Search results for: extended Kalman filter

Authors: Frank Kuebler, Rolf Steinhilper

Abstract:

Artificial neural networks (ANN) as well as Design of Experiments (DOE) based regression analysis (RA) are mainly used for modeling of complex systems. Both methodologies are commonly applied in process and quality control of manufacturing processes. Due to the fact that resource efficiency has become a critical concern for manufacturing companies, these models needs to be extended to predict resource-consumption of manufacturing processes. This paper describes an approach to use neural networks as well as DOE based regression analysis for predicting resource consumption of manufacturing processes and gives a comparison of the achievable results based on an industrial case study of a turning process.

Keywords: artificial neural network, design of experiments, regression analysis, resource efficiency, manufacturing process

Procedia PDF Downloads 523

Authors: Kamel Al-Khaled

Abstract:

Reaction-diffusion equations are commonly used in population biology to model the spread of biological species. In this paper, we propose a fractional reaction-diffusion equation, where the classical second derivative diffusion term is replaced by a fractional derivative of order less than two. Based on the symbolic computation system Mathematica, Adomian decomposition method, developed for fractional differential equations, is directly extended to derive explicit and numerical solutions of space fractional reaction-diffusion equations. The fractional derivative is described in the Caputo sense. Finally, the recent appearance of fractional reaction-diffusion equations as models in some fields such as cell biology, chemistry, physics, and finance, makes it necessary to apply the results reported here to some numerical examples.

Keywords: fractional partial differential equations, reaction-diffusion equations, adomian decomposition, biological species

Procedia PDF Downloads 374

Authors: Matthieu-P. Schapranow

Abstract:

Recruitment of participants for clinical trials requires the screening of a big number of potential candidates, i.e. the testing for trial-specific inclusion and exclusion criteria, which is a time-consuming and complex task. Today, a significant amount of time is spent on identification of adequate trial participants as their selection may affect the overall study results. We introduce a unique patient eligibility metric, which allows systematic ranking and classification of candidates based on trial-specific filter criteria. Our web application enables real-time analysis of patient data and assessment of candidates using freely definable inclusion and exclusion criteria. As a result, the overall time required for identifying eligible candidates is tremendously reduced whilst additional degrees of freedom for evaluating the relevance of individual candidates are introduced by our contribution.

Keywords: in-memory technology, clinical trials, screening, eligibility metric, data analysis, clustering

Procedia PDF Downloads 492

Authors: Dalila Oulhaci, Mohammed Zahaf

Abstract:

The watering technique is essential to maintain a moist perimeter around the roots of the crop. This is the case with topical watering, where the soil around the root system can be kept permanently moist between the two extremes of water content. Moreover, one of the oldest methods used since Roman times throughout North Africa and the Near East was based on the repeated pouring of water into porous earthen vessels buried in the ground. In this context, these two techniques have been combined by replacing the earthen vase with plastic bottles filled with sand which release water through their perforated walls into the surrounding soil. The objective of this work is to first determine the purifying power of the activated sludge treatment plant of Toggourt and then that of the bottled Sawdust filter. For the station, the BOD purification rate was (96.5%), the COD purification rate was (87%) and suspended solids (90%). For the bottle, the BOD removal rate was (35%), and COD removal rate was (12.58%). This work falls within the framework of water saving, sustainable development and environmental protection, and also within the framework of agriculture.

Keywords: wasterwater, sawdust, purification, irrigation, touggourt (Algeria)

Procedia PDF Downloads 84

Authors: Hari Pulapaka

Abstract:

This paper defines and develops the notion of a traceability index for food and may be used by any consumer (restaurant, distributor, average consumer etc.). The concept is then extended to a region's food system as a way to measure how well a regional food system utilizes its own bounty or at least, is connected to its food sources. With increasing emphases on the sustainability of aspects of regional and ultimately, the global food system, it is reasonable to accept that if we know how close (in relative terms) an end-user of a set of ingredients (as they traverse through the maze of supply chains) is from the sources, we may be better equipped to evaluate the quality of the set as measured by any number of qualitative and quantitative criteria. We propose a mathematical model which may be adapted to a number of contexts and sizes. Two hypothetical cases of different scope are presented which highlight how the model works as an evaluator of steps between an end-user and the source(s) of the ingredients they consume. The variables in the model are flexible enough to be adapted to other applications beyond food systems.

Keywords: food, traceability, supply chain, mathematical model

Procedia PDF Downloads 272

Authors: Klaus Peter Scherer, Daniel Knöll, Constantin Rieder

Abstract:

Problem and Purpose: Intelligent systems are available and helpful to support the human being decision process, especially when complex surgical eye interventions are necessary and must be performed. Normally, such a decision support system consists of a knowledge-based module, which is responsible for the real assistance power, given by an explanation and logical reasoning processes. The interview based acquisition and generation of the complex knowledge itself is very crucial, because there are different correlations between the complex parameters. So, in this project (semi)automated self-learning methods are researched and developed for an enhancement of the quality of such a decision support system. Methods: For ophthalmic data sets of real patients in a hospital, advanced data mining procedures seem to be very helpful. Especially subgroup analysis methods are developed, extended and used to analyze and find out the correlations and conditional dependencies between the structured patient data. After finding causal dependencies, a ranking must be performed for the generation of rule-based representations. For this, anonymous patient data are transformed into a special machine language format. The imported data are used as input for algorithms of conditioned probability methods to calculate the parameter distributions concerning a special given goal parameter. Results: In the field of knowledge discovery advanced methods and applications could be performed to produce operation and patient related correlations. So, new knowledge was generated by finding causal relations between the operational equipment, the medical instances and patient specific history by a dependency ranking process. After transformation in association rules logically based representations were available for the clinical experts to evaluate the new knowledge. The structured data sets take account of about 80 parameters as special characteristic features per patient. For different extended patient groups (100, 300, 500), as well one target value as well multi-target values were set for the subgroup analysis. So the newly generated hypotheses could be interpreted regarding the dependency or independency of patient number. Conclusions: The aim and the advantage of such a semi-automatically self-learning process are the extensions of the knowledge base by finding new parameter correlations. The discovered knowledge is transformed into association rules and serves as rule-based representation of the knowledge in the knowledge base. Even more, than one goal parameter of interest can be considered by the semi-automated learning process. With ranking procedures, the most strong premises and also conjunctive associated conditions can be found to conclude the interested goal parameter. So the knowledge, hidden in structured tables or lists can be extracted as rule-based representation. This is a real assistance power for the communication with the clinical experts.

Keywords: an expert system, knowledge-based support, ophthalmic decision support, self-learning methods

Procedia PDF Downloads 252

Authors: Syed Mahbubuz Zaman, A. B. M. Abrar Haque, Mehedi Hassan Nayeem, Misbah Uddin Sagor

Abstract:

Everyday e-mails are being used by millions of people as an effective form of communication over the Internet. Although e-mails allow high-speed communication, there is a constant threat known as spam. Spam e-mail is often called junk e-mails which are unsolicited and sent in bulk. These unsolicited emails cause security concerns among internet users because they are being exposed to inappropriate content. There is no guaranteed way to stop spammers who use static filters as they are bypassed very easily. In this paper, a smart system is proposed that will be using neural networks to approach spam in a different way, and meanwhile, this will also detect the most relevant features that will help to design the spam filter. Also, a comparison of different parameters for different neural network models has been shown to determine which model works best within suitable parameters.

Keywords: long short-term memory, bidirectional long short-term memory, gated recurrent unit, natural language processing, natural language processing

Procedia PDF Downloads 204

Authors: Mahdi Bazarganigilani

Abstract:

Computer-aided diagnosis (CAD) systems can play crucial roles in diagnosing crucial diseases such as breast cancer at the earliest. In this paper, a CAD system for the diagnosis of breast cancer was introduced and evaluated. This CAD system was developed by using spatio-temporal analysis of data on a set of consecutive thermographic images by employing wavelet transformation. By using this analysis, a very accurate machine learning model using random forest was obtained. The final results showed a promising accuracy of 91% in terms of the F1 measure indicator among 200 patients' sample data. The CAD system was further extended to obtain a detailed analysis of the effect of smaller sub-areas of each breast on the occurrence of cancer.

Keywords: computer-aided diagnosis systems, thermographic analysis, spatio-temporal analysis, image processing, machine learning

Procedia PDF Downloads 209

Authors: Csaba Kalman

Abstract:

In spite of the existence of numerous second language (L2) motivation theories, the teacher’s role in motivating learners has remained an under-researched niche to this day. If we narrow down our focus on the teacher’s role on motivating adult learners of English in an English as a Foreign Language (EFL) context in corporate environments, empirical research is practically non-existent. This study fills the above research niche by exploring the most motivating aspects of the teacher’s personality, behaviour, and teaching practices that affect adult learners’ L2 motivation in corporate contexts in Hungary. The study was conducted in a wide range of industries in 18 organisations that employ over 250 people in Hungary. In order to triangulate the research, 21 human resources managers, 18 language teachers, and 466 adult learners of English were involved in the investigation by participating in interview studies, and quantitative questionnaire studies that measured ten scales related to the teacher’s role, as well as two criterion measure scales of intrinsic and extrinsic motivation. The qualitative data were analysed using a template organising style, while descriptive, inferential statistics, as well as multivariate statistical techniques, such as correlation and regression analyses, were used for analysing the quantitative data. The results showed that certain aspects of the teacher’s personality (thoroughness, enthusiasm, credibility, and flexibility), as well as preparedness, incorporating English for Specific Purposes (ESP) in the syllabus, and focusing on the present, proved to be the most salient aspects of the teacher’s motivating influence. The regression analyses conducted with the criterion measure scales revealed that 22% of the variance in learners’ intrinsic motivation could be explained by the teacher’s preparedness and appearance, and 23% of the variance in learners’ extrinsic motivation could be attributed to the teacher’s personal branding and incorporating ESP in the syllabus. The findings confirm the pivotal role teachers play in motivating L2 learners independent of the context they teach in; and, at the same time, call for further research so that we can better conceptualise the motivating influence of L2 teachers.

Keywords: adult learners, corporate contexts, motivation, teacher’s role

Procedia PDF Downloads 103

Authors: Nevena Jakovčević Stor, Ivan Slapničar

Abstract:

Any polynomial can be expressed as a characteristic polynomial of a complex symmetric arrowhead matrix. This expression is not unique. If the polynomial is real with only real distinct roots, the matrix can be chosen as real. By using accurate forward stable algorithm for computing eigen values of real symmetric arrowhead matrices we derive a forward stable algorithm for computation of roots of such polynomials in O(n^2 ) operations. The algorithm computes each root to almost full accuracy. In some cases, the algorithm invokes extended precision routines, but only in the non-iterative part. Our examples include numerically difficult problems, like the well-known Wilkinson’s polynomials. Our algorithm compares favorably to other method for polynomial root-finding, like MPSolve or Newton’s method.

Keywords: roots of polynomials, eigenvalue decomposition, arrowhead matrix, high relative accuracy

Procedia PDF Downloads 415

Authors: Jaeyoung Lee

Abstract:

Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: edge network, embedded network, MMA, matrix multiplication accelerator, semantic segmentation network

Procedia PDF Downloads 128

Authors: C. P. Autry, A. W. Roscoe, Mykhailo Magal

Abstract:

We discuss the theoretical model underlying 2BPA (two-band peer authentication), a practical alternative to conventional authentication of entities and data in IoT. In essence, this involves assembling a virtual map of authentication assets in the network, typically leading to many paths of confirmation between any pair of entities. This map is continuously updated, confirmed, and evaluated. The value of authentication along multiple disjoint paths becomes very clear, and we require analogues of triangulation to extend authentication along extended paths and deliver it along all possible paths. We discover that if an attacker wants to make an honest node falsely believe she has authenticated another, then the length of the authentication paths is of little importance. This is because optimal attack strategies correspond to minimal cuts in the authentication graph and do not contain multiple edges on the same path. The authentication provided by disjoint paths normally is additive (in entropy).

Keywords: authentication, edge computing, industrial IoT, post-quantum resistance

Procedia PDF Downloads 195

Authors: Chen Xi, Lao Xuerui, Li Junjie, Jiang Yike, Wang Hanwei, Zeng Zihao

Abstract:

To further promote the development of smart cities, the microscopic "nerve endings" of the City Intelligent Model (CIM) are extended to be more sensitive. In this paper, we develop a pedestrian trajectory twin terminal based on the CIM and CNN technology. It also uses 5G networks, architectural and geoinformatics technologies, convolutional neural networks, combined with deep learning networks for human behaviour recognition models, to provide empirical data such as 'pedestrian flow data and human behavioural characteristics data', and ultimately form spatial performance evaluation criteria and spatial performance warning systems, to make the empirical data accurate and intelligent for prediction and decision making.

Keywords: urban planning, urban governance, CIM, artificial intelligence, convolutional neural network

Procedia PDF Downloads 145

Authors: Syazwani Idrus, S. Charles J. Banks, Sonia Heaven

Abstract:

The study compared the performance of upflow anaerobic sludge blanket (UASB) reactors and anaerobic filters (AF) for the treatment of wheat straw washwater (WSW) which has a high concentration of Potassium ions. The trial was conducted at mesophilic temperatures (37 °C). The digesters were started up over a 48-day period using a synthetic wastewater feed and reached an organic loading rate (OLR) of 6 g COD L^-1 day^-1 with a specific methane production (SMP) of 0.333 L CH4 g^-1 COD. When the feed was switched to WSW it was not possible to maintain the same loading rate as the SMP in all reactors fell sharply to less than 0.1 L CH4 g^-1 COD, with the AF affected more than the UASB. On reducing the OLR to 3 g COD L^-1 day^-1 the reactors recovered to produce 0.21 L CH4 g^-1 CODadded and gave 82% COD removal. A discrepancy between the COD consumed and the methane produced could be accounted for through increased maintenance energy requirement of the microbial community for osmo-regulation as K+ was found to accumulate in the sludge and in the UASB reached a concentration of 4.5 mg K g^-1 wet weight of granules.

Keywords: anaerobic digestion, osmotic stress, chemical oxygen demand, specific methane production

Procedia PDF Downloads 654

Authors: Maulik Parmar, Sumeet Sandhu

Abstract:

The online fashion industry experiences high product return rates. Many returns are because of size/fit mismatches -the size scale on labels can vary across brands, the size parameters may not capture all fit measurements, or the product may have manufacturing defects. Warehouse quality check of garment sizes can be semi-automated to improve speed and accuracy. This paper presents an approach for automatically measuring garment sizes from a single image of the garment -using Deep Learning to learn garment keypoints. The paper focuses on the waist size measurement of jeans and can be easily extended to other garment types and measurements. Experimental results show that this approach can greatly improve the speed and accuracy of today’s manual measurement process.

Keywords: convolutional neural networks, deep learning, distortion, garment measurements, image warping, keypoints

Procedia PDF Downloads 307

Authors: Kyle De Freitas, Margaret Bernard

Abstract:

Enabling data mining analysis among a wider audience of educators is an active area of research within the educational data mining (EDM) community. The paper proposes a framework for developing an environment that caters for educators who have little technical data mining skills as well as for more advanced users with some data mining expertise. This framework architecture was developed through the review of the strengths and weaknesses of existing models in the literature. The proposed framework provides a modular architecture for future researchers to focus on the development of specific areas within the EDM process. Finally, the paper also highlights a strategy of enabling analysis through either the use of predefined questions or a guided data mining process and highlights how the developed questions and analysis conducted can be reused and extended over time.

Keywords: educational data mining, learning management system, learning analytics, EDM framework

Procedia PDF Downloads 324

Authors: Somnath Bhattacharya, Kamal Sharma, Vaibhav Sonkar

Abstract:

Composite materials emerged in the middle of the 20th century as a promising class of engineering materials providing new prospects for modern technology. Recently, a new class of composite materials known as functionally graded materials (FGMs) has drawn considerable attention of the scientific community. In general, FGMs are defined as composite materials in which the composition or microstructure or both are locally varied so that a certain variation of the local material properties is achieved. This gradual change in composition and microstructure of material is suitable to get gradient of properties and performances. FGMs are synthesized in such a way that they possess continuous spatial variations in volume fractions of their constituents to yield a predetermined composition. These variations lead to the formation of a non-homogeneous macrostructure with continuously varying mechanical and / or thermal properties in one or more than one direction. Lightweight functionally graded composites with high strength to weight and stiffness to weight ratios have been used successfully in aircraft industry and other engineering applications like in electronics industry and in thermal barrier coatings. In the present work, elastic-plastic crack growth problems (using Ramberg-Osgood Model) in an FGM plate under cyclic load has been explored by extended finite element method. Both edge and centre crack problems have been solved by taking additionally holes, inclusions and minor cracks under plane stress conditions. Both soft and hard inclusions have been implemented in the problems. The validity of linear elastic fracture mechanics theory is limited to the brittle materials. A rectangular plate of functionally graded material of length 100 mm and height 200 mm with 100% copper-nickel alloy on left side and 100% ceramic (alumina) on right side is considered in the problem. Exponential gradation in property is imparted in x-direction. A uniform traction of 100 MPa is applied to the top edge of the rectangular domain along y direction. In some problems, domain contains major crack along with minor cracks or / and holes or / and inclusions. Major crack is located the centre of the left edge or the centre of the domain. The discontinuities, such as minor cracks, holes, and inclusions are added either singly or in combination with each other. On the basis of this study, it is found that effect of minor crack in the domain’s failure crack length is minimum whereas soft inclusions have moderate effect and the effect of holes have maximum effect. It is observed that the crack growth is more before the failure in each case when hard inclusions are present in place of soft inclusions.

Keywords: elastic-plastic, fatigue crack, functionally graded materials, extended finite element method (XFEM)

Procedia PDF Downloads 389

Authors: Sungroul Kim, Yeonjin Kim

Abstract:

This study was conducted by comparing the concentrations of heavy metals analyzed from the same samples with three X-Ray fluorescence (XRF) spectrometer in three different global research institutions, including PAN (A Branch of Malvern Panalytical, Seoul, South Korea), RTI (Research Triangle Institute, NC, U.S.A), and aerosol laboratory in Harvard University, Boston, U.S.A. To achieve our research objectives, the indoor air filter samples were collected at homes (n=24) of adults or child asthmatics then analyzed in PAN followed by Harvard University and RTI consecutively. Descriptive statistics were conducted for data comparison as well as correlation and simple regression analysis using R version 4.0.3. As a result, detection rates of most heavy metals analyzed in three institutions were about 90%. Of the 25 elements commonly analyzed among those institutions, 16 elements showed an R² (coefficient of determination) of 0.7 or higher (10 components were 0.9 or higher). The findings of this study demonstrated that XRF was a useful device ensuring reproducibility and compatibility for measuring heavy metals in PM2.5 collected from indoor air of asthmatics’ home.

Keywords: heavy metals, indoor air quality, PM2.5, X-ray fluorescence

Procedia PDF Downloads 199

Authors: M. Jegannath, V. Akshaya, B. Arunkumar, G. Lakshmi Soundharya, V. Thenmozhi, S. Varun, V. R. S. Kumar

Abstract:

For scarf inlet design, the primary variable of interest is the circumferential extent over which the extended lower lip is formed. In this paper, an attempt has been made to optimize the aerodynamic shape of a subsonic scarf inlet with aerodynamically shaped center-body with a particular value of the circumferential extent. The parametric analytical studies have been carried out using a Spalart-Allmaras turbulence model. From our preliminary studies, we concluded that for a particular value of circumferential extent, there will be an exact shape of the center-body with certain geometric orientation for the existence of an aerodynamically efficient scarf inlet for modern aircraft engines. This numerical study is a pointer towards for the design optimization of scarf inlets for modern aircraft engines.

Keywords: aerodynamics of scarf inlets, inlet design, modern aircraft inlets, subsonic scarf inlet

Procedia PDF Downloads 315

Authors: Pyung Soo Kim

Abstract:

This paper proposes an optimized reactive fast intermobility anchor point (MAP) handover scheme for Hierarchical Mobile IPv6, called the ORFH-HMIPv6, to minimize packet loss of the existing scheme. The key idea of the proposed ORFHHMIPv6 scheme is that the serving MAP buffers packets toward the mobile node (MN) as soon as the link layer between MN and serving base station is disconnected. To implement the proposed scheme, the MAP discovery message exchanged between MN and serving MAP is extended. In addition, the IEEE 802.21 Media Independent Handover Function (MIHF) event service message is defined newly. Through analytic performance evaluation, the proposed ORFH-HMIPv6 scheme can be shown to minimize packet loss much than the existing scheme.

Keywords: hierarchical mobile IPv6 (HMIPv6), fast handover, reactive behavior, packet loss

Procedia PDF Downloads 211

Authors: S. F. Afzali, W. L. Wong

Abstract:

The epizootic ulcerative syndrome (EUS) causes by the oomycete fungus, Aphanomyces invadans; known to be one of the infectious fish diseases for farmed and wild fishes in fresh and brackish-water from the Asia-pacific region, America and Africa. Although, EUS had been documented by the Office International des Epizooties (OIE) since 1995, hitherto, there is neither standard chemical agents that can be used for successful treatment of this destructive infection in the time of outbreak; nor available vaccine for prevention. Plant-based remedies in controlling fish diseases are gaining much attention recently as an alternative to chemical treatments, which possess negative effects to the environment and human. In present study, Sonneratia alba, a mangrove plant belongs to the Sonneratiaceae family, was screened in vitro and in vivo for its antifungal activity against A. invadans mycelium growth and its effects on fish innate immune system and disease resistant. The in vitro tests was performed using the disc diffusion methods with measurements of minimum inhibitory concentration (MIC) and inhibition zone. For in vivo study, the S. alba extract supplemented diets were administrated at 0.0, 1.0%, 3.0%, and 5.0% on healthy goldfish, Carassius auratus, which challenged with A. invadans zoospores (100 spores/ml). To compare the significant differences in the hematological and immunological parameters obtained from the experiments, the data were analysed using the SPSS. The methanol extract of S. alba effectively inhibited the mycelial growth of A. invadans at a minimum concentration of 1000 ppm for agar and filter paper diffusion experiments. In the agar diffusion test, 500 ppm of the extract inhibited the fungus mycelial growth up to 96 hours after exposure. The mycelial growth from the edge of the pre-inoculated A. invadans agar discs treated with S. alba extracts at concentrations of 100, 500 and 1000 ppm were 15, 8 and 0 mm respectively. The results of the filter paper disc test showed that the S. alba extract at its minimal inhibitory concentration (1000 ppm) has similar qualitative inhibitory effect as malachite green at 1 ppm and formalin at 250 ppm. According to the in vivo tests findings, in the infected fish fed with 3.0% and 5.0% supplementation diet, the numbers of white blood cell and myeloperoxidase activity significantly increased after the second week of treatment. Whilst the numbers of red blood cell significantly decreased in the infected fish fed with 0.0 and 1.0% supplementation diet. After the third week of feeding, significant increases in the total protein, albumin level, lysozyme activity were recorded in the infected fish fed with 3.0% and 5.0% supplementation diet. Also, the enriched diets increased the survival rate as compared to the untreated group that suffered from 90% mortality. The present study indicated that S. alba extract may inhibit the mycelial growth of A. invadans effectively, suggesting an alternative to other chemotherapeutic agents, which brought much environmental and health concerns to the public, for EUS treatment.

Keywords: fungal pathogen, goldfish, organic extract, treatment

Procedia PDF Downloads 287

Authors: Kunj Parikh, S. Bhattacharya, V. Natarajan

Abstract:

TCAD numerical simulation is one of the most tried and tested powerful tools for designing devices in semiconductor foundries worldwide. It has also been used to explain conduction in organic thin films where the processing temperature is often enough to make homogeneous samples (often imperfect, but homogeneously imperfect). In this report, we have presented the results of TCAD simulation in multi-grain thin films. The work has addressed the inhomogeneity in one dimension, but can easily be extended to two and three dimensions. The effect of grain boundaries has mainly been approximated as barriers located at the junction between two adjacent grains. The effect of the value of grain boundary barrier, the bulk traps, and the measurement temperature have been investigated.

Keywords: polycrystalline thin films, space charge limited conduction, Technology Computer-Aided Design (TCAD) simulation, traps

Procedia PDF Downloads 213

Authors: Syakirin O. Yong, Nasrudin A. Rahim, Bilal M. Eid, Buray Tankut

Abstract:

The aim of this paper is to develop an ultra-fast gate driver for Silicon Carbide (SiC) based switching device applications such as AC/DC DC/AC converters. Wide bandgap semiconductors such as SiC switches are growing rapidly nowadays due to their numerous capabilities such as faster switching, higher power density and higher voltage level. Wide band-gap switches can work properly on high frequencies such 50-250 kHz which is very useful for many power electronic applications such as solar inverters. Increasing the frequency minimizes the output filter size and system complexity however, this causes huge spike between MOSFET’s drain and source leg which leads to the failure of MOSFET if the voltage rating is exceeded. This paper investigates and concludes the optimum design for a gate drive board for SiC MOSFET switches without causing spikes and noises.

Keywords: PV system, lithium-ion, charger, constant current, constant voltage, renewable energy

Procedia PDF Downloads 153

Authors: Antoni Ivanov, Nikolay Dandanov, Nicole Christoff, Vladimir Poulkov

Abstract:

Spectrum underutilization has made cognitive radio a promising technology both for current and future telecommunications. This is due to the ability to exploit the unused spectrum in the bands dedicated to other wireless communication systems, and thus, increase their occupancy. The essential function, which allows the cognitive radio device to perceive the occupancy of the spectrum, is spectrum sensing. In this paper, the performance of modern adaptations of the four most widely used spectrum sensing techniques namely, energy detection (ED), cyclostationary feature detection (CSFD), matched filter (MF) and eigenvalues-based detection (EBD) is compared. The implementation has been accomplished through the PlutoSDR hardware platform and the GNU Radio software package in very low Signal-to-Noise Ratio (SNR) conditions. The optimal detection performance of the examined methods in a realistic implementation-oriented model is found for the common relevant parameters (number of observed samples, sensing time and required probability of false alarm).

Keywords: cognitive radio, dynamic spectrum access, GNU Radio, spectrum sensing

Procedia PDF Downloads 244

Authors: Manely Ansary Mood, Farzaneh Aziizi, Mahmoud Akbarian

Abstract:

This investigation included 130 cats diagnosed with FIP (Feb 2021-March 2022) in Iran, 74 with effusive FIP, and 56 with non-effusive FIP. The patients' initial dosage regime consisted of a subcutaneous injection of GS-441524 was 6-15mg/kg-every 24h (based on the wet or ocular and neurologic signs). The minimum treatment period was twelve weeks, extended in animals that still had abnormal lab values, clinical signs, and sonographic findings. The outcomes of the 130 cats that completed the duration of treatment (14 died, 116 cured) were checked and recorded. Clinical, sonographic, and laboratory responses were checked and compared on days 28, 56, and 83 of treatment. 2 of the 116 cured cats relapsed within observation days. At the time of this publication (May 2022), 114 of the studied patients remained healthy. We could conclude that GS-441524 appears to be an effective option for FIP treatment, and also, to the base of our knowledge, this is the first report for group treatment of infected cats of FIP with GS-441524 in Iran.

Keywords: FIP, cat, GS-441524, treatment

Procedia PDF Downloads 114

Authors: Khader S. Al-Aidmat, Venus W. Samawi

Abstract:

Blurred vision in the misty atmosphere is essential problem which needs to be resolved. To solve this problem, we developed a technique to restore foggy degraded image from its original version using Back-propagation neural network (BP-NN). The suggested technique is based on mapping between foggy scene and its corresponding original scene. Seven different approaches are suggested based on type of features used in image restoration. Features are extracted from spatial and spatial-frequency domain (using DCT). Each of these approaches comes with its own BP-NN architecture depending on type and number of used features. The weight matrix resulted from training each BP-NN represents a fog filter. The performance of these filters are evaluated empirically (using PSNR), and perceptually. By comparing the performance of these filters, the effective features that suits BP-NN technique for restoring foggy images is recognized. This system proved its effectiveness and success in restoring moderate foggy images.

Keywords: artificial neural network, discrete cosine transform, feed forward neural network, foggy image restoration

Procedia PDF Downloads 380

Authors: Bader Alhumaidi Alsubaei, Zahid H. Akash, Ali Imam Sunny

Abstract:

The heat transfer coefficient can be improved passively by using a dimpled surface on the tube. The contact area where heat transfer takes place can be enlarged and turbulence will be purposefully produced inside the duct; as a consequence, higher heat transfer quality will be achieved by employing an extended inner or outer surface (dimpled surface). In order to compare the rate and quality of heat transfer between a regular-shaped pipe and a dimpled pipe, a dimpled tube with a fixed dimple radius was created. Numerical analysis of the plain and dimpled pipes was performed using ANSYS. A 23% increase in Nusselt number was seen for dimpled tubes compared to plain tubes. In comparison to plain tubes, dimpled tubes' increase in thermal performance index was found to be between 8% and 10%. An increase in pressure drop of 18% was noted.

Keywords: heat transfer, dimpled tube, CFD, ANSYS

Procedia PDF Downloads 107

Authors: Pyung Soo Kim, Eung Hyuk Lee, Mun Suck Jang

Abstract:

In the current paper, an indoor positioning system is proposed with consideration of measurement delay. Firstly, an estimation filter with a measurement delay is designed for the indoor positioning mechanism under a weighted least square criterion, which utilizes only finite measurements on the most recent window. The proposed estimation filtering based scheme gives the filtered estimates for position, velocity and acceleration of moving target in real-time, while removing undesired noisy effects and preserving desired moving positions. Secondly, the proposed scheme is shown to have good inherent properties such as unbiasedness, efficiency, time-invariance, deadbeat, and robustness due to the finite memory structure. Finally, computer simulations shows that the performance of the proposed estimation filtering based scheme can outperform to the existing infinite memory filtering based mechanism.

Keywords: indoor positioning system, wireless sensor networks, measurement delay

Procedia PDF Downloads 481

Authors: A. Benatiallah, A. Hadjadj, D. Benatiallah, F. Abaidi, A. Harrouz

Abstract:

Photovoltaic system is established as a reliable and economical source of electricity in rural and Sahara areas, especially in developing countries where the population is dispersed, has low consumption of energy and the grid power is not extended to these areas due to viability and financial problems. The production of energy by the photovoltaic system is very fluctuates and depend of meteorological conditions. Wind is a very important and often neglected parameter in the behavior of the solar module. The electric performances of a solar module to the silicon are very appreciable to the blows; in the present work we have studies the behavior of multi-crystal solar module according to the density of dust, and the principals electric feature of the solar module. An evaluation permits to affirm that a solar module under the effect of sand will collect a lower flux to the normal conditions.

Keywords: photovoltaic, multi-crystal module, experimental, effect of dust, performances

Procedia PDF Downloads 304

Authors: Ja-Keoung Koo, Kensuke Nakamura, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Byung-Woo Hong

Abstract:

The machine learning techniques based on a convolutional neural network (CNN) have been actively developed and successfully applied to a variety of image analysis tasks including reconstruction, noise reduction, resolution enhancement, segmentation, motion estimation, object recognition. The classical visual information processing that ranges from low level tasks to high level ones has been widely developed in the deep learning framework. It is generally considered as a challenging problem to derive visual interpretation from high dimensional imagery data. A CNN is a class of feed-forward artificial neural network that usually consists of deep layers the connections of which are established by a series of non-linear operations. The CNN architecture is known to be shift invariant due to its shared weights and translation invariance characteristics. However, it is often computationally intractable to optimize the network in particular with a large number of convolution layers due to a large number of unknowns to be optimized with respect to the training set that is generally required to be large enough to effectively generalize the model under consideration. It is also necessary to limit the size of convolution kernels due to the computational expense despite of the recent development of effective parallel processing machinery, which leads to the use of the constantly small size of the convolution kernels throughout the deep CNN architecture. However, it is often desired to consider different scales in the analysis of visual features at different layers in the network. Thus, we propose a CNN model where different sizes of the convolution kernels are applied at each layer based on the random projection. We apply random filters with varying sizes and associate the filter responses with scalar weights that correspond to the standard deviation of the random filters. We are allowed to use large number of random filters with the cost of one scalar unknown for each filter. The computational cost in the back-propagation procedure does not increase with the larger size of the filters even though the additional computational cost is required in the computation of convolution in the feed-forward procedure. The use of random kernels with varying sizes allows to effectively analyze image features at multiple scales leading to a better generalization. The robustness and effectiveness of the proposed CNN based on random kernels are demonstrated by numerical experiments where the quantitative comparison of the well-known CNN architectures and our models that simply replace the convolution kernels with the random filters is performed. The experimental results indicate that our model achieves better performance with less number of unknown weights. The proposed algorithm has a high potential in the application of a variety of visual tasks based on the CNN framework. Acknowledgement—This work was supported by the MISP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by IITP, and NRF-2014R1A2A1A11051941, NRF2017R1A2B4006023.

Keywords: deep learning, convolutional neural network, random kernel, random projection, dimensionality reduction, object recognition

Procedia PDF Downloads 285