Miles and Huberman procedure for analyzing qualitative data: data reduction, data display and conclusion drawing/verification. It was observed that the trainees showed their confidence in teaching those competencies and skills. Teacher educators’ dissatisfaction has been a great impact on evaluation process.

Keywords: evaluation, perceptions & perspectives, pre-service, science teachering

Procedia PDF Downloads 315

Authors: Sukhleen Kaur

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In security groundwork, Intrusion Detection System (IDS) has become an important component. The IDS has received increasing attention in recent years. IDS is one of the effective way to detect different kinds of attacks and malicious codes in a network and help us to secure the network. Data mining techniques can be implemented to IDS, which analyses the large amount of data and gives better results. Data mining can contribute to improving intrusion detection by adding a level of focus to anomaly detection. So far the study has been carried out on finding the attacks but this paper detects the malicious files. Some intruders do not attack directly, but they hide some harmful code inside the files or may corrupt those file and attack the system. These files are detected according to some defined parameters which will form two lists of files as normal files and harmful files. After that data mining will be performed. In this paper a hybrid classifier has been used via Naive Bayes and Ripper classification methods. The results show how the uploaded file in the database will be tested against the parameters and then it is characterised as either normal or harmful file and after that the mining is performed. Moreover, when a user tries to mine on harmful file it will generate an exception that mining cannot be made on corrupted or harmful files.

Keywords: data mining, association, classification, clustering, decision tree, intrusion detection system, misuse detection, anomaly detection, naive Bayes, ripper

Procedia PDF Downloads 414

Authors: Abhijith Asok

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This paper presents a generalized approach for the simple linear data transformation, Y=bX, through an integration of multidimensional coordinate geometry, vector space theory and polygonal geometry. The scaling is performed by adding an additional ’Dummy Dimension’ to the n-dimensional data, which helps plot two dimensional component-wise straight lines on pairs of dimensions. The end result is a set of scaled extensions of observations in any of the 2n spatial divisions, where n is the total number of applicable dimensions/dataset variables, created by shifting the n-dimensional plane along the ’Dummy Axis’. The derived scaling factor was found to be dependent on the coordinates of the common point of origin for diverging straight lines and the plane of extension, chosen on and perpendicular to the ’Dummy Axis’, respectively. This result indicates the geometrical interpretation of a linear data transformation and hence, opportunities for a more informed choice of the factor ’b’, based on a better choice of these coordinate values. The paper follows on to identify the effect of this transformation on certain popular distance metrics, wherein for many, the distance metric retained the same scaling factor as that of the features.

Keywords: data transformation, dummy dimension, linear transformation, scaling

Procedia PDF Downloads 298

Authors: Minna Pikkarainen, Yueqiang Xu

Abstract:

The integration of digital technology with existing healthcare processes has been painfully slow, a huge gap exists between the fields of strictly regulated official medical care and the quickly moving field of health and wellness technology. We claim that the promises of preventive healthcare can only be fulfilled when this gap is closed – health care and self-care becomes seamless continuum “correct information, in the correct hands, at the correct time allowing individuals and professionals to make better decisions” what we call connected health approach. Currently, the issues related to security, privacy, consumer consent and data sharing are hindering the implementation of this new paradigm of healthcare. This could be solved by following MyData principles stating that: Individuals should have the right and practical means to manage their data and privacy. MyData infrastructure enables decentralized management of personal data, improves interoperability, makes it easier for companies to comply with tightening data protection regulations, and allows individuals to change service providers without proprietary data lock-ins. This paper tackles today’s unprecedented challenges of enabling and stimulating multiple healthcare data providers and stakeholders to have more active participation in the digital health ecosystem. First, the paper systematically proposes the MyData approach for healthcare and preventive health data ecosystem. In this research, the work is targeted for health and wellness ecosystems. Each ecosystem consists of key actors, such as 1) individual (citizen or professional controlling/using the services) i.e. data subject, 2) services providing personal data (e.g. startups providing data collection apps or data collection devices), 3) health and wellness services utilizing aforementioned data and 4) services authorizing the access to this data under individual’s provided explicit consent. Second, the research extends the existing four archetypes of orchestrator-driven healthcare data business models for the healthcare industry and proposes the fifth type of healthcare data model, the MyData Blockchain Platform. This new architecture is developed by the Action Design Research approach, which is a prominent research methodology in the information system domain. The key novelty of the paper is to expand the health data value chain architecture and design from centralization and pseudo-decentralization to full decentralization, enabled by blockchain, thus the MyData blockchain platform. The study not only broadens the healthcare informatics literature but also contributes to the theoretical development of digital healthcare and blockchain research domains with a systemic approach.

Keywords: blockchain, health data, platform, action design

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Authors: Yu-Lin Shen, Ming-Kuen Chang

Abstract:

In addition to a considerable amount of machinery and equipment, intricacies of the transmission pipeline exist in Petrochemical plants. Long term corrosion may lead to pipeline thinning and rupture, causing serious safety concerns. With the advances in non-destructive testing technology, more rapid and long-range ultrasonic detection techniques are often used for pipeline inspection, EMAT without coupling to detect, it is a non-contact ultrasonic, suitable for detecting elevated temperature or roughened e surface of line. In this study, we prepared artificial defects in pipeline for Electromagnetic Acoustic Transducer testing (EMAT) to survey the relationship between the defect location, sizing and the EMAT signal. It was found that the signal amplitude of EMAT exhibited greater signal attenuation with larger defect depth and length. In addition, with bigger flat hole diameter, greater amplitude attenuation was obtained. In summary, signal amplitude attenuation of EMAT was affected by the defect depth, defect length and the hole diameter and size.

Keywords: EMAT, artificial defect, NDT, ultrasonic testing

Procedia PDF Downloads 350

Authors: Janet C. Read

Abstract:

UClan set collaboration with Lingokids to assess the Lingokids learning app's impact on learning outcomes in classrooms in the UK for children with ages ranging from 3 to 5 years. Data gathered during the controlled study with 69 children includes attitudinal data, engagement, and learning scores. Data shows that children enjoyment while learning was higher among those children using the game-based app compared to those children using other traditional methods. It’s worth pointing out that engagement when using the learning app was significantly higher than other traditional methods among older children. According to existing literature, there is a direct correlation between engagement, motivation, and learning. Therefore, this study provides relevant data points to conclude that Lingokids learning app serves its purpose of encouraging learning through playful and interactive content. That being said, we believe that learning outcomes should be assessed with a wider range of methods in further studies. Likewise, it would be beneficial to assess the level of usability and playability of the app in order to evaluate the learning app from other angles.

Keywords: learning app, learning outcomes, rapid test activity, Smileyometer, early childhood education, innovative pedagogy

Procedia PDF Downloads 71

Authors: Jatin Kumar Choudhary, Naren Rayala, Abbas Eslami Kiasari, Fahimeh Jafari

Abstract:

The Great Britain has one of the safest road networks in the world. However, the consequences of any death or serious injury are devastating for loved ones, as well as for those who help the severely injured. This paper aims to analyse the Great Britain's road safety situation and show the response measures for areas where the total damage caused by accidents can be significantly and quickly reduced. In this paper, we do an exploratory data analysis using STATS19 data. For the past 30 years, the UK has had a good record in reducing fatalities. The UK ranked third based on the number of road deaths per million inhabitants. There were around 165,000 accidents reported in the Great Britain in 2009 and it has been decreasing every year until 2019 which is under 120,000. The government continues to scale back road deaths empowering responsible road users by identifying and prosecuting the parameters that make the roads less safe.

Keywords: road safety, data analysis, openstreetmap, feature expanding.

Procedia PDF Downloads 140

Authors: Zyad Elkhadir, Khalid Chougdali, Mohammed Benattou

Abstract:

Most of the existing intrusion detection systems works on quantitative network traffic data with many irrelevant and redundant features, which makes detection process more time’s consuming and inaccurate. A several feature extraction methods, such as linear discriminant analysis (LDA), have been proposed. However, LDA suffers from the small sample size (SSS) problem which occurs when the number of the training samples is small compared with the samples dimension. Hence, classical LDA cannot be applied directly for high dimensional data such as network traffic data. In this paper, we propose two solutions to solve SSS problem for LDA and apply them to a network IDS. The first method, reduce the original dimension data using principal component analysis (PCA) and then apply LDA. In the second solution, we propose to use the pseudo inverse to avoid singularity of within-class scatter matrix due to SSS problem. After that, the KNN algorithm is used for classification process. We have chosen two known datasets KDDcup99 and NSLKDD for testing the proposed approaches. Results showed that the classification accuracy of (PCA+LDA) method outperforms clearly the pseudo inverse LDA method when we have large training data.

Keywords: LDA, Pseudoinverse, PCA, IDS, NSL-KDD, KDDcup99

Procedia PDF Downloads 227

Authors: O. Crisan, A. D. Crisan, I. Mercioniu, R. Nicula, F. Vasiliu

Abstract:

FePt-based systems are currently under scrutiny for their possible use as future materials for perpendicular magnetic recording. Another possible application is in the field of permanent magnets without rare-earths, magnets that are capable to operate at higher temperatures than the classic Nd-Fe-B magnets. Within this work, FeCoPt alloys prepared by rapid solidification from the melt are structurally and magnetically characterized. Extended transmission electron microscopy analysis shows the high degree of L10 ordering. X-ray diffraction is used to characterize the phase structure and to obtain the structural parameters of interest for L10 ordering. Co-existence of hard CoFePt and CoPt L10 phases with the soft fcc FePt phase is obtained within a refined microstructure made of alternatively disposed grains of around 5 to 20 nm in size. Magnetic measurements show increased remanence close to the parent L10 FePt phase and not so high coercivity due to the significant presence of the soft magnetic constituent phase. A Curie temperature of about 820K is reported for the FeCoPt alloy.

Keywords: melt-spinning, FeCoPt alloys, high-resolution electron microscopy (HREM), ordered L10 structure

Procedia PDF Downloads 318

Authors: E. L. Albuquerque, U. L. Fulco, G. S. Ourique

Abstract:

The focus of this work is on the numerical investigation of the charge transport properties of the de novo-designed alpha3 polypeptide, as well as in its variants, all of them probed by gene engineering. The theoretical framework makes use of a tight-binding model Hamiltonian, together with ab-initio calculations within quantum chemistry simulation. The alpha3 polypeptide is a 21-residue with three repeats of the seven-residue amino acid sequence Leu-Glu-Thr-Leu-Ala-Lys-Ala, forming an alpha–helical bundle structure. Its variants are obtained by Ala→Gln substitution at the e (5th) and g (7th) position, respectively, of the alpha3 polypeptide amino acid sequence. Using transmission electron microscopy and atomic force microscopy, it was observed that the alpha3 polypeptide and one of its variant do have the ability to form fibrous assemblies, while the other does not. Our main aim is to investigate whether or not the biased alpha3 polypeptide and its variants can be also identified by quantum charge transport measurements through current-voltage (IxV) curves as a pattern to characterize their fibrous assemblies. It was observed that each peptide has a characteristic current pattern, which may be distinguished by charge transport measurements, suggesting that it might be a useful tool for the development of biosensors.

Keywords: charge transport properties, electronic transmittance, current-voltage characteristics, biological sensor

Procedia PDF Downloads 665