Search results for: automatic impedance matching

Authors: P. S. Jagadeesh Kumar, S. Meenakshi Sundaram, Wenli Hu, Yang Yung

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In 3D video games, the dominance of production is unceasingly growing with a protruding level of affordability in terms of budget. Afterward, the automation of speech-lip synchronization technique is customarily onerous and has advanced a critical research subject in virtual reality based 3D video games. This paper presents one of these automatic tools, precisely riveted on the synchronization of the speech and the lip movement of the game characters. A robust and precise speech recognition segment that systematized with Algebraic Code Excited Linear Prediction method is developed which unconventionally delivers lip sync results. The Algebraic Code Excited Linear Prediction algorithm is constructed on that used in code-excited linear prediction, but Algebraic Code Excited Linear Prediction codebooks have an explicit algebraic structure levied upon them. This affords a quicker substitute to the software enactments of lip sync algorithms and thus advances the superiority of service factors abridged production cost.

Keywords: algebraic code excited linear prediction, speech-lip synchronization, video games, virtual reality

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Authors: Jie Huo, Jonathan Wu

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Segmentation of anatomical structures in medical images is essential for scientific inquiry into the complex relationships between biological structure and clinical diagnosis, treatment and assessment. As a method of incorporating the prior knowledge and the anatomical structure similarity between a target image and atlases, multi-atlas segmentation has been successfully applied in segmenting a variety of medical images, including the brain, cardiac, and abdominal images. The basic idea of multi-atlas segmentation is to transfer the labels in atlases to the coordinate of the target image by matching the target patch to the atlas patch in the neighborhood. However, this technique is limited by the pairwise registration between target image and atlases. In this paper, a novel multi-atlas segmentation approach is proposed by introducing a dynamic energy model. First, the target is mapped to each atlas image by minimizing the dynamic energy function, then the segmentation of target image is generated by weighted fusion based on the energy. The method is tested on MICCAI 2012 Multi-Atlas Labeling Challenge dataset which includes 20 target images and 15 atlases images. The paper also analyzes the influence of different parameters of the dynamic energy model on the segmentation accuracy and measures the dice coefficient by using different feature terms with the energy model. The highest mean dice coefficient obtained with the proposed method is 0.861, which is competitive compared with the recently published method.

Keywords: brain MRI segmentation, dynamic energy model, multi-atlas segmentation, energy minimization

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Authors: Yu-Hsi Huang, Ying-Der Tsai

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Piezoelectric circular plates can provide out-of-plane vibrational displacements on low frequency and in-plane vibrational displacements on high frequency. Piezoelectric semi-spherical shell, which is double-curvature structure, can induce three-dimensional vibrational displacements over a large frequency range. In this study, three-dimensional vibrational characteristics of piezoelectric semi-spherical shells with free boundary conditions are investigated using three experimental methods and finite element numerical modeling. For the experimental measurements, amplitude-fluctuation electronic speckle pattern interferometry (AF-ESPI) is used to obtain resonant frequencies and radial and azimuthal mode shapes. This optical technique utilizes a full-field and non-contact optical system that measures both the natural frequency and corresponding vibration mode shape simultaneously in real time. The second experimental technique used, laser displacement meter is a point-wise displacement measurement method that determines the resonant frequencies of the piezoelectric shell. An impedance analyzer is used to determine the in-plane resonant frequencies of the piezoelectric semi-spherical shell. The experimental results of the resonant frequencies and mode shapes for the piezoelectric shell are verified with the result from finite element analysis. Excellent agreement between the experimental measurements and numerical calculation is presented on the three-dimensional vibrational characteristics of the piezoelectric semi-spherical shell.

Keywords: piezoelectric semi-spherical shell, mode shape, resonant frequency, electronic speckle pattern interferometry, radial vibration, azimuthal vibration

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Authors: Rahmi Yetri Kasri, Paulus Wirutomo

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Citizen engagement approach has become increasingly important in the rural water sector. However, the question remains as to what exactly is meant by citizen engagement and how this approach can lead to sustainable service delivery. To understand citizen engagement, this paper argues that we need to understand basic elements of social life that consist of social structure, process, and culture within the realm of community’s living environment. Extracting from empirical data from Pamsimas villages in rural West Java, Indonesia, this paper will identify basic elements of social life and environment that influence and form the engagement of citizen and government in delivering and sustaining rural water supply services in Indonesia. Pamsimas or the Water Supply and Sanitation for Low Income Communities project is the biggest rural water program in Indonesia, implemented since 1993 in more than 27,000 villages. The sustainability of this sector is explored through a rural water supply service delivery life-cycle, starts with capital investment, operational and maintenance, asset expansion or renewal, strategic planning for future services and matching cost with financing. Using mixed-method data collection in case study research, this paper argues that increased citizen engagement contributes to a more sustainable rural water service delivery.

Keywords: citizen engagement, rural water supply, sustainability, Indonesia

Procedia PDF Downloads 266

Authors: Henrik L. Funke, Lars Ulke-Winter, Sandra Gelbrich, Lothar Kroll

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This work reports about an approach for an automatic adaptation of concrete formulations based on genetic algorithms (GA) to optimize a wide range of different fit-functions. In order to achieve the goal, a method was developed which provides a numerical description of a fibre reinforced concrete (FRC) mixture regarding the production technology and the property spectrum of the concrete. In a first step, the FRC mixture with seven fixed components was characterized by varying amounts of the components. For that purpose, ten concrete mixtures were prepared and tested. The testing procedure comprised flow spread, compressive and bending tensile strength. The analysis and approximation of the determined data was carried out by GAs. The aim was to obtain a closed mathematical expression which best describes the given seven-point cloud of FRC by applying a Gene Expression Programming with Free Coefficients (GEP-FC) strategy. The seven-parametric FRC-mixtures model which is generated according to this method correlated well with the measured data. The developed procedure can be used for concrete mixtures finding closed mathematical expressions, which are based on the measured data.

Keywords: concrete design, fibre reinforced concrete, genetic algorithms, GEP-FC

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Authors: Maureen Montalban

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The military is a predominantly male-dominated organisation that has entrenched hierarchical and patriarchal norms. Since 1975, women have been allowed to continue active service in the Australian Defence Force during pregnancy and after the birth of a child; prior to this time, pregnancy was grounds for automatic termination. The military and family, as institutions, make great demands on individuals with respect to their commitment, loyalty, time and energy. This research explores what it means to serve in the Australian Army as a woman through a gender lens, overlaid during a specific time period of their service; that is, during pregnancy, birth, and being a mother. It investigates the external demands faced by servicewomen who are mothers, whether it be from society, the Army, their teammates, their partners, or their children; and how they internally make sense of that with respect to their own identity and role as a mother, servicewoman, partner and as an individual. It also seeks to uncover how Australian Army servicewomen who are also mothers attempt to manage the dilemma of serving two greedy institutions when both expect and demand so much and whether this is, in fact, an impossible dilemma.

Keywords: women's health, gender studies, military culture, identity

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Authors: Abder-Rahman Ali, Antoine Vacavant, Manuel Grand-Brochier, Adélaïde Albouy-Kissi, Jean-Yves Boire

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In this paper, we present a new segmentation approach for liver lesions in regions of interest within MRI (Magnetic Resonance Imaging). This approach, based on a two-cluster Fuzzy C-Means methodology, considers the parameter variable compactness to handle uncertainty. Fine boundaries are detected by a local recursive merging of ambiguous pixels with a sequential forward floating selection with Zernike moments. The method has been tested on both synthetic and real images. When applied on synthetic images, the proposed approach provides good performance, segmentations obtained are accurate, their shape is consistent with the ground truth, and the extracted information is reliable. The results obtained on MR images confirm such observations. Our approach allows, even for difficult cases of MR images, to extract a segmentation with good performance in terms of accuracy and shape, which implies that the geometry of the tumor is preserved for further clinical activities (such as automatic extraction of pharmaco-kinetics properties, lesion characterization, etc).

Keywords: defuzzification, floating search, fuzzy clustering, Zernike moments

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Authors: Rhea Kapoor

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Fractal dimension provides a way to characterize non-geometric shapes like those found in nature. The purpose of this research is to estimate Minkowski fractal dimension of human lung images for early detection of lung cancer. Lung cancer is the leading cause of death among all types of cancer and an early histopathological analysis will help reduce deaths primarily due to late diagnosis. A Python application program, PathoPy2.0, was developed for analyzing medical images in pixelated format and estimating Minkowski fractal dimension using a new box-counting algorithm that allows windowing of images for more accurate calculation in the suspected areas of cancerous growth. Benchmark geometric fractals were used to validate the accuracy of the program and changes in fractal dimension of lung images to indicate the presence of issues in the lung. The accuracy of the program for the benchmark examples was between 93-99% of known values of the fractal dimensions. Fractal dimension values were then calculated for lung images, from National Cancer Institute, taken over time to correctly detect the presence of cancerous growth. For example, as the fractal dimension for a given lung increased from 1.19 to 1.27 due to cancerous growth, it represents a significant change in fractal dimension which lies between 1 and 2 for 2-D images. Based on the results obtained on many lung test cases, it was concluded that fractal dimension of human lungs can be used to diagnose lung cancer early. The ideas behind PathoPy2.0 can also be applied to study patterns in the electrical activity of the human brain and DNA matching.

Keywords: fractals, histopathological analysis, image processing, lung cancer, Minkowski dimension

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Authors: Kiurski S. Jelena, Kecić S. Vesna, Aksentijević M. Snežana

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Samples of tap and wastewater were collected in three offset printing facilities in Novi Sad, Serbia. Ten physicochemical parameters were analyzed within all collected samples: pH, conductivity, m - alkalinity, p - alkalinity, acidity, carbonate concentration, hydrogen carbonate concentration, active oxygen content, chloride concentration and total alkali content. All measurements were conducted using the standard analytical and instrumental methods. Comparing the obtained results for tap water and wastewater, a clear quality difference was noticeable, since all physicochemical parameters were significantly higher within wastewater samples. The study also involves the application of simple linear regression analysis on the obtained dataset. By using software package ORIGIN 5 the pH value was mutually correlated with other physicochemical parameters. Based on the obtained values of Pearson coefficient of determination a strong positive correlation between chloride concentration and pH (r = -0.943), as well as between acidity and pH (r = -0.855) was determined. In addition, statistically significant difference was obtained only between acidity and chloride concentration with pH values, since the values of parameter F (247.634 and 182.536) were higher than F_critical (5.59). In this way, results of statistical analysis highlighted the most influential parameter of water contamination in offset printing, in the form of acidity and chloride concentration. The results showed that variable dependence could be represented by the general regression model: y = a₀ + a₁x+ k, which further resulted with matching graphic regressions.

Keywords: pollution, printing industry, simple linear regression analysis, wastewater

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Authors: Dawood Al Chanti, Alice Caplier

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Sparse representations of signals have received a great deal of attention in recent years. In this paper, the interest of using sparse representation as a mean for performing sparse discriminative analysis between spontaneous facial expressions is demonstrated. An automatic facial expressions recognition system is presented. It uses a KSVD-SVM approach which is made of three main stages: A pre-processing and feature extraction stage, which solves the problem of shared subspace distribution based on the random projection theory, to obtain low dimensional discriminative and reconstructive features; A dictionary learning and sparse coding stage, which uses the KSVD model to learn discriminative under or over dictionaries for sparse coding; Finally a classification stage, which uses a SVM classifier for facial expressions recognition. Our main concern is to be able to recognize non-basic affective states and non-acted expressions. Extensive experiments on the JAFFE static acted facial expressions database but also on the DynEmo dynamic spontaneous facial expressions database exhibit very good recognition rates.

Keywords: dictionary learning, random projection, pose and spontaneous facial expression, sparse representation

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Authors: Temitope O. Tokosi

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Healthcare is a human right. The sensitivity of health issues has necessitated the introduction of Enterprise Content Management (ECM) at district hospitals in the Western Cape Province of South Africa. The objective is understanding clinicians’ perception of ECM at their workplace. It is a descriptive case study design of constructivist paradigm. It employed a phenomenological data analysis method using a pattern matching deductive based analytical procedure. Purposive and s4nowball sampling techniques were applied in selecting participants. Clinicians expressed concerns and frustrations using ECM such as, non-integration with other hospital systems. Inadequate access points to ECM. Incorrect labelling of notes and bar-coding causes more time wasted in finding information. System features and/or functions (such as search and edit) are not possible. Hospital management and clinicians are not constantly interacting and discussing. Information turnaround time is unacceptably lengthy. Resolving these problems would involve a positive working relationship between hospital management and clinicians. In addition, prioritising the problems faced by clinicians in relation to relevance can ensure problem-solving in order to meet clinicians’ expectations and hospitals’ objective. Clinicians’ perception should invoke attention from hospital management with regards technology use. The study’s results can be generalised across clinician groupings exposed to ECM at various district hospitals because of professional and hospital homogeneity.

Keywords: clinician, electronic content management, hospital, perception, technology

Procedia PDF Downloads 232

Authors: Jun Hong Lee

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Background: Alzheimer`s disease (AD) I: creases with age and is characterized by the premature progressive loss of neuronal cell. In contrast, cancer cells have inappropriate cell proliferation and resistance to cell death. Objective: We evaluated the association between cancer and AD and also examined the specific types of cancer. Patients and Methods/Material and Methods: This retrospective, nationwide, longitudinal study used National Health Insurance Service – Senior cohort (NHIS-Senior) 2002-2013, which was released by the KNHIS in 2016, comprising 550,000 random subjects who were selected from over than 60. The study included a cohort of 4,408 patients who were first diagnoses as AD between 2003 and 2005. To match each dementia patient, 19,150 subjects were selected from the database by Propensity Score Matching. Results: We enrolled 4,790 patients for analysis in this cohort and the prevalence of AD was higher in female (19.29%) than in male (17.71%). A higher prevalence of AD was observed in the 70-84 year age group and in the higher income status group. A total of 540 cancers occurred within the observation interval. Overall cancer was less frequent in those with AD (12.25%) than in the control (18.46%), with HR 0.704 (95% Confidence Intervals (CIs)=0.0.64-0.775, p-Value < 0.0001). Conclusion: Our data showed a decreased incidence of overall cancers in patients with AD similar to previous studies. Patients with AD had a significantly decreased risk of colon & rectum, lung and stomach cancer. This finding lower than but consistent with Western countries. We need further investigation of genetic evidence linking AD to cancer.

Keywords: Alzheimer, cancer, nationwide, longitudinal study

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Authors: Atanu K Samanta, Asim Ali Khan

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Due to the acquisition of huge amounts of brain tumor magnetic resonance images (MRI) in clinics, it is very difficult for radiologists to manually interpret and segment these images within a reasonable span of time. Computer-aided diagnosis (CAD) systems can enhance the diagnostic capabilities of radiologists and reduce the time required for accurate diagnosis. An intelligent computer-aided technique for automatic detection of a brain tumor through MRI is presented in this paper. The technique uses the following computational methods; the Level Set for segmentation of a brain tumor from other brain parts, extraction of features from this segmented tumor portion using gray level co-occurrence Matrix (GLCM), and the Artificial Neural Network (ANN) to classify brain tumor images according to their respective types. The entire work is carried out on 50 images having five types of brain tumor. The overall classification accuracy using this method is found to be 98% which is significantly good.

Keywords: brain tumor, computer-aided diagnostic (CAD) system, gray-level co-occurrence matrix (GLCM), tumor segmentation, level set method

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Authors: Chao Yi, Cunyue Lu, Lingwei Quan

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Piezoelectric linear motors have the characteristics of great electromagnetic compatibility, high positioning accuracy, compact structure and no deceleration mechanism, which make it promising to applicate in micro-miniature precision drive systems. However, most piezoelectric motors are employed by flexible clamping, which has insufficient rigidity and is difficult to use in rapid positioning. Another problem is that this clamping method seriously affects the vibration efficiency of the vibrating unit. In order to solve these problems, this paper proposes a piezoelectric stack linear motor based on double-end rigid clamping. First, a piezoelectric linear motor with a length of only 35.5 mm is designed. This motor is mainly composed of a motor stator, a driving foot, a ceramic friction strip, a linear guide, a pre-tightening mechanism and a base. This structure is much simpler and smaller than most similar motors, and it is easy to assemble as well as to realize precise control. In addition, the properties of piezoelectric stack are reviewed and in order to obtain the elliptic motion trajectory of the driving head, a driving scheme of the longitudinal-shear composite stack is innovatively proposed. Finally, impedance analysis and speed performance testing were performed on the piezoelectric linear motor prototype. The motor can measure speed up to 25.5 mm/s under the excitation of signal voltage of 120 V and frequency of 390 Hz. The result shows that the proposed piezoelectric stacked linear motor obtains great performance. It can run smoothly in a large speed range, which is suitable for various precision control in medical images, aerospace, precision machinery and many other fields.

Keywords: piezoelectric stack, linear motor, rigid clamping, elliptical trajectory

Procedia PDF Downloads 152

Authors: Tiago do Carmo Nogueira, Cássio Dener Noronha Vinhal, Gélson da Cruz Júnior, Matheus Rudolfo Diedrich Ullmann

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Generating automatic image descriptions through natural language is a challenging task. Image captioning is a task that consistently describes an image by combining computer vision and natural language processing techniques. To accomplish this task, cutting-edge models use encoder-decoder structures. Thus, Convolutional Neural Networks (CNN) are used to extract the characteristics of the images, and Recurrent Neural Networks (RNN) generate the descriptive sentences of the images. However, cutting-edge approaches still suffer from problems of generating incorrect captions and accumulating errors in the decoders. To solve this problem, we propose a model based on the encoder-decoder structure, introducing a module that generates the weights according to the importance of the word to form the sentence, using the part-of-speech (PoS). Thus, the results demonstrate that our model surpasses state-of-the-art models.

Keywords: gated recurrent units, caption generation, convolutional neural network, part-of-speech

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Authors: I. Karagjozova, B. Dejanova, J. Pluncevic, S. Petrovska, V. Antevska, L. Todorovska

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Introduction: Medical checking assessment is important in sports medicine. To follow the health condition in subjects who perform sports, body composition parameters, such as intracellular water, extracellular water, protein and mineral content, muscle and fat mass might be useful. The aim of the study was to show available parameters and to compare them to manual assessment. Material and methods: A number of 20 subjects (14 male and 6 female) at age of 20±2 years were determined in the study, 5 performed recreational sports, while others were professional ones. The mean height was 175±7 cm, the mean weight was 72±9 cm, and the body mass index (BMI) was 23±2 kg/m2. The measured compartments were as following: intracellular water (IW), extracellular water (EW), protein component (PC), mineral component (MC), skeletal muscle mass (SMM) and body fat mass (BFM). Lean balance were examined for right and left arm (LA), trunk (T), right leg (RL) and left leg (LL). The comparison was made between the calculation derived by manual made measurements, using Matejka formula and parameters obtained by body composition analyzer (BCA) - Inbody 720 BCA Biospace. Used parameters for the comparison were muscle mass (SMM), body fat mass (BFM). Results: BCA obtained values were for: IW - 22.6±5L, EW - 13.5±2 L, PC - 9.8±0.9 kg, MC - 3.5±0.3, SMM - 27±3 kg, BFM - 13.8±4 kg. Lean balance showed following values for: RA - 2.45±0.2 kg, LA - 2.37±0.4, T - 20.9±5 kg, RL - 7.43±1 kg, and LL - 7.49 ±1.5 kg. SMM showed statistical difference between manual obtained value, 51±01% to BCA parameter 45.5±3% (p<0.001). Manual obtained values for BFM was lower (17±2%) than BCA obtained one, 19.5±5.9% (p<0.02). Discussion: The obtained results showed appropriate values for the examined age, regarding to all examined parameters which contribute to overview the body compartments, important for sport performing. Due to comparison between the manual and BCA assessment, we may conclude that manual measurements may differ from the certain ones, which is confirmed by statistical significance.

Keywords: athletes, body composition, bio electrical impedance, sports medicine

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Authors: Shari Ann S. Botin, Ruziel Larmae T. Gimpaya, Rembrant Rockwell Gamboa, Rinlee Butch M. Cervera

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In recent years, demand for the use of solid electrolytes as alternatives to liquid electrolytes has increased due to recurring battery safety and stability issues, in addition to an increase in energy density requirement which can be made possible by using solid electrolytes. Among the solid electrolyte systems, Li7La3Zr2O12 (LLZ) is one of the most promising as it exhibits good chemical stability against Li metal and has a relatively high ionic conductivity. In this study, pure and doped LLZ were synthesized via conventional solid state reaction. The precursor chemicals (such as LiOH, La2O3, Ga2O3 and ZrO2) were ground and then calcined at 900 °C, pressed into pellets and finally sintered at 1000 °C to 1200 °C. The microstructure and ionic conductivity of the obtained samples have been investigated. Results show that for pure LLZ, sintering at lower temperature (1000 °C) produced tetragonal LLZ while sintering at higher temperatures (≥ 1150 °C) produced cubic LLZ based from the XRD results. However, doping with Ga produces an easier formation of LLZ with cubic structure at lower sintering duration. On the other hand, the lithium conductivity of the samples was investigated using electrochemical impedance spectroscopy at room temperature. Among the obtained samples, Ga-doped LLZ sintered at 1150 °C obtained the highest ionic conductivity reaching to about 1x10⁻⁴ S/cm at room temperature. In addition, fabrication and initial investigation of an all-solid state Lithium Battery using the synthesized LLZ sample with the use of commercial cathode materials have been investigated.

Keywords: doped LLZ, lithium-ion battery, pure LLZ, solid electrolytes

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Authors: Vahid Bayrami Rad

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In recent years, the use of artificial intelligence techniques, tools, and methods in processing medical images and health-related applications has been highlighted and a lot of research has been done in this regard. For example, colonoscopy and diagnosis of colon lesions are some cases in which the process of diagnosis of lesions can be improved by using image processing and artificial intelligence algorithms, which help doctors a lot. Due to the lack of accurate measurements and the variety of injuries in colonoscopy images, the process of diagnosing the type of lesions is a little difficult even for expert doctors. Therefore, by using different software and image processing, doctors can be helped to increase the accuracy of their observations and ultimately improve their diagnosis. Also, by using automatic methods, the process of diagnosing the type of disease can be improved. Therefore, in this paper, a deep learning framework called KCBA is proposed to classify colonoscopy lesions which are composed of several methods such as K-means clustering, a bag of features and deep auto-encoder. Finally, according to the experimental results, the proposed method's performance in classifying colonoscopy images is depicted considering the accuracy criterion.

Keywords: colorectal cancer, colonoscopy, region of interest, narrow band imaging, texture analysis, bag of feature

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Authors: Volkan Ayik

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Loosening as a result of torque loss in bolted joints is one of the most encountered problems resulting in loss of connection between parts. The main reason for this is the dynamic loads to which the joints are subjected while the vehicle is moving. In particular, vibration-induced loads can loosen the joints in any size and geometry. The aim of this study is to study an improved method due to road-induced vibration in heavy commercial vehicles for estimating the vibration performance of bolted joints of the components connected to the chassis, before conducting prototype level vehicle structural strength tests on a proving ground. The frequency and displacements caused by the road conditions-induced vibration loads have been determined for the parts connected to the chassis, and various experimental design scenarios have been formed by matching specific components and vibration behaviors. In the studies, the performance of the torque, washer, test displacement, and test frequency parameters were observed by maintaining the connection characteristics on the vehicle, and the sensitivity ratios for these variables were calculated. As a result of these experimental design findings, tests performed on a developed device based on Junker’s vibration device and proving ground conditions versus test correlation levels were found.

Keywords: bolted joints, junker’s test, loosening failure, torque loss

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Authors: Güzi̇n Ti̇rkeş, Özge Teki̇n, Kerem Kurtuluş, Y. Yekta Yurtseven, Murat Baran

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A scanning electron microscope (SEM) is a type of electron microscope mainly used in nanoscience and nanotechnology areas. Automatic image recognition and classification are among the general areas of application concerning SEM. In line with these usages, the present paper proposes a deep learning algorithm that classifies SEM images into nine categories by means of an online application to simplify the process. The NFFA-EUROPE - 100% SEM data set, containing approximately 21,000 images, was used to train and test the algorithm at 80% and 20%, respectively. Validation was carried out using a separate data set obtained from the Middle East Technical University (METU) in Turkey. To increase the accuracy in the results, the Inception ResNet-V2 model was used in view of the Fine-Tuning approach. By using a confusion matrix, it was observed that the coated-surface category has a negative effect on the accuracy of the results since it contains other categories in the data set, thereby confusing the model when detecting category-specific patterns. For this reason, the coated-surface category was removed from the train data set, hence increasing accuracy by up to 96.5%.

Keywords: convolutional neural networks, deep learning, image classification, scanning electron microscope

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Authors: Allmin Susaiyah, Aki Härmä, Milan Petković

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Automatic generation of insights from data using insight mining systems (IMS) is useful in many applications, such as personal health tracking, patient monitoring, and business process management. Existing IMS face challenges in controlling insight extraction, scaling to large databases, and generalising to unseen domains. In this work, we propose a hybrid approach consisting of rule-based and neural components for generating insights from data while overcoming the aforementioned challenges. Firstly, a rule-based data 2CNL component is used to extract statistically significant insights from data and represent them in a controlled natural language (CNL). Secondly, a BERTSum-based CNL2NL component is used to convert these CNLs into natural language texts. We improve the model using task-specific and domain-specific fine-tuning. Our approach has been evaluated using statistical techniques and standard evaluation metrics. We overcame the aforementioned challenges and observed significant improvement with domain-specific fine-tuning.

Keywords: data mining, insight mining, natural language generation, pre-trained language models

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Authors: Kaan Karaoglu, Raif Bayir

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In this study, a cartesian robot is developed to improve the performance and efficiency of wireless charging of electric vehicles. The cartesian robot has three axes, each of which moves linearly. Magnetic positioning is used to align the cartesian robot transmitter charging pad. There are two different wireless charging methods, static and dynamic, for charging electric vehicles. The current state of charge information (SOC State of Charge) and location information are received wirelessly from the electric vehicle. Based on this information, the power to be transmitted is determined, and the transmitter and receiver charging pads are aligned for maximum efficiency. With this study, a fully automated cartesian robot structure will be used to charge electric vehicles with the highest possible efficiency. With the wireless communication established between the electric vehicle and the charging station, the charging status will be monitored in real-time. The cartesian robot developed in this study is a fully automatic system that can be easily used in static wireless charging systems with vehicle-machine communication.

Keywords: electric vehicle, wireless charging systems, energy efficiency, cartesian robot, location detection, trajectory planning

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Authors: Pooja Verma, Sumana Ghosh

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There is a growing need for alternative way of power generation worldwide. The reason can be attributed to limited resources of fossil fuels, environmental pollution, increasing cost of conventional fuels, and lower efficiency of conversion of energy in existing systems. In this context, one of the potential alternatives for power generation is wave energy. However, it is difficult to estimate the amount of electrical energy generation in an irregular sea condition by experiment and or analytical methods. Therefore in this work, a numerical wave tank is developed using the computational fluid dynamics software Open FOAM. In this software a specific utility known as waves2Foam utility is being used to carry out the simulation work. The computational domain is a tank of dimension: 5m*1.5m*1m with a floating object of dimension: 0.5m*0.2m*0.2m. Regular waves are generated at the inlet of the wave tank according to Stokes second order theory. The main objective of the present study is to validate the numerical model against existing experimental data. It shows a good matching with the existing experimental data of floater displacement. Later the model is exploited to estimate energy extraction due to the movement of such a point absorber in real sea conditions. Scale down the wave properties like wave height, wave length, etc. are used as input parameters. Seasonal variations are also considered.

Keywords: OpenFOAM, numerical wave tank, regular waves, floating object, point absorber

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Authors: B. Kurbanova, M. Karibayev, N. Almas, K. Ospanov, K. Aimaganbetov, T. Kuanyshbekov, K. Akatan, S. Kabdrakhmanova

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Proton exchange membranes (PEMs) operating at high temperatures above 100 °C with the excellent mechanical, chemical and thermochemical stability have been received much attention, because of their practical application of proton exchange membrane fuel cells (PEMFCs). Nowadays, a huge number of polymers and polymer-mixed various membranes have been investigated for this application, all of which offer both pros and cons. However, PEMFCs are still lack of ideal membranes with unique properties. In this work, carboxymethylcellulose (CMC) based membranes with dispersive graphene oxide (GO) sheets were fabricated and investigated for PEMFCs application. These membranes and pristine GO were studied by a combination of XRD, XPS, Raman, Brillouin, FTIR, thermo-mechanical analysis (TGA and Dynamic Mechanical Analysis) and SEM microscopy, while substantial studies on the proton transport properties were provided by Electrochemical Impedance Spectroscopy (EIS) measurements. It was revealed that the addition of CMC to the GO boosts proton conductivity of the whole membrane, while GO provides good mechanical and thermomechanical stability to the membrane. Further, the continuous and ordered nanochannels with well-tailored chemical structures were obtained by irradiation of heavy ions Kr⁺¹⁷ with an energy of 1.75 MeV/nucleon on the heavy ion accelerator. The formation of these nanochannels led to the significant increase of proton conductivity at 50% Relative Humidity. Also, FTIR and XPS measurement results show that ion irradiation eliminated the GO’s surface oxygen chemical bonds (C=O, C-O), and led to the formation of C = C, C – C bonds, whereas these changes connected with an increase in conductivity.

Keywords: proton exchange membranes, graphene oxide, fuel cells, carboxymethylcellulose, ion irradiation

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Authors: Basant H. El-Refay, Nabeel T. Faiad

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Background: The high prevalence of obesity in Egypt has a great impact on the health care system, economic and social situation. Evidence suggests that even a moderate amount of weight loss can be useful. Aim of the study: To analyze the effects of lower body positive pressure supported treadmill training, conducted with hypocaloric diet, on body composition of obese children. Methods: Thirty children aged between 8 and 14 years, were randomly assigned into two groups: intervention group (15 children) and control group (15 children). All of them were evaluated using body composition analysis through bioelectric impedance. The following parameters were measured before and after the intervention: body mass, body fat mass, muscle mass, body mass index (BMI), percentage of body fat and basal metabolic rate (BMR). The study group exercised with antigravity treadmill three times a week during 2 months, and participated in a hypocaloric diet program. The control group participated in a hypocaloric diet program only. Results: Both groups showed significant reduction in body mass, body fat mass and BMI. Only study group showed significant reduction in percentage of body fat (p = 0.0.043). Changes in muscle mass and BMR didn't reach statistical significance in both groups. No significant differences were observed between groups except for muscle mass (p = 0.049) and BMR (p = 0.042) favoring study group. Conclusion: Both programs proved effective in the reduction of obesity indicators, but lower body positive pressure supported treadmill training was more effective in improving muscle mass and BMR.

Keywords: children, hypocaloric diet, lower body positive pressure supported treadmill, obesity

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Authors: Royan Dawud Aldian, Endah Purwanti, Soegianto Soelistiono

Abstract:

In this research we have been developed an automatic investigation to classify normal children voice or autistic by using modern computation technology that is computation based on artificial neural network. The superiority of this computation technology is its capability on processing and saving data. In this research, digital voice features are gotten from the coefficient of linear-predictive coding with auto-correlation method and have been transformed in frequency domain using fast fourier transform, which used as input of artificial neural network in back-propagation method so that will make the difference between normal children and autistic automatically. The result of back-propagation method shows that successful classification capability for normal children voice experiment data is 100% whereas, for autistic children voice experiment data is 100%. The success rate using back-propagation classification system for the entire test data is 100%.

Keywords: autism, artificial neural network, backpropagation, linier predictive coding, fast fourier transform

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Authors: V. Marzal, J. C. Torres, B. Garcia-Camara, Manuel Cano-Garcia, Xabier Quintana, I. Perez Garcilopez, J. M. Sanchez-Pena

Abstract:

At the present time, the use of nanostructures in complex media, like liquid crystals, is widely extended to manipulate their properties, either electrical or optical. In addition, these media can also be used to control the optical properties of the nanoparticles, for instance when they are resonant. In this work, the change on electrical properties of a liquid crystal cell by adding TiO₂ nanoparticles on one of the alignment layers has been analyzed. These nanoparticles, with a diameter of 100 nm and spherical shape, were deposited in one of the substrates (ITO + polyimide) by spin-coating in order to produce a homogeneous layer. These substrates were checked using an optical microscope (objective x100) to avoid potential agglomerates. The liquid crystal cell is then fabricated, using one of these substrates and another without nanoparticles, and filled with E7. The study of the electrical response was done through impedance measurements in a long range of frequencies (3 Hz- 6 MHz) and at ambient temperature. Different nanoparticle concentrations were considered, as well as pure E7 and an empty cell for comparison purposes. Results about the effective dielectric permittivity and conductivity are presented along with models of equivalent electric circuits and its physical interpretation. As a summary, it has been observed the clear influence of the presence of the nanoparticles, strongly modifying the electric response of the device. In particular, a variation of both the effective permittivity and the conductivity of the device have been observed. This result requires a deep analysis of the effect of these nanoparticles on the trapping of free ions in the device, allowing a controlled manipulation and frequency tuning of the electrical response of these devices.

Keywords: alignment layer, electrical behavior, liquid crystal, TiO₂ nanoparticles

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Authors: Elysia Barker, Jian Guo Zhou, Ling Qian, Steve Decent

Abstract:

A method of modelling topography used in the simulation of riverbeds is proposed in this paper, which removes the need for datapoints and measurements of physical terrain. While complex scans of the contours of a surface can be achieved with other methods, this requires specialised tools, which the proposed method overcomes by using fractional Brownian motion (FBM) as a basis to estimate the real surface within a 15% margin of error while attempting to optimise algorithmic efficiency. This removes the need for complex, expensive equipment and reduces resources spent modelling bed topography. This method also accounts for the change in topography over time due to erosion, sediment transport, and other external factors which could affect the topography of the ground by updating its parameters and generating a new bed. The lattice Boltzmann method (LBM) is used to simulate both stationary and steady flow cases in a side-by-side comparison over the generated bed topography using the proposed method and a test case taken from an external source. The method, if successful, will be incorporated into the current LBM program used in the testing phase, which will allow an automatic generation of topography for the given situation in future research, removing the need for bed data to be specified.

Keywords: bed topography, FBM, LBM, shallow water, simulations

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Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan

Abstract:

The most aggressive form of brain tumor is called glioma. Glioma is kind of tumor that arises from glial tissue of the brain and occurs quite often. A fully automatic 2D-CNN model for brain tumor segmentation is presented in this paper. We performed pre-processing steps to remove noise and intensity variances using N4ITK and standard intensity correction, respectively. We used Keras open-source library with Theano as backend for fast implementation of CNN model. In addition, we used BRATS 2015 MRI dataset to evaluate our proposed model. Furthermore, we have used SimpleITK open-source library in our proposed model to analyze images. Moreover, we have extracted random 2D patches for proposed 2D-CNN model for efficient brain segmentation. Extracting 2D patched instead of 3D due to less dimensional information present in 2D which helps us in reducing computational time. Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.77 for complete, 0.76 for core, 0.77 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.

Keywords: brain tumor segmentation, convolutional neural networks, deep learning, LGG

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Authors: Angelo Gabriel Buenaventura, Allan Christopher Yago

Abstract:

A carbon paste electrode (CPE) composed of Multiwalled Carbon Nanotube (MWCNT) conducting particle and Polydimethylsiloxane (PDMS) binder was used for simultaneous detection of Dopamine (DA) and Uric Acid (UA) in the presence of Ascorbic Acid (AA) at physiological level. The MWCNT-PDMS CPE was initially activated via potentiodynamic cycling in a basic (NaOH) solution, which resulted in enhanced electrochemical properties. Electrochemical Impedance Spectroscopy measurements revealed a significantly lower charge transfer resistance (Rct) for the OH--activated MWCNT-PDMS CPE (Rct = 5.08kΩ) as compared to buffer (pH 7)-activated MWCNT-PDMS CPE (Rct = 25.9kΩ). Reversibility analysis of Fe(CN)63-/4- redox couple of both Buffer-Activated CPE and OH--Activated CPE showed that the OH—Activated CPE have peak current ratio (Ia/Ic) of 1.11 at 100mV/s while 2.12 for the Buffer-Activated CPE; this showed an electrochemically reversible behavior for Fe(CN)63-/4- redox couple even at relatively fast scan rate using the OH--activated CPE. Enhanced voltammetric signal for DA and significant peak separation between DA and UA was obtained using the OH--activated MWCNT-PDMS CPE in the presence of 50 μM AA via Differential Pulse Voltammetry technique. The anodic peak currents which appeared at 0.263V and 0.414 V were linearly increasing with increasing concentrations of DA and UA, respectively. The linear ranges were obtained at 25 μM – 100 μM for both DA and UA. The detection limit was determined to be 3.86 μM for DA and 5.61 μM for UA. These results indicate a practical approach in the simultaneous detection of important bio-organic molecules using a simple CPE composed of MWCNT and PDMS with base anodization as activation technique.

Keywords: anodization, ascorbic acid, carbon paste electrodes, dopamine, uric acid

Procedia PDF Downloads 280