Search results for: thermal cycling machine

Authors: Aldan A. Sapargaliyev, Yerbol A. Sapargaliyev

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Since the invention, the electric machine (EM) can be defined as oEM – one-vector electric machine, as it works due to one-vector inductive coupling with use of one-vector electromagnet. The disadvantages of oEM are large size and limited efficiency at low and medium power applications. This paper describes multi-vector electric machine (mEM) based on multi-vector inductive coupling, which is characterized by the increased surface area of ​​the inductive coupling per EM volume, with a reduced share of inefficient and energy-consuming part of the winding, in comparison with oEM’s. Particularly, it is considered, calculated and compared the performance of three different electrical motors and their power at the same volumes and rotor frequencies. It is also presented the result of calculation of correlation between power density and volume for oEM and mEM. The method of multi-vector inductive coupling enables mEM to possess 1.5-4.0 greater density of power per volume and significantly higher efficiency, in comparison with today’s oEM, especially in low and medium power applications. mEM has distinct advantages, when used in transport vehicles such as electric cars and aircrafts.

Keywords: electric machine, electric motor, electromagnet, efficiency of electric motor

Procedia PDF Downloads 315

Authors: Foroozan Sadri, Mohammadmehdi Moulaii, Farkhondeh Vahdati

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Smart materials can create a great revolution in our built environment, as living systems do. In this research, the optimal structure of healthcare building envelopes is analyzed in terms of thickness according to the utility of the smart-nano materials as nontoxic substances in the region. The research method in this paper is based on library studies and simulation. Grasshopper program is employed to simulate thermal characteristics to achieve the optimum U-value in Semnan desert climate, according to Iranian national standards. The potential of healthcare envelope layouts in thermal properties development (primarily U-value) of these buildings is discussed due to the high thermal loads of healthcare buildings and also toxicity effects of conventional materials. As a result, envelope thicknesses are calculated, and the performance of the nano-PCM and gypsum wallboards are compared. A solution with comparable performance using smart-nano materials instead of conventional materials would determine a decrease in wall thickness.

Keywords: energy saving, exterior envelope, smart-nano materials, thermal performance, U-value

Procedia PDF Downloads 141

Authors: Nitin Shravan, Sudarsun Santhiappan, B. Sivaselvan

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Subsection identification, in the context of Electronic Health Records (EHRs), is identifying the important sections for down-stream tasks like auto-coding. In this work, we classify the text present in EHRs according to their information, using machine learning and deep learning techniques. We initially describe briefly about the problem and formulate it as a text classification problem. Then, we discuss upon the methods from the literature. We try two approaches - traditional feature extraction based machine learning methods and deep learning methods. Through experiments on a private dataset, we establish that the deep learning methods perform better than the feature extraction based Machine Learning Models.

Keywords: deep learning, machine learning, semantic clinical classification, subsection identification, text classification

Procedia PDF Downloads 183

Authors: Fatema Tasmia, Brishti Majumder, Atiqur Rahman

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Attaining appropriate comfort conditions in a place where the climate is hot and humid can be perplexing. Especially, when it is resided at a congested place like old Dhaka Bangladesh, the provision of giving cross ventilation and building with proper orientation is quite difficult. Courtyards are the part of buildings which are used as space for outdoor household activities, social gathering and it is also proved to have indoor thermal comfort as an effect of courtyard. This paper aims to investigate the effect of courtyard in indoor thermal environment of a room adjacent to the courtyard and a room next to transitional space after a courtyard through field measurements of a case study house. The field measurement was conducted in a two-storey house. Among different aspects of thermal environment, the study of this paper is based on the analysis of temperature in both situations. Ventilation or air movement was considered to have no impact because of the rooms’ layout and location. Other aspects and their variables were considered as constant (especially material) for accuracy and avoidance of confusion. This study focuses on the outcome that can ultimately contribute to the configuration of courtyards and in its relation to indoor space while achieving thermal comfort.

Keywords: courtyard, old Dhaka, temperature, thermal comfort, transitional space

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Authors: M. A. Lahmer, K. Guergouri

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In this work, Kinetic Monte Carlo (KMC) method was applied to study the thermal stability of hydrogen in ZnO bulk and thin films. Our simulation includes different possible events such as interstitial hydrogen (Hi) jumps, substitutional hydrogen (HO) formation and dissociation, oxygen and zinc vacancies jumps, hydrogen-VZn complexes formation and dissociation, HO-Hi complex formation and hydrogen molecule (H2) formation and dissociation. The obtained results show that the hidden hydrogen formed during thermal annealing or at room temperature is constituted of both hydrogen molecule and substitutional hydrogen. The ratio of this constituants depends on the initial defects concentration as well as the annealing temperature. For annealing temperature below 300°C hidden hydrogen was found to be constituted from both substitutional hydrogen and hydrogen molecule, however, for higher temperature it is composed essentially from HO defects only because H2 was found to be unstable. In the other side, our results show that the remaining hydrogen amount in sample during thermal annealing depend greatly on the oxygen vacancies in the material. H2 molecule was found to be stable for thermal annealing up to 200°C, VZnHn complexes are stable up to 350°C and HO was found to be stable up to 450°C.

Keywords: ZnO, hydrogen, thermal annealing, kinetic Monte Carlo

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Authors: Kewen Li

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Much attention has been paid to the application of low temperature thermal resources, especially for power generation in recent years. Most of the current commercialized thermal, including geothermal, power-generation technologies convert thermal energy to electric energy indirectly, that is, making mechanical work before producing electricity. Technology using thermoelectric generator (TEG), however, can directly transform thermal energy into electricity by using Seebeck effect. TEG technology has many advantages such as compactness, quietness, and reliability because there are no moving parts. One of the big disadvantages of TEGs is the low efficiency from thermal to electric energy. For this reason, we redesigned and modified our previous 1 KW (at a temperature difference of around 120 °C) TEG system. The efficiency of the system was improved significantly, about 20% greater. Laboratory experiments have been conducted to measure the output power, including both open and net power, at different conditions: different modes of connections between TEG modules, different mechanical structures, different temperature differences between hot and cold sides. The cost of the TEG power generator has been reduced further because of the increased efficiency and is lower than that of photovoltaics (PV) in terms of equivalent energy generated. The TEG apparatus has been pilot tested and the data will be presented. This kind of TEG power system can be applied in many thermal and geothermal sites with low temperature resources, including oil fields where fossil and geothermal energies are co-produced.

Keywords: TEG, direct power generation, efficiency, thermoelectric effect

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation

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Authors: A. Şahinoğlu, R. Gürbüz, A. Güllü, M. Karhan

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This study in production machines, it is aimed that machine will automatically perceive cutting data and alter cutting parameters. The two most important parameters have to be checked in machine control unit are progress feed rate and speeds. These parameters are aimed to be controlled by sounds of machine. Optimum sound’s features introduced to computer. During process, real time data is received and converted by Matlab software. Data is converted into numerical values. According to them progress and speeds decreases/increases at a certain rate and thus optimum sound is acquired. Cutting process is made in respect of optimum cutting parameters. During chip remove progress, features of cutting tools, kind of cut material, cutting parameters and used machine; affects on various parameters. Instead of required parameters need to be measured such as temperature, vibration, and tool wear that emerged during cutting process; detailed analysis of the sound emerged during cutting process will provide detection of various data that included in the cutting process by the much more easy and economic way. The relation between cutting parameters and sound is being identified.

Keywords: cutting process, sound processing, intelligent late, sound analysis

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Authors: A. Dėdelė, A. Miškinytė

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The assessment of air pollution exposure in different microenvironments is important for better understanding the relationship between health effects caused by air pollution. The recent researches revealed that the level of air pollution in transport microenvironment contributes considerably to the total exposure of air pollution. The aim of the study was to determine air pollution of nitrogen dioxide and to assess the exposure of NO2 dependence on the chosen commuting mode using a global positioning system (GPS). The same travel destination was chosen and 30 rides in three different commuting modes: cycling, walking, and public transport were made. Every different mean of transport is associated with different route. GPS device and travel diary data were used to track all routes of different commuting modes. Air pollution of nitrogen dioxide was determined using the ADMS-Urban dispersion model. The average annual concentration of nitrogen dioxide was modeled for 2011 year in Kaunas city. The geographical information systems were used to visualize the travel routes, to create maps indicating the route of different commuting modes and to combine modelled nitrogen dioxide data. The results showed that there is a significant difference between the selected commuting mode and the exposure of nitrogen dioxide. The concentrations in the microenvironments were 22.4 μg/m3, 21.4 μg/m3, and 25.9 μg/m3 for cycling, walking and public transport respectively. Of all the modes of commuting, the highest average exposure of nitrogen dioxide was found travelling by public transport, while the lowest average concentration of NO2 was determined by walking.

Keywords: nitrogen dioxide, dispersion model, commuting mode, GPS

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Authors: Jeff Clarine, Chang-Shyh Peng, Daisy Sang

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Bioassay is the measurement of the potency of a chemical substance by its effect on a living animal or plant tissue. Bioassay data and chemical structures from pharmacokinetic and drug metabolism screening are mined from and housed in multiple databases. Bioassay prediction is calculated accordingly to determine further advancement. This paper proposes a four-step preprocessing of datasets for improving the bioassay predictions. The first step is instance selection in which dataset is categorized into training, testing, and validation sets. The second step is discretization that partitions the data in consideration of accuracy vs. precision. The third step is normalization where data are normalized between 0 and 1 for subsequent machine learning processing. The fourth step is feature selection where key chemical properties and attributes are generated. The streamlined results are then analyzed for the prediction of effectiveness by various machine learning algorithms including Pipeline Pilot, R, Weka, and Excel. Experiments and evaluations reveal the effectiveness of various combination of preprocessing steps and machine learning algorithms in more consistent and accurate prediction.

Keywords: bioassay, machine learning, preprocessing, virtual screen

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Authors: Muhammad Khalil, Nader Abuelfoutouh, Gasser Abdelal, Adrian Murphy

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Nowadays, the direct effects of lightning to aircrafts are of great importance because of the massive use of composite materials. In comparison with metallic materials, composites present several weaknesses for lightning strike direct effects. Especially, their low electrical and thermal conductivities lead to severe lightning strike damage. The lightning strike direct effects are burning, heating, magnetic force, sparking and arcing. As the problem is complex, we investigated it gradually. A magnetohydrodynamics (MHD) model is developed to simulate the lightning strikes in order to estimate the damages on the composite materials. Then, a coupled thermal-electrical finite element analysis is used to study the interaction between the lightning arc and the composite laminate and to investigate the material degradation.

Keywords: composite structures, lightning multiphysics, magnetohydrodynamic (MHD), coupled thermal-electrical analysis, thermal plasmas.

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Authors: Lavanya Madhuri Bollipo, K. V. Kadambari

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Objective: To discuss key issues in the diagnosis of Parkinson disease (PD), To discuss features influencing PD progression, To discuss importance of brain SPECT data in PD diagnosis, and To discuss the essentiality of machine learning techniques in early diagnosis of PD. An accurate and early diagnosis of PD is nowadays a challenge as clinical symptoms in PD arise only when there is more than 60% loss of dopaminergic neurons. So far there are no laboratory tests for the diagnosis of PD, causing a high rate of misdiagnosis especially when the disease is in the early stages. Recent neuroimaging studies with brain SPECT using 123I-Ioflupane (DaTSCAN) as radiotracer shown to be widely used to assist the diagnosis of PD even in its early stages. Machine learning techniques can be used in combination with image analysis procedures to develop computer-aided diagnosis (CAD) systems for PD. This paper addressed recent studies involving diagnosis of PD in its early stages using brain SPECT data with Machine Learning Techniques.

Keywords: Parkinson disease (PD), dopamine transporter, single-photon emission computed tomography (SPECT), support vector machine (SVM)

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Authors: Anne-Lena Kampen, Øivind Kure

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Machine learning (ML) can be implemented in Wireless Sensor Networks (WSNs) as a central solution or distributed solution where the ML is embedded in the nodes. Embedding improves privacy and may reduce prediction delay. In addition, the number of transmissions is reduced. However, quality factors such as prediction accuracy, fault detection efficiency and coordinated control of the overall system suffer. Here, we discuss and highlight the trade-offs that should be considered when choosing between embedding and centralized ML, especially for multihop networks. In addition, we present estimations that demonstrate the energy trade-offs between embedded and centralized ML. Although the total network energy consumption is lower with central prediction, it makes the network more prone for partitioning due to the high forwarding load on the one-hop nodes. Moreover, the continuous improvements in the number of operations per joule for embedded devices will move the energy balance toward embedded prediction.

Keywords: central machine learning, embedded machine learning, energy consumption, local machine learning, wireless sensor networks, WSN

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Authors: Mathieu Le Cam, Tejaswinee Darure, Mateusz Pawlucki

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Climate control of cabin aircraft is traditionally conditioned as a single unit by the environmental control system. Cabin temperature is controlled by the crew while passengers of the aircraft have control on the gaspers providing fresh air from the above head area. The small nozzles are difficult to reach and adjust to meet the passenger’s needs in terms of flow and direction. More dedicated control over the near environment of each passenger can be beneficial in many situations. The European project COCOON, funded under Clean Sky 2, aims at developing and demonstrating a microclimate conditioning module (MCM) integrated into a standard economy 3-seat row. The system developed will lead to improved passenger comfort with more control on their personal thermal area. This study focuses on the assessment of thermal comfort of passengers in the cabin aircraft through simulation on the TAITherm modelling platform. A first analysis investigates thermal comfort and sensation of passengers in varying cabin environmental conditions: from cold to very hot scenarios, with and without MCM installed in the seats. The modelling platform is also used to evaluate the impact of different physiologies of passengers on their thermal comfort as well as different seat locations. Under the current cabin conditions, a passenger of a 50th percentile body size is feeling uncomfortably cool due to the high velocity cabin air ventilation. The simulation shows that the in-seat MCM developed in COCOON project improves the thermal comfort of the passenger.

Keywords: cabin aircraft, in-seat HVAC, microclimate conditioning module, thermal comfort

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Authors: Juan C. Domínguez, Belén Del Saz-Orozco, María V. Alonso, Mercedes Oliet, Francisco Rodríguez

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In the present study, the kinetics of thermal degradation of a phenolic and lignin reinforced phenolic foams, and the lignin used as reinforcement were studied and the activation energies of their degradation processes were obtained by a DAEM model. The average values for five heating rates of the mean activation energies obtained were: 99.1, 128.2, and 144.0 kJ.mol-1 for the phenolic foam, 109.5, 113.3, and 153.0 kJ.mol-1 for the lignin reinforcement, and 82.1, 106.9, and 124.4 kJ. mol-1 for the lignin reinforced phenolic foam. The standard deviation ranges calculated for each sample were 1.27-8.85, 2.22-12.82, and 3.17-8.11 kJ.mol-1 for the phenolic foam, lignin and the reinforced foam, respectively. The DAEM model showed low mean square errors (< 1x10-5), proving that is a suitable model to study the kinetics of thermal degradation of the foams and the reinforcement.

Keywords: kinetics, lignin, phenolic foam, thermal degradation

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Authors: A. Mousavi, S. Sadegzadeh

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In this research, the effect of non-thermal micro plasma with non-Maxwellian distribution function on the one dimensional plasma photonic crystals containing alternate plasma-dielectric layers, has been studied. By using Kronig Penny model, the dispersion relation of electromagnetic modes for such a periodic structure is obtained. In this study we take two plasma photonic crystals with different dielectric layers: the first one with Silicon monoxide named PPCI, and the second one with Tellurium dioxide named PPCII. The effects of the plasma layer thickness and the material of the dielectric layer on the plasma photonic crystal band gaps have been illustrated in the dispersion relation and the group velocity figures. Results revealed that in such a system, the non-thermal plasma exerts stronger limit on the wave’s propagation. In another word, for the non-thermal plasma photonic crystals (NPPC), there are two distinct regions in the dispersion plot. The upper region consists of alternate band gaps in such a way that both width and length of the bands decrease gradually as the band gaps order increases. Whereas in the lower region where v_ph > 20 c (for PPCI), waves will not be allowed to propagate.

Keywords: band gap, dispersion relation, non-thermal plasma, plasma photonic crystal

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Authors: Sahil Kumar, Surinder Pal, Rahul Kapoor

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This paper is focused on to define the basic difference between hot and cold chamber high pressure die casting process which is not fully defined in a research before paper which we have studied. The pressure die casting is basically defined into two types (1) Hot chamber Die Casting (2) Cold chamber Die Casting. Cold chamber die casting is used for casting alloys that require high pressure and have a high melting temperature, such as brass, aluminum, magnesium, copper based alloys and other high melting point nonferrous alloys. Hot chamber die casting is suitable for casting zinc, tin, lead, and low melting point alloys. In hot chamber die casting machine, the molten metal is an integral pan of the machine. It mainly consists of hot chamber and gooseneck type metal container made of cast iron. This machine is mainly used for low melting alloys and alloys of metals like zinc, lead etc. Metals and alloys having a high melting point and those which are having an affinity for iron cannot be cast by this machine, which could otherwise attack the shot sleeve and damage the machine.

Keywords: hot chamber die casting, cold chamber die casting, metals and alloys, casting technology

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Authors: Yessica Nataliani, Miin-Shen Yang

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Cell formation (CF) is an important step in group technology. It is used in designing cellular manufacturing systems using similarities between parts in relation to machines so that it can identify part families and machine groups. There are many CF methods in the literature, but there is less spectral clustering used in CF. In this paper, we propose a spectral clustering algorithm for machine-part CF. Some experimental examples are used to illustrate its efficiency. Overall, the spectral clustering algorithm can be used in CF with a wide variety of machine/part matrices.

Keywords: group technology, cell formation, spectral clustering, grouping efficiency

Procedia PDF Downloads 375

Authors: A. Hassannia, S. Ramezani

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The technology of superconductivity has developed in many power system devices such as transmission cable, transformer, current limiter, motor and generator. Superconducting wires can carry high density current without loss, which is the capability that is used to design the compact, lightweight and more efficient electrical machines. Superconducting motors have found applications in marine and air propulsion systems as well as superconducting generators are considered in low power hydraulic and wind generators. This paper presents a rim-driven superconducting synchronous generator for hydraulic power plant. The rim-driven concept improves the performance of hydro turbine. Furthermore, high magnetic field that is produced by superconducting windings allows replacing the rotor core. As a consequent, the volume and weight of the machine is decreased significantly. In this paper, a 1 MW coreless rim-driven superconducting synchronous generator is designed. Main performance characteristics of the proposed machine are then evaluated using finite elements method and compared to an ordinary similar size synchronous generator.

Keywords: coreless machine, electrical machine design, hydraulic generator, rim-driven machine, superconducting generator

Procedia PDF Downloads 141

Authors: Chongsutthamani Sitthiwet, Praphatsorn Plerdsranoy, Palmarin Dansirima, Priew Eiamlamai, Oliver Utke, Rapee Utke

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Hydrogen storage tank containing compacted LiNH2-LiH is developed by doping with TiF₄ and multi-walled nanotubes (MWCNTs) to study kinetic properties. Transition metal-based catalyst (TiF₄) provides the catalytic effect on hydrogen dissociation/recombination, while MWCNTs benefit thermal conductivity and hydrogen permeability during de/rehydrogenation process. The Enhancement of dehydrogenation kinetics is observed from the single-step reaction at a narrower and lower temperature range of 150-350 ºC (100 ºC lower than the compacted LiNH₂-LiH without additives) as well as long plateau temperature and constant hydrogen flow rate (50 SCCM) up to 30 min during desorption. Besides, Hydrogen contents de/absorbed during 5-6 cycles increase from 1.90-2.40 to 3.10-4.70 wt. % H₂ (from 29 to up to 80 % of theoretical capacity). In the process, Li₅TiN₃ is detected upon cycling probably absorbs NH₃ to form Li₅TiN₃(NH₃)x, which is favoring hydrogen sorption properties of the LiNH₂-LiH system. Importantly, the homogeneous reaction mechanisms and performances are found at all positions inside the tank of compacted LiNH₂-LiH doped with TiF₄ and MWCNTs.

Keywords: carbon, hydride, kinetics, dehydrogenation

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Authors: Amit Arora, P. M. V. Subbarao, R. S. Agarwal

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This study attempts to find promising locations of upwash delta winglets for an inline finned tube heat exchanger. Later, location of winglets that delivers highest improvement in thermal performance is identified. Numerical results clearly showed that optimally located upwash delta winglets not only improved the thermal performance of fin area in tube wake and tubes, but also improved overall thermal performance of heat exchanger.

Keywords: apparent friction factor, delta winglet, fin and tube heat exchanger, longitudinal vortices

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Authors: Boroka Gács, Tibor Szolcsányi, Árpad Csathó

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Background: Individuals frequently show habituation to repeated noxious heat. However, given the defensive function of human pain processing, it is reasonable to assume that individuals imagine that they would become increasingly sensitive to repeated thermal pain stimuli. To the best of the authors' knowledge, no previous studies have, however, been addressed to this assumption. Therefore, in the current study, we investigated how healthy human individuals imagine the intensity of repeated thermal pain stimulations, and compared this with the intensity ratings given after physically induced thermal pain trials. Methods: Healthy participants (N = 20) gave pain intensity ratings in two conditions: imagined and real thermal pain. In the real pain condition thermal pain stimuli of two intensities (minimal and moderate pain) were delivered in four consecutive trials. The duration of the peak temperature was 20s, and stimulation was always delivered to the same location. In each trial, participants rated the pain intensity twice, 5s and 15s after the onset of the peak temperature. In the imagined pain condition, participants were subjected to a reference pain stimulus and then asked to imagine and rate the same sequence of stimulations as in the induced pain condition. Results: Ratings of imagined pain and physically induced pain followed opposite courses over repeated stimulation: Ratings of imagined pain indicated sensitization whereas ratings for physically induced pain indicated habituation. The findings were similar for minimal and moderate pain intensities. Conclusions: The findings suggest that, rather than habituating to pain, healthy individuals imagine that they would become increasingly sensitive to repeated thermal pain stimuli.

Keywords: habituation, imagined pain, pain perception, thermal stimulation

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Authors: Leila Momenzadeh, Alexander V. Evteev, Elena V. Levchenko, Tanvir Ahmed, Irina Belova, Graeme Murch

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In this work, the phonon thermal conductivity of f.c.c. Al is investigated in detail in the temperature range 100 – 900 K within the framework of equilibrium molecular dynamics simulations making use of the Green-Kubo formalism and one of the most reliable embedded-atom method potentials. It is found that the heat current auto-correlation function of the f.c.c. Al model demonstrates a two-stage temporal decay similar to the previously observed for f.c.c Cu model. After the first stage of decay, the heat current auto-correlation function of the f.c.c. Al model demonstrates a peak in the temperature range 100-800 K. The intensity of the peak decreases as the temperature increases. At 900 K, it transforms to a shoulder. To describe the observed two-stage decay of the heat current auto-correlation function of the f.c.c. Al model, we employ decomposition model recently developed for phonon-mediated thermal transport in a monoatomic lattice. We found that the electronic contribution to the total thermal conductivity of f.c.c. Al dominates over the whole studied temperature range. However, the phonon contribution to the total thermal conductivity of f.c.c. Al increases as temperature decreases. It is about 1.05% at 900 K and about 12.5% at 100 K.

Keywords: aluminum, gGreen-Kubo formalism, molecular dynamics, phonon thermal conductivity

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Authors: Seyfullah Madakbas, Hatice Birtane, Memet Vezir Kahraman

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In this study, we aimed to synthesize and characterize polyhedral oligomeric silsesquioxanes containing polyimide nanocomposite. Polyimide nanocomposites widely have been used in membranes in fuel cell, solar cell, gas filtration, sensors, aerospace components, printed circuit boards. Firstly, polyamic acid was synthesized and characterized by Fourier Transform Infrared. Then, polyhedral oligomeric silsesquioxanes containing polyimide nanocomposite was prepared with thermal imidization method. The obtained polyimide nanocomposite was characterized by Fourier Transform Infrared, Scanning Electron Microscope, Thermal Gravimetric Analysis and Differential Scanning Calorimetry. Thermal stability of polyimide nanocomposite was evaluated by thermal gravimetric analysis and differential scanning calorimetry. Surface morphology of composite samples was investigated by scanning electron microscope. The obtained results prove that successfully prepared polyhedral oligomeric silsesquioxanes are containing polyimide nanocomposite. The obtained nanocomposite can be used in many industries such as electronics, automotive, aerospace, etc.

Keywords: polyimide, nanocomposite, polyhedral oligomeric silsesquioxanes

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Authors: Chalakorn Chitsaart, Suchada Rianmora, Mann Rattana-Areeyagon, Wutichai Namjaiprasert

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G-code is the main factor in computer numerical control (CNC) machine for controlling the tool-paths and generating the profile of the object’s features. For obtaining high surface accuracy of the surface finish, non-stop operation is required for CNC machine. Recently, to design a new product, the strategy that concerns about a change that has low impact on business and does not consume lot of resources has been introduced. Cost and time for designing minor changes can be reduced since the traditional geometric details of the existing models are applied. In order to support this strategy as the alternative channel for machining operation, this research proposes the automatic generating codes for CNC milling operation. Using this technique can assist the manufacturer to easily change the size and the geometric shape of the product during the operation where the time spent for setting up or processing the machine are reduced. The algorithm implemented on MATLAB platform is developed by analyzing and evaluating the geometric information of the part. Codes are created rapidly to control the operations of the machine. Comparing to the codes obtained from CAM, this developed algorithm can shortly generate and simulate the cutting profile of the part.

Keywords: geometric shapes, milling operation, minor changes, CNC Machine, G-code, cutting parameters

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Authors: Ghada A. Alfattni

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Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates. 

Keywords: imbalanced datasets, SMOTE, machine learning, logistic regression, support vector machine, nearest neighbour

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Authors: Arezoo Hakimi, Afshin Farahbakhsh

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Herein, we comparison synthesized Fe3O4 using, hydrothermal method, Mechanochemical processes and solvent thermal method. The Hydrothermal Technique has been the most popular one, gathering interest from scientists and technologists of different disciplines, particularly in the last fifteen years. In the hydrothermal method Fe3O4 microspheres, in which many nearly monodisperse spherical particles with diameters of about 400nm, in the mechanochemical method regular morphology indicates that the particles are well crystallized and in the solvent thermal method Fe3O4 nanoparticles have good properties of uniform size and good dispersion.

Keywords: Fe3O4 nanoparticles, hydrothermal method, mechanochemical processes, solvent thermal method

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Authors: Valeria Piccioni, Matthias Leschok, Ina Cheibas, Illias Hischier, Benjamin Dillenburger, Arno Schlueter, Matthias Kohler, Fabio Gramazio

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The decarbonisation of the building sector requires the development of building components that provide energy efficiency while producing minimal environmental impact. Recent advancements in large-scale 3D printing have shown that it is possible to fabricate components with embedded performances that can be tuned for their specific application. We investigate the potential of polymer 3D printing for the fabrication of translucent facade components. In this study, we explore the effect of geometry on thermal insulation of printed cavity structures following a Hot Box test method. The experimental results are used to calibrate a finite-element simulation model which can support the informed design of 3D printed insulation structures. We show that it is possible to fabricate components providing thermal insulation ranging from 1.7 to 0.95 W/m2K only by changing the internal cavity distribution and size. Moreover, we identify design guidelines that can be used to fabricate components for different climatic conditions and thermal insulation requirements. The research conducted provides the first insights into the thermal behaviour of polymer 3DP facades on a large scale. These can be used as design guidelines for further research toward performant and low-embodied energy 3D printed facade components.

Keywords: 3D printing, thermal performance, polymers, facade components, hot-box method

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Authors: Fuad M. Alkoot

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The main component of a machine learning system is the classifier. Classifiers are mathematical models that can perform classification tasks for a specific application area. Additionally, many classifiers are combined using any of the available methods to reduce the classifier error rate. The benefits gained from the combination of multiple classifier designs has motivated the development of diverse approaches to multiple classifiers. We aim to investigate using fractal geometry to develop an improved classifier combiner. Initially we experiment with measuring the fractal dimension of data and use the results in the development of a combiner strategy.

Keywords: fractal geometry, machine learning, classifier, fractal dimension

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Authors: Bijay Kumar Sahoo

Abstract:

An important feature of InₓGa₁-ₓN/GaN heterostructures is strong built-in polarization (BIP) electric field at the hetero-interface due to spontaneous (sp) and piezoelectric (pz) polarizations. The intensity of this electric field reaches several MV/cm. This field has profound impact on optical, electrical and thermal properties. In this work, the effect of BIP field on thermal conductivity of InₓGa₁-ₓN/GaN heterostructure has been investigated theoretically. The interaction between the elastic strain and built in electric field induces additional electric polarization. This additional polarization contributes to the elastic constant of InₓGa₁-ₓN alloy. This in turn modifies material parameters of InₓGa₁-ₓN. The BIP mechanism enhances elastic constant, phonon velocity and Debye temperature and their bowing constants in InₓGa₁-ₓN alloy. These enhanced thermal parameters increase phonon mean free path which boost thermal conduction process. The thermal conductivity (k) of InxGa1-xN alloy has been estimated for x=0, 0.1, 0.3 and 0.9. Computation finds that irrespective of In content, the room temperature k of InₓGa₁-ₓN/GaN heterostructure is enhanced by BIP mechanism. Our analysis shows that at a certain temperature both k with and without BIP show crossover. Below this temperature k with BIP field is lower than k without BIP; however, above this temperature k with BIP field is significantly contributed by BIP mechanism leading to k with BIP field become higher than k without BIP field. The crossover temperature is primary pyroelectric transition temperature. The pyroelectric transition temperature of InₓGa₁-ₓN alloy has been predicted for different x. This signature of pyroelectric nature suggests that thermal conductivity can reveal pyroelectricity in InₓGa₁-ₓN alloy. The composition dependent room temperature k for x=0.1 and 0.3 are in line with prior experimental studies. The result can be used to minimize the self-heating effect in InₓGa₁-ₓN/GaN heterostructures.

Keywords: built-in polarization, phonon relaxation time, thermal properties of InₓGa₁-ₓN /GaN heterostructure, self-heating

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