Abstract: This article presents the application of the semi-analytic method (SAM) in the thermal management solution (TMS) of the energy storage system (ESS). The TMS studied in this work is fluid cooling. In fluid cooling, both effective heat conduction and heat convection are indispensable due to the heat transfer from solid to fluid. Correspondingly, an efficient TMS requires a design investigation of the following parameters: fluid inlet temperature, ESS initial temperature, fluid flow rate, working c rate, continuous working time, and materials properties. Their variation induces a change of thermal performance in the battery module, which is usually evaluated by numerical simulation. Compared to complicated computation resources and long computation time in simulation, the SAM is developed in this article to predict the thermal influence within a few seconds. In SAM, a fast prediction model is reckoned by combining numerical simulation with theoretical/empirical equations. The SAM can explore the thermal effect of boundary parameters in both steady-state and transient heat transfer scenarios within a short time. Therefore, the SAM developed in this work can simplify the design cycle of TMS and inspire more possibilities in TMS design.
Abstract: This paper presents and benchmarks a number of
end-to-end Deep Learning based models for metaphor detection in
Greek. We combine Convolutional Neural Networks and Recurrent
Neural Networks with representation learning to bear on the metaphor
detection problem for the Greek language. The models presented
achieve exceptional accuracy scores, significantly improving the
previous state-of-the-art results, which had already achieved accuracy
0.82. Furthermore, no special preprocessing, feature engineering or
linguistic knowledge is used in this work. The methods presented
achieve accuracy of 0.92 and F-score 0.92 with Convolutional
Neural Networks (CNNs) and bidirectional Long Short Term Memory
networks (LSTMs). Comparable results of 0.91 accuracy and 0.91
F-score are also achieved with bidirectional Gated Recurrent Units
(GRUs) and Convolutional Recurrent Neural Nets (CRNNs). The
models are trained and evaluated only on the basis of training tuples,
the related sentences and their labels. The outcome is a state-of-the-art
collection of metaphor detection models, trained on limited labelled
resources, which can be extended to other languages and similar
tasks.
Abstract: Airborne Laser Scanning (ALS) is one of the main technologies for generating high-resolution digital terrain models (DTMs). DTMs are crucial to several applications, such as topographic mapping, flood zone delineation, geographic information systems (GIS), hydrological modelling, spatial analysis, etc. Laser scanning system generates irregularly spaced three-dimensional cloud of points. Raw ALS data are mainly ground points (that represent the bare earth) and non-ground points (that represent buildings, trees, cars, etc.). Removing all the non-ground points from the raw data is referred to as filtering. Filtering heavily forested areas is considered a difficult and challenging task as the canopy stops laser pulses from reaching the terrain surface. This research presents an approach for removing non-ground points from raw ALS data in densely forested areas. Smoothing splines are exploited to interpolate and fit the noisy ALS data. The presented filter utilizes a weight function to allocate weights for each point of the data. Furthermore, unlike most of the methods, the presented filtering algorithm is designed to be automatic. Three different forested areas in the United Kingdom are used to assess the performance of the algorithm. The results show that the generated DTMs from the filtered data are accurate (when compared against reference terrain data) and the performance of the method is stable for all the heavily forested data samples. The average root mean square error (RMSE) value is 0.35 m.
Abstract: This paper presents open-loop vector control method of induction motor with space vector pulse width modulation (SVPWM) technique. Normally, the closed loop speed control is preferred and is believed to be more accurate. However, it requires a position sensor to track the rotor position which is not desirable to use it for certain workspace applications. This paper exhibits the performance of three-phase induction motor with the simplest control algorithm without the use of a position sensor nor an estimation block to estimate rotor position for sensorless control. The motor stator currents are measured and are transformed to synchronously rotating (d-q-axis) frame by use of Clarke and Park transformation. The actual control happens in this frame where the measured currents are compared with the reference currents. The error signal is fed to a conventional PI controller, and the corrected d-q voltage is generated. The controller outputs are transformed back to three phase voltages and are fed to SVPWM block which generates PWM signal for the voltage source inverter. The open loop vector control model along with SVPWM algorithm is modeled in MATLAB/Simulink software and is experimented and validated in TMS320F28335 DSP board.
Abstract: This paper presents the experimental design and needs justifications for a localized intelligent parking system (L-IPS), ideal for quasi-close communities with increasing vehicular volume that depends on limited or constant parking facilities. For a constant supply in parking facilities, the demand for an increasing vehicular volume could lead to poor time conservation or extended travel time, traffic congestion or impeded mobility, and safety issues. Increased negative environmental and economic externalities are other associated and consequent downsides of disparities in demand and supply. This L-IPS is designed using a microcontroller, ultrasonic sensors, LED indicators, such that the current status, in terms of parking spots availability, can be known from the main entrance to the community or a parking zone on a LCD screen. As an advanced traffic management system (ATMS), the L-IPS is designed to resolve aspects of infrastructure-to-driver (I2D) communication and parking detection issues. Thus, this L-IPS can act as a timesaver for users by helping them know the availability of parking spots. Providing on-time, informed routing, to a next preference or seamless moving to berth on the available spot on a proximate facility as the case may be. Its use could also increase safety and increase mobility, and fuel savings and costs, therefore, reducing negative environmental and economic externalities due to transportation systems.
Abstract: Digital Terrain Model (DTM) is a digital numerical representation of the Earth's surface. DTMs have been applied to a diverse field of tasks, such as urban planning, military, glacier mapping, disaster management. In the expression of the Earth' surface as a mathematical model, an infinite number of point measurements are needed. Because of the impossibility of this case, the points at regular intervals are measured to characterize the Earth's surface and DTM of the Earth is generated. Hitherto, the classical measurement techniques and photogrammetry method have widespread use in the construction of DTM. At present, RADAR, LiDAR, and stereo satellite images are also used for the construction of DTM. In recent years, especially because of its superiorities, Airborne Light Detection and Ranging (LiDAR) has an increased use in DTM applications. A 3D point cloud is created with LiDAR technology by obtaining numerous point data. However recently, by the development in image mapping methods, the use of unmanned aerial vehicles (UAV) for photogrammetric data acquisition has increased DTM generation from image-based point cloud. The accuracy of the DTM depends on various factors such as data collection method, the distribution of elevation points, the point density, properties of the surface and interpolation methods. In this study, the random data reduction method is compared for DTMs generated from image based point cloud data. The original image based point cloud data set (100%) is reduced to a series of subsets by using random algorithm, representing the 75, 50, 25 and 5% of the original image based point cloud data set. Over the ANS campus of Afyon Kocatepe University as the test area, DTM constructed from the original image based point cloud data set is compared with DTMs interpolated from reduced data sets by Kriging interpolation method. The results show that the random data reduction method can be used to reduce the image based point cloud datasets to 50% density level while still maintaining the quality of DTM.
Abstract: The use of biometric identifiers in the field of
information security, access control to resources, authentication in
ATMs and banking among others, are of great concern because of
the safety of biometric data. In the general architecture of a biometric
system have been detected eight vulnerabilities, six of them allow
obtaining minutiae template in plain text. The main consequence
of obtaining minutia templates is the loss of biometric identifier
for life. To mitigate these vulnerabilities several models to protect
minutiae templates have been proposed. Several vulnerabilities in the
cryptographic security of these models allow to obtain biometric data
in plain text. In order to increase the cryptographic security and ease
of reversibility, a minutiae templates protection model is proposed.
The model aims to make the cryptographic protection and facilitate
the reversibility of data using two levels of security. The first level
of security is the data transformation level. In this level generates
invariant data to rotation and translation, further transformation is
irreversible. The second level of security is the evaluation level,
where the encryption key is generated and data is evaluated using a
defined evaluation function. The model is aimed at mitigating known
vulnerabilities of the proposed models, basing its security on the
impossibility of the polynomial reconstruction.
Abstract: Background: Hypertensive response during maximal exercise test provides important information on the level of blood pressure control and evaluation of treatment. Method: A single center retrospective descriptive study was conducted among 117 young (aged 20 to 40) and middle age (aged 40 to 65) hypertensive patients, who underwent treadmill stress test. Currently on maintenance frontline medication either monotherapy (Angiotensin-converting enzyme inhibitor/Angiotensin receptor blocker [ACEi/ARB], Calcium channel blocker [CCB], Diuretic - Hydrochlorthiazide [HCTZ]) or combination therapy (ARB+CCB, ARB+HCTZ), who attained a maximal exercise on treadmill stress test (TMST) with hypertensive response (systolic blood pressure: male >210 mm Hg, female >190 mm Hg, diastolic blood pressure >100 mmHg, or increase of >10 mm Hg at any time during the test), on Bruce and Modified Bruce protocol. Exaggerated blood pressure response during exercise (systolic [SBP] and diastolic [DBP]), peak exercise blood pressure (SBP and DBP), recovery period (SBP and DBP) and test for ischemia and their antihypertensive medication/s were investigated. Analysis of variance and chi-square test were used for statistical analysis. Results: Hypertensive responses on maximal exercise test were seen mostly among female population (P < 0.000) and middle age (P < 0.000) patients. Exaggerated diastolic blood pressure responses were significantly lower in patients who were taking CCB (P < 0.004). A longer recovery period that showed a delayed decline in SBP was observed in patients taking ARB+HCTZ (P < 0.036). There were no significant differences in the level of exaggerated systolic blood pressure response and during peak exercise (both systolic and diastolic) in patients using either monotherapy or combination antihypertensives. Conclusion: Calcium channel blockers provided lower exaggerated diastolic BP response during maximal exercise test in hypertensive middle age patients. Patients on combination therapy using ARB+HCTZ exhibited a longer recovery period of systolic blood pressure.
Abstract: This paper investigates the use of atmospheric pressure plasma for improving the surface hydrophobicity of polyurethane synthetic leather with tetramethylsilane (TMS). The atmospheric pressure plasma treatment with TMS is a single-step process to enhance the hydrophobicity of polyurethane synthetic leather. The hydrophobicity of the treated surface was examined by contact angle measurement. The physical and chemical surface changes were evaluated by scanning electron microscopy (SEM) and infrared spectroscopy (FTIR). The purpose of this paper is to provide learning materials for understanding how to use atmospheric pressure plasma in the textile finishing process to transform a hydrophilic surface to hydrophobic.
Abstract: The identification of lipid and soluble sugar components in flour samples of different cultivars belonging to common oat species (Avena sativa L.) was performed: spring oat, winter oat and hulless oat. Fatty acids were extracted from flour samples with n-hexane, and derivatized into volatile methyl esters, using TMSH (trimethylsulfonium hydroxide in methanol). Soluble sugars were then extracted from defatted and dried samples of oat flour with 96% ethanol, and further derivatized into corresponding TMS-oximes, using hydroxylamine hydrochloride solution and BSTFA (N,O-bis-(trimethylsilyl)-trifluoroacetamide). The hexane and ethanol extracts of each oat cultivar were analyzed using GC-MS system. Lipid and simple sugar compositions are very similar in all samples of investigated cultivars. Chemometric tool was applied to numeric values of automatically integrated surface areas of detected lipid and simple sugar components in their corresponding derivatized forms. Hierarchical cluster analysis shows a very high similarity between the investigated flour samples of oat cultivars, according to the fatty acid content (0.9955). Moderate similarity was observed according to the content of soluble sugars (0.50). These preliminary results support the idea of establishing methods for oat flour authentication, and provide the means for distinguishing oat flour samples, regardless of the variety, from flour samples made of other cereal species, just by lipid and simple sugar profile analysis.
Abstract: In the standards of IEC 60076-2 and IEC 60076-7, three different hot-spot temperature estimation methods are suggested. In this study, the algorithms which used in hot-spot temperature calculations are analyzed by comparing the algorithms with the results of an experimental set-up made by a Transformer Monitoring System (TMS) in use. In tested system, TMS uses only top oil temperature and load ratio for hot-spot temperature calculation. And also, it uses some constants from standards which are on agreed statements tables. During the tests, it came out that hot-spot temperature calculation method is just making a simple calculation and not uses significant all other variables that could affect the hot-spot temperature.
Abstract: The learning process needs to be so pervasive to impart the quality in acquiring the knowledge about a subject by making use of the advancement in the field of information and communication systems. However, pervasive learning paradigms designed so far are system automation types and they lack in factual pervasive realm. Providing factual pervasive realm requires subtle ways of teaching and learning with system intelligence. Augmentation of intelligence with pervasive learning necessitates the most efficient way of representing knowledge for the system in order to give the right learning material to the learner. This paper presents a method of representing knowledge for Pervasive Toolroom Maintenance System (PTMS) in which a learner acquires sublime knowledge about the various kinds of tools kept in the toolroom and also helps for effective maintenance of the toolroom. First, we explicate the generic model of knowledge representation for PTMS. Second, we expound the knowledge representation for specific cases of toolkits in PTMS. We have also presented the conceptual view of knowledge representation using ontology for both generic and specific cases. Third, we have devised the relations for pervasive knowledge in PTMS. Finally, events are identified in PTMS which are then linked with pervasive data of toolkits based on relation formulated. The experimental environment and case studies show the accuracy and efficient knowledge representation of toolkits in PTMS.
Abstract: The increasing availability of information about earth
surface elevation (Digital Elevation Models DEM) generated from
different sources (remote sensing, Aerial Images, Lidar) poses the
question about how to integrate and make available to the most than
possible audience this huge amount of data. In order to exploit the potential of 3D elevation representation the
quality of data management plays a fundamental role. Due to the high
acquisition costs and the huge amount of generated data, highresolution
terrain surveys tend to be small or medium sized and
available on limited portion of earth. Here comes the need to merge
large-scale height maps that typically are made available for free at
worldwide level, with very specific high resolute datasets. One the
other hand, the third dimension increases the user experience and the
data representation quality, unlocking new possibilities in data
analysis for civil protection, real estate, urban planning, environment
monitoring, etc. The open-source 3D virtual globes, which are
trending topics in Geovisual Analytics, aim at improving the
visualization of geographical data provided by standard web services
or with proprietary formats. Typically, 3D Virtual globes like do not
offer an open-source tool that allows the generation of a terrain
elevation data structure starting from heterogeneous-resolution terrain
datasets. This paper describes a technological solution aimed to set
up a so-called “Terrain Builder”. This tool is able to merge
heterogeneous-resolution datasets, and to provide a multi-resolution
worldwide terrain services fully compatible with CesiumJS and
therefore accessible via web using traditional browser without any
additional plug-in.
Abstract: Lightweight and efficient structures have the aim to
enhance the efficiency of the components in various industries.
Toward this end, composites are one of the most widely used
materials because of durability, high strength and modulus, and low
weight. One type of the advanced composites is grid-stiffened
composite (GSC) structures, which have been extensively considered
in aerospace, automotive, and aircraft industries. They are one of the
top candidates for replacing some of the traditional components,
which are used here. Although there are a good number of published
surveys on the design aspects and fabrication of GSC structures, little
systematic work has been reported on their material modification to
improve their properties, to our knowledge. Matrix modification
using nanoparticles is an effective method to enhance the flexural
properties of the fibrous composites. In the present study, a silanecoupling
agent (3-glycidoxypropyltrimethoxysilane/3-GPTS) was
introduced onto the silica (SiO2) nanoparticle surface and its effects
on the three-point flexural response of isogrid E-glass/epoxy
composites were assessed. Based on the Fourier Transform Infrared
Spectrometer (FTIR) spectra, it was inferred that the 3-GPTS
coupling agent was successfully grafted onto the surface of SiO2
nanoparticles after modification. Flexural test revealed an
improvement of 16%, 14%, and 36% in stiffness, maximum load and
energy absorption of the isogrid specimen filled with 3 wt.% 3-
GPTS/SiO2 compared to the neat one. It would be worth mentioning
that in these structures, considerable energy absorption was observed
after the primary failure related to the load peak. In addition, 3-
GPTMS functionalization had a positive effect on the flexural
behavior of the multiscale isogrid composites. In conclusion, this
study suggests that the addition of modified silica nanoparticles is a
promising method to improve the flexural properties of the gridstiffened
fibrous composite structures.
Abstract: Automated Teller Machines (ATMs) can be
considered among one of the most important service facilities in the
banking industry. The investment in ATMs and the impact on the
banking industry is growing steadily in every part of the world. The
banks take into consideration many factors like safety, convenience,
visibility, and cost in order to determine the optimum locations of
ATMs. Today, ATMs are not only available in bank branches but
also at retail locations. Another important factor is the cash
management in ATMs. A cash demand model for every ATM is
needed in order to have an efficient cash management system. This
forecasting model is based on historical cash demand data which is
highly related to the ATMs location. So, the location and the cash
management problem should be considered together. This paper
provides a general review on studies, efforts and development in
ATMs location and cash management problem.
Abstract: Optic disk segmentation plays a key role in the mass
screening of individuals with diabetic retinopathy and glaucoma
ailments. An efficient hardware-based algorithm for optic disk
localization and segmentation would aid for developing an automated
retinal image analysis system for real time applications. Herein,
TMS320C6416DSK DSP board pixel intensity based fractal analysis
algorithm for an automatic localization and segmentation of the optic
disk is reported. The experiment has been performed on color and
fluorescent angiography retinal fundus images. Initially, the images
were pre-processed to reduce the noise and enhance the quality. The
retinal vascular tree of the image was then extracted using canny
edge detection technique. Finally, a pixel intensity based fractal
analysis is performed to segment the optic disk by tracing the origin
of the vascular tree. The proposed method is examined on three
publicly available data sets of the retinal image and also with the data
set obtained from an eye clinic. The average accuracy achieved is
96.2%. To the best of the knowledge, this is the first work reporting
the use of TMS320C6416DSK DSP board and pixel intensity based
fractal analysis algorithm for an automatic localization and
segmentation of the optic disk. This will pave the way for developing
devices for detection of retinal diseases in the future.
Abstract: Fast speed drives for Permanent Magnet Synchronous
Motor (PMSM) is a crucial performance for the electric traction
systems. In this paper, PMSM is derived with a Model-based
Predictive Control (MPC) technique. Fast speed tracking is achieved
through optimization of the DC source utilization using MPC. The
technique is based on predicting the optimum voltage vector applied
to the driver. Control technique is investigated by comparing to the
cascaded PI control based on Space Vector Pulse Width Modulation
(SVPWM). MPC and SVPWM-based FOC are implemented with the
TMS320F2812 DSP and its power driver circuits. The designed MPC
for a PMSM drive is experimentally validated on a laboratory test
bench. The performances are compared with those obtained by a
conventional PI-based system in order to highlight the improvements,
especially regarding speed tracking response.
Abstract: Ontologies offer a means for representing and sharing
information in many domains, particularly in complex domains. For
example, it can be used for representing and sharing information
of System Requirement Specification (SRS) of complex systems
like the SRS of ERTMS/ETCS written in natural language. Since
this system is a real-time and critical system, generic ontologies,
such as OWL and generic ERTMS ontologies provide minimal
support for modeling temporal information omnipresent in these SRS
documents. To support the modeling of temporal information, one
of the challenges is to enable representation of dynamic features
evolving in time within a generic ontology with a minimal redesign
of it. The separation of temporal information from other information
can help to predict system runtime operation and to properly design
and implement them. In addition, it is helpful to provide a reasoning
and querying techniques to reason and query temporal information
represented in the ontology in order to detect potential temporal
inconsistencies. To address this challenge, we propose a lightweight
3-layer temporal Quality of Service (QoS) ontology for representing,
reasoning and querying over temporal and non-temporal information
in a complex domain ontology. Representing QoS entities in separated
layers can clarify the distinction between the non QoS entities
and the QoS entities in an ontology. The upper generic layer of
the proposed ontology provides an intuitive knowledge of domain
components, specially ERTMS/ETCS components. The separation of
the intermediate QoS layer from the lower QoS layer allows us to
focus on specific QoS Characteristics, such as temporal or integrity
characteristics. In this paper, we focus on temporal information that
can be used to predict system runtime operation. To evaluate our
approach, an example of the proposed domain ontology for handover
operation, as well as a reasoning rule over temporal relations in this
domain-specific ontology, are presented.
Abstract: A process of conversion of flour from three varieties of cassava, namely Odongbo, ofege and TMS30752 to ethanol using α-amylase locally sourced from germinated unhusked paddy rice and yeast isolated from palm wine was developed. It involves the germination of paddy rice for a period of 15days to produce α-amylase for starch hydrolysis and isolation of yeast from palm wine for fermentation. The results showed that optimum amylase yield of “ofada” rice paddy was at 6th day germination which was 576.9ml/g. Ethanol yield for TMS30572 (440.3%) was significantly higher than “Odongbo” (160.2%) and “Ofege’’ (115.1%), Sugar conversion efficiency were 311.0%v/v, 268.2%v/v and 186.84%v/v for TMS30572, “Odongbo” and “Ofege” respectively. The ethanol boiling points were 78oC, 76oC and 80oC for TMS30572, “Odongbo” and “Ofege” respectively. This study showed that cassava varieties affects quality of ethanol produced and germination of “ofada” rice for 6 days ensures optimum production of crude amylase enzyme.
Abstract: This paper presents advances in pulse width modulation techniques which refers to a method of carrying information on train of pulses and the information be encoded in the width of pulses. Pulse Width Modulation is used to control the inverter output voltage. This is done by exercising the control within the inverter itself by adjusting the ON and OFF periods of inverter. By fixing the DC input voltage we get AC output voltage. In variable speed AC motors the AC output voltage from a constant DC voltage is obtained by using inverter. Recent developments in power electronics and semiconductor technology have lead improvements in power electronic systems. Hence, different circuit configurations namely multilevel inverters have became popular and considerable interest by researcher are given on them. A fast space-vector pulse width modulation (SVPWM) method for five-level inverter is also discussed. In this method, the space vector diagram of the five-level inverter is decomposed into six space vector diagrams of three-level inverters. In turn, each of these six space vector diagrams of three-level inverter is decomposed into six space vector diagrams of two-level inverters. After decomposition, all the remaining necessary procedures for the three-level SVPWM are done like conventional two-level inverter. The proposed method reduces the algorithm complexity and the execution time. It can be applied to the multilevel inverters above the five-level also. The experimental setup for three-level diode-clamped inverter is developed using TMS320LF2407 DSP controller and the experimental results are analyzed.