Search results for: e-content producing algorithm

Authors: Mohan Li

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Nowadays, intelligent research technology is more and more important than traditional research methods in urban research work, and this proportion will greatly increase in the next few decades. Frequently such analyzing work cannot be carried without some software engineering knowledge. And here, domain models of urban research will be necessary when applying software engineering knowledge to urban work. In many urban plan practice projects, making rational models, feeding reliable data, and providing enough computation all make indispensable assistance in producing good urban planning. During the whole work process, domain models can optimize workflow design. At present, human beings have entered the era of big data. The amount of digital data generated by cities every day will increase at an exponential rate, and new data forms are constantly emerging. How to select a suitable data set from the massive amount of data, manage and process it has become an ability that more and more planners and urban researchers need to possess. This paper summarizes and makes predictions of the emergence of technologies and technological iterations that may affect urban research in the future, discover urban problems, and implement targeted sustainable urban strategies. They are summarized into seven major domain models. They are urban and rural regional domain model, urban ecological domain model, urban industry domain model, development dynamic domain model, urban social and cultural domain model, urban traffic domain model, and urban space domain model. These seven domain models can be used to guide the construction of systematic urban research topics and help researchers organize a series of intelligent analytical tools, such as Python, R, GIS, etc. These seven models make full use of quantitative spatial analysis, machine learning, and other technologies to achieve higher efficiency and accuracy in urban research, assisting people in making reasonable decisions.

Keywords: big data, domain model, urban planning, urban quantitative analysis, machine learning, workflow design

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Authors: Jung–Min Yang

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Solvability of the model matching problem for input/output switched asynchronous sequential machines is discussed in this paper. The control objective is to determine the existence condition and design algorithm for a corrective controller that can match the stable-state behavior of the closed-loop system to that of a reference model. Switching operations and correction procedures are incorporated using output feedback so that the controlled switched machine can show the desired input/output behavior. A matrix expression is presented to address reachability of switched asynchronous sequential machines with output equivalence with respect to a model. The presented reachability condition for the controller design is validated in a simple example.

Keywords: asynchronous sequential machines, corrective control, model matching, input/output control

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Authors: Amira Dhokar, Lobna Hlaoua, Lotfi Ben Romdhane

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Tweet contextualization (TC) is a new issue that aims to answer questions of the form 'What is this tweet about?' The idea of this task was imagined as an extension of a previous area called multi-document summarization (MDS), which consists in generating a summary from many sources. In both TC and MDS, the summary should ideally contain the most relevant information of the topic that is being discussed in the source texts (for MDS) and related to the query (for TC). Furthermore of being informative, a summary should be coherent, i.e. well written to be readable and grammatically compact. Hence, coherence is an essential characteristic in order to produce comprehensible texts. In this paper, we propose a new approach to improve readability and coherence for tweet contextualization based on bipartite graphs. The main idea of our proposed method is to reorder sentences in a given paragraph by combining most expressive words detection and HITS (Hyperlink-Induced Topic Search) algorithm to make up a coherent context.

Keywords: bipartite graphs, readability, summarization, tweet contextualization

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Authors: Ng Liang Shen, Ngahzaifa Abdul Ghani

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This research aimed at developing an early warning system for pre-diabetic and diabetics by analyzing simple and easily determinable signs and symptoms of diabetes among the people living in Malaysia using Particle Swarm Optimized Artificial. With the skyrocketing prevalence of Type 2 diabetes in Malaysia, the system can be used to encourage affected people to seek further medical attention to prevent the onset of diabetes or start managing it early enough to avoid the associated complications. The study sought to find out the best predictive variables of Type 2 Diabetes Mellitus, developed a system to diagnose diabetes from the variables using Artificial Neural Networks and tested the system on accuracy to find out the patent generated from diabetes diagnosis result in machine learning algorithms even at primary or advanced stages.

Keywords: diabetes diagnosis, Artificial Neural Networks, artificial intelligence, soft computing, medical diagnosis

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Authors: Yuzhi Cai

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This talk presents a Bayesian approach to quantile autoregressive (QAR) time series model estimation and forecasting. We establish that the joint posterior distribution of the model parameters and future values is well defined. The associated MCMC algorithm for parameter estimation and forecasting converges to the posterior distribution quickly. We also present a combining forecasts technique to produce more accurate out-of-sample forecasts by using a weighted sequence of fitted QAR models. A moving window method to check the quality of the estimated conditional quantiles is developed. We verify our methodology using simulation studies and then apply it to currency exchange rate data. An application of the method to the USD to GBP daily currency exchange rates will also be discussed. The results obtained show that an unequally weighted combining method performs better than other forecasting methodology.

Keywords: combining forecasts, MCMC, quantile modelling, quantile forecasting, predictive density functions

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Authors: Sohal Latif

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Solar power is in the form of photovoltaic, also known as PV, which is a form of renewable energy that applies solar panels in producing electricity from the sun. It has a vital role in fulfilling the present need for clean and renewable energy to get rid of conventional and non-renewable energy sources that emit high levels of greenhouse gases. Solar energy is embraced because of its availability, easy accessibility, and effectiveness in the provision of power, chiefly in country areas. In solar charging, device charge entails a change of light power into electricity using photovoltaic or PV panels, which supply direct current electric power or DC. Here, the solar charge controller has a very crucial role to play regarding the voltages and the currents coming from the solar panels to take up the changing needs of a battery without overcharging the same. Certain devices, such as inverters, are required to transform the DC power produced by the solar panels into an AC to serve the normal electrical appliances and the current power network. This project was initiated for a project of a solar charge controller and power converter with the MULTISIM. The formation of this project begins with a literature survey to obtain basic knowledge about power converters, charge controllers, and photovoltaic systems. Fundamentals of the operation of solar panels include the process by which light is converted into electricity and a comparison of PWM and MPPT chargers with controllers. Knowledge of rectifiers is built to help achieve AC-to-DC and DC-AC change. Choosing a resistor, capacitance, MOSFET, and OP-AMP is done by the need of the system. The circuit diagrams of converters and charge controllers are designed using the Multisim program. Pulse width modulation, Bubba oscillator circuit, and inverter circuits are modeled and simulated. In the subsequent steps, the analysis of the simulation outcomes indicates the efficiency of the intended converter systems. The various outputs from the different configurations, with the transformer incorporated as well as without it, are then monitored for effective power conversion as well as power regulation.

Keywords: solar charge controller, MULTISIM, converter, inverter

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Authors: Sunday Nathaniel Obasi, Chiamak Chinasa Obasi

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Soil characterization and classification provide the basic information necessary to create a functional evaluation and soil classification schemes. Fertility capability classification (FCC) on the other hand is a technical system that groups the soils according to kinds of problems they present for management of soil physical and chemical properties. This research was carried out in Ebonyi state, southeastern Nigeria, which is an agrarian state and a leading rice producing part of southeastern Nigeria. In order to maximize the soil and enhance the productivity of rice in Ebonyi soils, soil classification, and fertility classification information need to be supplied. The state was grouped into three locations according to their agricultural zones namely; Ebonyi north, Ebonyi central and Ebonyi south representing Abakaliki, Ikwo and Ivo locations respectively. Major rice growing areas of the soils were located and two profile pits were sunk in each of the studied zones from which soils were characterized, classified and fertility capability classification (FCC) developed. Soil classification was done using United State Department of Agriculture (USDA) Soil Taxonomy and correlated with World Reference Base for soil resources. Results obtained classified Abakaliki 1 and Abakaliki 2 as Typic Fluvaquents (Ochric Fluvisols). Ikwo 1 was classified as Vertic Eutrudepts (Eutric Vertisols) while Ikwo 2 was classified as Typic Eutrudepts (Eutric Cambisols). Ivo 1 and Ivo 2 were both classified as Aquic Eutrudepts (Gleyic Leptosols). Fertility capability classification (FCC) revealed that all studied soils had mostly loamy topsoils and subsoils except Ikwo 1 with clayey topsoil. Limitations encountered in the studied soils include; dryness (d), low ECEC (e), low nutrient capital reserve (k) and water logging/ anaerobic condition (gley). Thus, FCC classifications were Ldek for Abakaliki 1 and 2, Ckv for Ikwo 1, LCk for Ikwo 2 while Ivo 1 and 2 were Legk and Lgk respectively.

Keywords: soil classification, soil fertility, limitations, modifiers, Southeastern Nigeria

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Authors: Amor Jnifene, Cedric Cocaud

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One of the most constraining problems facing the development of autonomous vehicles is the limitations of current technologies. Guidance and navigation controllers need to be faster and more robust. Communication data links need to be more reliable and secure. For an Unmanned Aerial Vehicles (UAV) to be useful, and fully autonomous, one important feature that needs to be an integral part of the navigation system is autonomous trajectory planning. The work discussed in this paper presents a method for on-line trajectory planning for UAV’s. This method takes into account various constraints of different types including specific vectors of approach close to target points, multiple objectives, and other constraints related to speed, altitude, and obstacle avoidance. The trajectory produced by the proposed method ensures a smooth transition between different segments, satisfies the minimum curvature imposed by the dynamics of the UAV, and finds the optimum velocity based on available atmospheric conditions. Given a set of objective points and waypoints a skeleton of the trajectory is constructed first by linking all waypoints with straight segments based on the order in which they are encountered in the path. Secondly, vectors of approach (VoA) are assigned to objective waypoints and their preceding transitional waypoint if any. Thirdly, the straight segments are replaced by 3D curvilinear trajectories taking into account the aircraft dynamics. In summary, this work presents a method for on-line 3D trajectory generation (TG) of Unmanned Aerial Vehicles (UAVs). The method takes as inputs a series of waypoints and an optional vector of approach for each of the waypoints. Using a dynamic model based on the performance equations of fixed wing aircrafts, the TG computes a set of 3D parametric curves establishing a course between every pair of waypoints, and assembling these sets of curves to construct a complete trajectory. The algorithm ensures geometric continuity at each connection point between two sets of curves. The geometry of the trajectory is optimized according to the dynamic characteristics of the aircraft such that the result translates into a series of dynamically feasible maneuvers. In summary, this work presents a method for on-line 3D trajectory generation (TG) of Unmanned Aerial Vehicles (UAVs). The method takes as inputs a series of waypoints and an optional vector of approach for each of the waypoints. Using a dynamic model based on the performance equations of fixed wing aircraft, the TG computes a set of 3D parametric curves establishing a course between every pair of waypoints, and assembling these sets of curves to construct a complete trajectory. The algorithm ensures geometric continuity at each connection point between two sets of curves. The geometry of the trajectory is optimized according to the dynamic characteristics of the aircraft such that the result translates into a series of dynamically feasible maneuvers.

Keywords: trajectory planning, unmanned autonomous air vehicle, vector of approach, waypoints

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Authors: Radhia Toujani, Jalel Akaichi

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Social Networks generally present a hierarchy of communities. To determine these communities and the relationship between them, detection algorithms should be applied. Most of the existing algorithms, proposed for hierarchical communities identification, are based on either agglomerative clustering or divisive clustering. In this paper, we present a hybrid hierarchical clustering approach for community detection based on both bottom-up and bottom-down clustering. Obviously, our approach provides more relevant community structure than hierarchical method which considers only divisive or agglomerative clustering to identify communities. Moreover, we performed some comparative experiments to enhance the quality of the clustering results and to show the effectiveness of our algorithm.

Keywords: agglomerative hierarchical clustering, community structure, divisive hierarchical clustering, hybrid hierarchical clustering, opinion mining, social network, social network analysis

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Authors: Kolade Olawande Owoeye, George R. S. Weir

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The widespread of terrorism and extremism activities on the internet has become a major threat to the government and national securities due to their potential dangers which have necessitated the need for intelligence gathering via web and real-time monitoring of potential websites for extremist activities. However, the manual classification for such contents is practically difficult or time-consuming. In response to this challenge, an automated classification system called composite technique was developed. This is a computational framework that explores the combination of both semantics and syntactic features of textual contents of a web. We implemented the framework on a set of extremist webpages dataset that has been subjected to the manual classification process. Therein, we developed a classification model on the data using J48 decision algorithm, this is to generate a measure of how well each page can be classified into their appropriate classes. The classification result obtained from our method when compared with other states of arts, indicated a 96% success rate in classifying overall webpages when matched against the manual classification.

Keywords: extremist, web pages, classification, semantics, posit

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Authors: E. Bartkiene, V. Krungleviciute, J. Kucinskiene, R. Antanaitis, A. Kucinskas

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In calves less than 10-day-old, infection commonly cause severe diarrhoea and high mortality. To prevention of calves diseases a common practice is to treat calves with prophylactic antibiotics, in this case the use of lactic acid bacteria (LAB) is promising. Often LAB strains are incubated in comercial de Man-Rogosa-Sharpe (MRS) medium, the culture are centrifuged, the cells are washing with sterile water, and this suspension is used as a starter culture for animal health care. Juice of potatoe tubers is industrial wastes, wich may constitute a source of digestible nutrients for microorganisms. In our study the ability of LAB to utilize potatoe tubers juice in cell synthesis without external nutrient supplement was investigated, and the influence of multiplied LAB on calves blood parameters was evaluated. Calves were selected based on the analogy principle (treatment group (n=6), control group (n=8)). For the treatment group 14 days was given a 50 ml of fermented potatoe tubers juice containing 9.6 log10 cfu/ml of LAB. Blood parameters (gas and biochemical) were assessed by use of an auto-analyzers (Hitachi 705 and EPOC). Before the experiment, blood pH of treatment group calves was 7.33, control – 7.36, whereas, after 14 days, 7.28 and 7.36, respectively. Calves blood pH in the treatment group remained stable over the all experiment period. Concentration of PCO2 in control calves group blood increased from 63.95 to 70.93, whereas, in the treatment group decreased from 63.08 to 60.71. Concentration of lactate in the treatment group decreased from 3.20 mmol/l to 2.64 mmol/l, whereas, in control - increased from 3.95 mmol/l to 4.29 mmol/l. Concentration of AST in the control calves group increased from 50.18 IU/L to 58.9 IU/L, whereas, in treatment group decreased from 49.82 IU/L to 33.1 IU/L. We conclude that the 50 ml of fermented potatoe tubers juice containing 9.6 log10 cfu/ml of LAB per day, by using 14 days, reduced risk of developing acidosis (stabilizes blood pH (p < 0.05)), reduces lactates and PCO2 concentration (p < 0.05) and risk of liver lesions (reduces AST concentration (p < 0.005)) in blood of calves.

Keywords: alternative substrate, blood parameters, calves, lactic acid bacteria

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Authors: Goran Begović

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In recent years, many researchers are focusing on non-intrusive measuring methods when it comes to human biosignals. These methods provide solutions for everyday use, whether it’s health monitoring or finessing the workout routine. One of the biggest issues with these solutions is that the sensors’ accuracy is highly variable due to many factors, such as ambiental light, skin color diversity, etc. That is why we wanted to explore different outcomes under those kinds of circumstances in order to find the most optimal algorithm(s) for extracting heart rate (HR) information. The optimization of such algorithms can benefit the wider, cheaper, and safer application of home health monitoring, without having to visit medical professionals as often when it comes to observing heart irregularities. In this study, we explored the accuracy of infrared (IR), red, and green LED sensorics in a controlled environment and compared the results with a medically accurate ECG monitoring device.

Keywords: data science, ECG, heart rate, holter monitor, LED sensors

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Authors: Sakshi Piplani, Ajit Kumar

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Valproic acid (VPA) is the first synthetic therapeutic agent used to treat epileptic disorders, which account for affecting nearly 1% world population. Teratogenicity caused by VPA has prompted the search for next generation drug with better efficacy and lower side effects. Recent studies have posed HDAC8 as direct target of VPA that causes the teratogenic effect in foetus. We have employed molecular dynamics (MD) and docking simulations to understand the binding mode of VPA and their analogues onto HDAC8. A total of twenty 3D-structures of human HDAC8 isoforms were selected using BLAST-P search against PDB. Multiple sequence alignment was carried out using ClustalW and PDB-3F07 having least missing and mutated regions was selected for study. The missing residues of loop region were constructed using MODELLER and energy was minimized. A set of 216 structural analogues (>90% identity) of VPA were obtained from Pubchem and ZINC database and their energy was optimized with Chemsketch software using 3-D CHARMM-type force field. Four major neurotransmitters (GABAt, SSADH, α-KGDH, GAD) involved in anticonvulsant activity were docked with VPA and its analogues. Out of 216 analogues, 75 were selected on the basis of lower binding energy and inhibition constant as compared to VPA, thus predicted to have anti-convulsant activity. Selected hHDAC8 structure was then subjected to MD Simulation using licenced version YASARA with AMBER99SB force field. The structure was solvated in rectangular box of TIP3P. The simulation was carried out with periodic boundary conditions and electrostatic interactions and treated with Particle mesh Ewald algorithm. pH of system was set to 7.4, temperature 323K and pressure 1atm respectively. Simulation snapshots were stored every 25ps. The MD simulation was carried out for 20ns and pdb file of HDAC8 structure was saved every 2ns. The structures were analysed using castP and UCSF Chimera and most stabilized structure (20ns) was used for docking study. Molecular docking of 75 selected VPA-analogues with PDB-3F07 was performed using AUTODOCK4.2.6. Lamarckian Genetic Algorithm was used to generate conformations of docked ligand and structure. The docking study revealed that VPA and its analogues have more affinity towards ‘hydrophobic active site channel’, due to its hydrophobic properties and allows VPA and their analogues to take part in van der Waal interactions with TYR24, HIS42, VAL41, TYR20, SER138, TRP137 while TRP137 and SER138 showed hydrogen bonding interaction with VPA-analogues. 14 analogues showed better binding affinity than VPA. ADMET SAR server was used to predict the ADMET properties of selected VPA analogues for predicting their druggability. On the basis of ADMET screening, 09 molecules were selected and are being used for in-vivo evaluation using Danio rerio model.

Keywords: HDAC8, docking, molecular dynamics simulation, valproic acid

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Authors: Sarra Kitanou

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Water recycling and reuse is an effective measure to solve the water stress problem. The sustainable use of water resource has become a national development strategy in Morocco. A key aspect of improving overall sustainability is the potential for direct wastewater effluent reuse. However, the hybrid technology membrane bioreactors (MBR) have been identified as an attractive option for producing high quality and nutrient-rich effluents for wastewater treatment. It is based on complex interactions between biological processes, filtration process and rheological properties of the liquid to be treated. Currently, with the evolution of wastewater treatment projects in Morocco, the MBR technology can be used as a technology treating different types of wastewaters and to produce effluent with suitable quality for reuse. However, the energetic consumption of this process is a great concern, which can limit the development and implementation of this technology. In this investigation, the electric energy consumption of an ultrafiltration membrane bioreactor process in domestic wastewater treatment is evaluated and compared to some MBR installations based on literature review. Energy requirements of the MBR are linked to operational parameters and reactor performance. The analysis of energy consumption shows that the biological aeration and membrane filtration are more energy consuming than the other components listed as feed and recirculation pumps. Biological aeration needs 53% of the overall energetic consumption and the specific energy consumption for membrane filtration is about 25%. However, aeration is a major energy consumer, often exceeding 50% share of total energy consumption. The optimal results obtained on the MBR process (pressure p = 1.15 bar), hydraulic retention time (15 h) showed removal efficiencies up to 90% in terms of organic compounds removal, 100% in terms of suspended solids presence and up to 80% reduction of total nitrogen and total phosphorus. The effluent from this MBR system could be considered as qualified for irrigation reuse, showing its potential application in the future.

Keywords: hybrid process, membrane bioreactor, wastewater treatment, reuse

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Authors: Cornelius Femi Popoola

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Nigerian manufacturing industry, particularly the flour producing firms play vital roles in Nigerian economy. This sector’s quality management practice is given a little attention along with organizational factors that hinder successful practice of total quality management which needs to be documented. Honeywell Flour Mills Plc operate in Nigeria with an appreciable number of products that serves this sector of the economy. Internal-external disposition of the company and total quality practice of the company deserve some elucidations. Hence, this study examined the influence of organizational factors on total quality management practice of Nigerian manufacturing industry, using Honeywell Flour Mills Plc as a case study. The study employed the correlational type of descriptive survey research design. The population consisted of 656 staff of Honeywell Flour Mills Plc, out of which 235 members were selected through scientific sampling method developed by Paler-Calmorin and Calmorin. A total of 235 copies of questionnaires titled 'Organisational Factors and Total Quality Management Practices (QF-TQM) Questionnaire' were administered with a response rate of 66 copies returned. The following variables were applied internal organisational factors (IOFs), external organizational factors (EOFs) and total quality management (TQM). Data generated were analysed using frequency distribution and regression analysis at 0.05 level. The findings revealed that IOFs positively and significantly related with TQM (r = .147**, N= 64, P(.000) < .01). Also, EOFs negatively and significantly related with TQM (r = -.117, N= 64, P(.000) < .01). Findings showed that internal and external organizational factors jointly influenced TQM practiced in F₍₂,₆₁₎=22.250; R²=.629; Adj.R²=.603; P(.000) < .05). The study concluded that organizational factors are determinants of TQM practice in Nigerian manufacturing industry. It is recommended that both internal and external organizational factors influencing TQM practices should be considered in the development of TQM strategies.

Keywords: external organizational factors, internal organisational factors, Nigerian manufacturing industry, total quality management

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Authors: Anubhav Shrivastava, Lakshya Bhat, Shivarudraswamy

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Distributed Generation is the need of the hour. With current advancements in the DG technology, there are some major issues that need to be tackled in order to make this method of generation of energy more efficient and feasible. Among other problems, the control in voltage is the major issue that needs to be addressed. This paper focuses on control of voltage using reactive power control of DGs with the help of fuzzy logic. The membership functions have been defined accordingly and the control of the system is achieved. Finally, with the help of simulation results in Matlab, the control of voltage within the tolerance limit set (+/- 5%) is achieved. The voltage waveform graphs for the IEEE 14 bus system are obtained by using simple algorithm with MATLAB and then with fuzzy logic for 14 bus system. The goal of this project was to control the voltage within limits by controlling the reactive power of the DG using fuzzy logic.

Keywords: distributed generation, fuzzy logic, matlab, newton raphson, IEEE 14 bus, voltage regulation, radial network

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Authors: Adnan I. O. Zaid

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Zinc aluminum alloys are versatile materials which are widely used in manufacturing several parts in the automobile and aircraft industries. The effect of grain refinement of these alloys by rare earth elements on their mechanical characteristics is scarce. The equal channel angular pressing is relatively recent method for producing severe plastic deformation in materials subjected to it resulting in refinement of their structure and enhancement of their mechanical characteristics. The phase diagram of these alloys indicates that large dendrites of large grain size can be formed during their solidification of the cast which tends to deteriorate their mechanical strength and surface quality. To overcome this problem they are normally grain refined by either titanium or titanium + boron to their melt prior to solidification. In this paper, comparison between the effect of adding either titanium, (Ti), titanium+boron, (Ti+B), or Molybdenum, Mo, to zinc-aluminum22, alloy, (ZA22) on its metallurgical and mechanical characteristics in the cast condition and after pressing by the ECAP process is investigated. It was found that addition of either Ti, Ti+B, or Mo to the ZA22 alloy in the cast condition resulted in refining of their structure being more refined by the addition of Mo, then .Ti+B and less refining by Ti addition. Furthermore, the ECAP process resulted in further refinement of the alloy micro structure except in case of Ti+B addition where poisoning i.e. coarsening of the grains has occurred. Regarding the addition of these element on the mechanical behavior; it was found that addition of Ti Or Ti+B resulted in little enhancement of the alloy strength factor and its flow stress at 20% true strain; whereas, the addition of resulted in deteriorating of its mechanical behavior as % decrease in the strength factor and % in its flow stress of 20%. As for the strain hardening index; addition of any of these elements resulted in decreasing the strain hardening index.

Keywords: addition, grain refinement, mechanical characteristics, microstructure, rare earth elements, ZA-22, Zinc- aluminum alloy

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Authors: Raida Zouari, Iness Ahriz, Rafik Zayani, Ali Dziri, Ridha Bouallegue

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This paper proposes a room level indoor localization algorithm based on the use Multi-Layer Neural Network (MLNN) classifiers and one versus one strategy. Seven parameters of the Channel Impulse Response (CIR) were used and Gram-Shmidt Orthogonalization was performed to study the relevance of the extracted parameters. Simulation results show that when relevant CIR parameters are used as position fingerprint and when optimal MLNN architecture is selected good room level localization score can be achieved. The current study showed also that some of the CIR parameters are not correlated to the location and can decrease the localization performance of the system.

Keywords: mobile indoor localization, multi-layer neural network (MLNN), channel impulse response (CIR), Gram-Shmidt orthogonalization

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Authors: R. Adwaith, J. Gopinath, Vasantha Kohila B., R. Chandru, Arul Prakash R.

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The turbofan engine is a type of air breathing engine that is widely used in aircraft propulsion produces thrust mainly from the mass-flow of air bypassing the engine core. The present research has developed an effective method numerically by increasing the thrust generated from the bypass air. This thrust increase is brought about by heating the walls of the bypass valve from the combustion chamber using convective heat transfer method. It is achieved computationally by the use external heat to enhance the velocity of bypass air of turbofan engines. The bypass valves are either heated externally using multicell tube resistor which convert electricity generated by dynamos into heat or heat is transferred from the combustion chamber. This increases the temperature of the flow in the valves and thereby increase the velocity of the flow that enters the nozzle of the engine. As a result, mass-flow of air passing the core engine for producing more thrust can be significantly reduced thereby saving considerable amount of Jet fuel. Numerical analysis has been carried out on a scaled down version of a typical turbofan bypass valve, where the valve wall temperature has been increased to 700 Kelvin. It is observed from the analysis that, the exit velocity contributing to thrust has significantly increased by 10 % due to the heating of by-pass valve. The degree of optimum increase in the temperature, and the corresponding effect in the increase of jet velocity is calculated to determine the operating temperature range for efficient increase in velocity. The technique used in the research increases the thrust by using heated by-pass air without extracting much work from the fuel and thus improve the efficiency of existing turbofan engines. Dimensional analysis has been carried to prove the accuracy of the results obtained numerically.

Keywords: turbofan engine, bypass valve, multi-cell tube, convective heat transfer, thrust

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Authors: M. Sazali Said, Rahim Jamian, Shahrizan Yusoff, Shahruzaman Sulaiman, Jum'Azulhisham Abdul Shukor

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This case study focuses on the role of Universiti Kuala Lumpur Malaysian Spanish Institute (UniKL MSI) to produce technologist in order to reduce the shortage of skilled workers especially in the automotive industry. The purpose of the study therefore seeks to examine the effectiveness of Technical Education and Vocational Training (TEVT) curriculum of UniKL MSI to produce graduates that could immediately be productively employed by the automotive industry. The approach used in this study is through performance evaluation of students attending the Industrial Training Attachment (INTRA). The sample of study comprises of 37 students, 16 university supervisors and 26 industrial supervisors. The research methodology involves the use of quantitative and qualitative methods of data collections through the triangulation approach. The quantitative data was gathered from the students, university supervisors and industrial supervisors through the use of questionnaire. Meanwhile, the qualitative data was obtained from the students and university supervisors through the use of interview and observation. Both types of data have been processed and analyzed in order to summarize the results in terms of frequency and percentage by using a computerized spread sheet. The result shows that industrial supervisors were satisfied with the students’ performance. Meanwhile, university supervisors rated moderate effectiveness of the UniKL MSI curriculum in producing graduates with appropriate skills and in meeting the industrial needs. During the period of study, several weaknesses in the curriculum have been identified for further continuous improvements. Recommendations and suggestions for curriculum improvement also include the enhancement of technical skills and competences of students towards fulfilling the needs and demand of the automotive industries.

Keywords: technical education and vocational training (TEVT), industrial training attachment (INTRA), curriculum improvement, automotive industry

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Authors: S. Ahmad, C. Yuan, Q. Zhang

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The assorted architectural plasticity of a plant is majorly specified by stooling, a phenomenon tackled by a combination of developmental, environmental and hormonal accelerators of lateral buds. Chrysanthemums (Chrysanthemum morifolium) are one of the most economically important ornamental plants worldwide on the account of having plentiful architectural patterns, diverse shapes and attractive colors. Side branching is the major determinant guaranteeing the consistent demand of cut chrysanthemum in flower industry. Presence of immense number of axillary branches devalues the economic importance of this imperative plant and is a major challenge for mum growers to hold a stake in the cut flower market. Restricting branches to a minimum level, or no branches at all, is the dire need of the day in order to introducing novelty in cut chrysanthemums. Temperature is a potent factor which affects largely the escalation, development of chrysanthemum, and also the genetic expression of various vegetative traits like branching. It affects differently the developmental characteristics and phenotypic expressions of inherent qualities, thereby playing a significant role in differentiating the developmental responses in different cultivars of chrysanthemum. A detailed study pertaining to the affect of temperature on branching in chrysanthemum is a clear lacking throughout the literature on mums. Therefore, searching with temperature as an effective means of reducing side branching to a desired level could be an influencing extension of struggles about how to nullify stooling. This requires plenty of research in order to reveal the extended penetration of temperature in manipulating the genetic control of various important traits like branching, which is a burning issue now a days in producing cut flowers in chrysanthemum. The present review will highlight the impact of temperature on branching control mechanism in chrysanthemum at morpho-physiological, hormonal and molecular levels.

Keywords: branching, chrysanthemum, genetic control, hormonal, morpho-physiological, temperature

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Authors: Nadia Akkari

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This paper presents the analysis and simulation of the control of double star induction motor, using direct torque control (DTC) based on RST regulator. The DTC is an excellent solution for general- purpose induction drives in very wide range the short sampling time required by the TC schemes makes them suited to a very fast torque and flux controlled drives as well the simplicity of the control algorithm. DTC is inherently a motion sensorless control method. The RST regulator can improve the double star induction motor performance in terms of overshoot, rapidity, cancellation of disturbance, and capacity to maintain a high level of performance. Simulation results indicate that the proposed regulator has better performance responses. The implementation of the DTC applied to a double star induction motor based on RST regulator is validated with simulated results.

Keywords: Direct Torque Control (DTC), Double Star Induction Motor (DSIM), RST Regulator

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Authors: Vijay Kr. Yadav, Nilam Rathi

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Accurate prediction of glucose level for diabetes mellitus is required to avoid affecting the functioning of major organs of human body. This study describes the fundamental assumptions and two different methodologies of the Blood glucose prediction. First is based on the back-propagation algorithm of Artificial Neural Network (ANN), and second is based on the Neuro-Fuzzy technique, called Fuzzy Inference System (FIS). Errors between proposed methods further discussed through various statistical methods such as mean square error (MSE), normalised mean absolute error (NMAE). The main objective of present study is to develop mathematical model for blood glucose prediction before 12 hours advanced using data set of three patients for 60 days. The comparative studies of the accuracy with other existing models are also made with same data set.

Keywords: back-propagation, diabetes mellitus, fuzzy inference system, neuro-fuzzy

Procedia PDF Downloads 259

Authors: Vaishakh Medikeri

Abstract:

Lightning, the marvelous, spectacular and the awesome truth of nature is one of the greatest energy sources left unharnessed since ages. A single lightning bolt of lightning contains energy of about 15 billion joules. This huge amount of energy cannot be harnessed completely but partially. This paper proposes to harness the energy from lightning strikes. Throughout the globe the frequency of lightning is 40-50 flashes per second, totally 1.4 billion flashes per year; all of these flashes carrying an average energy of about 15 billion joules each. When a lightning bolt strikes the ground, tremendous amounts of energy is transferred to earth which propagates in the form of concentric circular energy waves. These waves have a frequency of about 7.83Hz. Harvesting the lightning bolt directly seems impossible, but harvesting the energy waves produced by the lightning is pretty easier. This can be done using a tricoil energy harnesser which is a new device which I have invented. We know that lightning bolt seeks the path which has minimum resistance down to the earth. For this we can make a lightning rod about 100 meters high. Now the lightning rod is attached to the tricoil energy harnesser. The tricoil energy harnesser contains three coils whose centers are collinear and all the coils are parallel to the ground. The first coil has one of its ends connected to the lightning rod and the other end grounded. There is a secondary coil wound on the first coil with one of its end grounded and the other end pointing to the ground and left unconnected and placed a little bit above the ground so that this end of the coil produces more intense currents, hence producing intense energy waves. The first coil produces very high magnetic fields and induces them in the second and third coils. Along with the magnetic fields induced by the first coil, the energy waves which are currents also flow through the second and the third coils. The second and the third coils are connected to a generator which in turn is connected to a capacitor which stores the electrical energy. The first coil is placed in the middle of the second and the third coil. The stored energy can be used for transmission of electricity. This new technique of harnessing the lightning strikes would be most efficient in places with more probability of the lightning strikes. Since we are using a lightning rod sufficiently long, the probability of cloud to ground strikes is increased. If the proposed apparatus is implemented, it would be a great source of pure and clean energy.

Keywords: generator, lightning rod, tricoil energy harnesser, harvesting energy

Procedia PDF Downloads 386

Authors: Qing K. Zhu

Abstract:

Very Large Scale Integration (VLSI) design becomes very complex due to the continuous integration of millions of gates in one chip based on Moore’s law. Designers have encountered numerous report files during design iterations using timing and noise analysis tools. This paper presented our work using data mining techniques combined with HTML tables to extract and represent critical timing/noise data. When we apply this data-mining tool in real applications, the running speed is important. The software employs table look-up techniques in the programming for the reasonable running speed based on performance testing results. We added several advanced features for the application in one industry chip design.

Keywords: VLSI design, data mining, big data, HTML forms, web, VLSI, EDA, timing, noise

Procedia PDF Downloads 254

Authors: Karina Zaccari, Ernesto Cordeiro Marujo

Abstract:

This paper presents a Machine Learning (ML) approach to support Meningitis diagnosis in patients at a children’s hospital in Sao Paulo, Brazil. The aim is to use ML techniques to reduce the use of invasive procedures, such as cerebrospinal fluid (CSF) collection, as much as possible. In this study, we focus on predicting the probability of Meningitis given the results of a blood and urine laboratory tests, together with the analysis of pain or other complaints from the patient. We tested a number of different ML algorithms, including: Adaptative Boosting (AdaBoost), Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Random Forest and Support Vector Machines (SVM). Decision Tree algorithm performed best, with 94.56% and 96.18% accuracy for training and testing data, respectively. These results represent a significant aid to doctors in diagnosing Meningitis as early as possible and in preventing expensive and painful procedures on some children.

Keywords: machine learning, medical diagnosis, meningitis detection, pediatric research

Procedia PDF Downloads 151

Authors: Dassou Nagassou

Abstract:

Recent increase in frequency and severity of climatic impacts throughout the world has put a particular emphasis on the urgency to address the anthropogenic greenhouse gas emissions. The latter, mainly composed of carbon dioxide are responsible for the global warming of planet earth. Despite efforts to transition towards a zero-emission economy, manufacturing industries, electricity generation power plants, and transportation sectors continue to encounter challenges which hinder their progress towards a full decarbonization. The growing energy demand from both developed and under-developed economies exacerbates the situation and as a result, more carbon dioxide is discharged into the atmosphere. This situation imposes a lot of constraints on industries which are involved i.e., manufacturing industries, transportation, and electricity generation which must navigate the stringent environmental regulations in order to remain profitable. Existing solutions such as energy efficiencies, green materials (life cycle analysis), and many more have fallen short to address the problem due to their inadaptation to existing infrastructures, low efficiencies, and prohibitive costs. The proposed technology exploits the synergistic interaction between solar radiation and plasma to boost a direct decomposition of the molecules of carbon dioxide while producing alternative fuels which can be used to sustain on-site high-temperature processes via 100% solar energy harvesting in the form of photons and electricity. The advantages of this technology and its ability to be easily integrated into existing systems make it appealing for the industry which can now afford to fast track on the path towards full decarbonization, thanks to the solar plasma reactor. Despite the promising experimental results which proved the viability of this concept, solar-plasma reactors require further investigations to understand the synergistic interactions between plasma and solar radiation for a potential technology scale-up.

Keywords: solar, non-equilibrium, plasma, reactor, greenhouse-gases, solar-fuels

Procedia PDF Downloads 59

Authors: Andrey Yatsun, Andrei Malchikov

Abstract:

Exoskeletons are used to support and compensate for the load on the human musculoskeletal system. Elastomers are an important component of exoskeletons, providing additional support and compensating for the load. The algorithm of the active elastomer tension system provides the required auxiliary force depending on the angle of rotation and the tilt speed of the operator's torso. Feedback for the drive is provided by a force sensor integrated into the attachment of the exoskeleton vest. The use of direct force measurement ensures the required accuracy in all settings of the man-machine system. Non-adjustable elastic elements make it difficult to move without load, tilt forward and walk. A strategy for the organization of the auxiliary forces management system is proposed based on the allocation of 4 operating modes of the human-machine system.

Keywords: soft exoskeleton, mathematical modeling, pre-tension elastomer, human-machine interaction

Procedia PDF Downloads 68

Authors: Shivani Singh, Shantanu Nakhare, Kalyani Nair, Rohan Shetty

Abstract:

As social networking is growing at a rapid pace today it is vital that we work on improving its management. Research has shown that the content present in online social networks may have significant influence on impressionable minds. If such platforms are misused, it will lead to negative consequences. Detecting insults or inappropriate messages continues to be one of the most challenging aspects of Online Social Networks (OSNs) today. We address this problem through a Machine Learning Based Soft Text Classifier approach using Support Vector Machine algorithm. The proposed system acts as a screening mechanism the alerts the user about such messages. The messages are classified according to their subject matter and each comment is labeled for the presence of profanity and insults.

Keywords: machine learning, online social networks, soft text classifier, support vector machine

Procedia PDF Downloads 510

Authors: Guilherme Ribeiro, Alexandre Oliveira, Antonio Ferreira, Shyam Visweswaran, Gregory Cooper

Abstract:

Patient-specific models are instance-based learning algorithms that take advantage of the particular features of the patient case at hand to predict an outcome. We introduce two patient-specific algorithms based on decision tree paradigm that use AUC as a metric to select an attribute. We apply the patient specific algorithms to predict outcomes in several datasets, including medical datasets. Compared to the patient-specific decision path (PSDP) entropy-based and CART methods, the AUC-based patient-specific decision path models performed equivalently on area under the ROC curve (AUC). Our results provide support for patient-specific methods being a promising approach for making clinical predictions.

Keywords: approach instance-based, area under the ROC curve, patient-specific decision path, clinical predictions

Procedia PDF Downloads 479