Search results for: matrix minimization algorithm

Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

Procedia PDF Downloads 110

Authors: Erwin San Juan Martinez, Miguel Angel Aguilar Mendez, Manuel Sandoval Villa, Libia Iris Trejo Tellez

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Some nanomaterials may improve the vegetal growth in certain concentration intervals, and could be used as nanofertilizers in order to increase crops yield, and decreasing the environmental pollution due to non-controlled use of conventional fertilizers, therefore the present investigation’s objective was to synthetize and characterize gelatin nanoparticles loaded with calcium generated through pulverization technique and be used as nanofertilizers. To obtain these materials, a fractional factorial design 27-4 was used in order to evaluate the largest number of factors (concentration of Ca2+, temperature and agitation time of the solution and calcium concentration, drying temperature, and % spray) with a possible effect on the size, distribution and morphology of nanoparticles. For the formation of nanoparticles, a Nano Spray-Dryer B - 90® (Buchi, Flawil, Switzerland), equipped with a spray cap of 4 µm was used. Size and morphology of the obtained nanoparticles were evaluated using a scanning electron microscope (JOEL JSM-6390LV model; Tokyo, Japan) equipped with an energy dispersive x-ray X (EDS) detector. The total quantification of Ca2+ as well as its release by the nanoparticles was carried out in an equipment of induction atomic emission spectroscopy coupled plasma (ICP-ES 725, Agilent, Mulgrave, Australia). Of the seven factors evaluated, only the concentration of fertilizer, % spray and concentration of polymer presented a statistically significant effect on particle size. Micrographs of SEM from six of the eight conditions evaluated in this research showed particles separated and with a good degree of sphericity, while in the other two particles had amorphous morphology and aggregation. In all treatments, most of the particles showed smooth surfaces. The average size of smallest particle obtained was 492 nm, while EDS results showed an even distribution of Ca2+ in the polymer matrix. The largest concentration of Ca2+ in ICP was 10.5%, which agrees with the theoretical value calculated, while the release kinetics showed an upward trend within 24 h. Using the technique employed in this research, it was possible to obtain nanoparticles loaded with calcium, of good size, sphericity and with release controlled properties. The characteristics of nanoparticles resulted from manipulation of the conditions of synthesis which allow control of the size and shape of the particles, and provides the means to adapt the properties of the materials to an specific application.

Keywords: calcium, controlled release, gelatin, nano spraydryer, nanofertilizer

Procedia PDF Downloads 154

Authors: Hesham Abd Elkhalek, Sherif M. Hafez, Yasser M. El Fahham

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Allocating limited budget to maintain bridge networks and selecting effective maintenance strategies for each bridge represent challenging tasks for maintenance managers and decision makers. In Egypt, bridges are continuously deteriorating. In many cases, maintenance works are performed due to user complaints. The objective of this paper is to develop a practical and reliable framework to manage the maintenance, repair, and rehabilitation (MR&R) activities of Bridges network considering performance and budget limits. The model solves an optimization problem that maximizes the average condition of the entire network given the limited available budget using Genetic Algorithm (GA). The framework contains bridge inventory, condition assessment, repair cost calculation, deterioration prediction, and maintenance optimization. The developed model takes into account multiple parameters including serviceability requirements, budget allocation, element importance on structural safety and serviceability, bridge impact on network, and traffic. A questionnaire is conducted to complete the research scope. The proposed model is implemented in software, which provides a friendly user interface. The framework provides a multi-year maintenance plan for the entire network for up to five years. A case study of ten bridges is presented to validate and test the proposed model with data collected from Transportation Authorities in Egypt. Different scenarios are presented. The results are reasonable, feasible and within acceptable domain.

Keywords: bridge management systems (BMS), cost optimization condition assessment, fund allocation, Markov chain

Procedia PDF Downloads 264

Authors: Shian Guan, Marie Bourdin, Isabelle Trenque, Younes Messaddeq, Thierry Cardinal, Nicolas Penin, Issam Mjejri, Aline Rougier, Etienne Duguet, Stephane Mornet, Manuel Gaudon

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At the interface of the studies performed by 3 Ph.D. students: Shian Guan (2017-2020), Marie Bourdin (2016-2019) and Isabelle Trenque (2012-2015), a single synthesis route: polyol-mediated process, was used with success for the preparation of different inorganic oxides. Both of these inorganic oxides were elaborated for their potential application as smart optical compounds. This synthesis route has allowed us to develop nanoparticles of zinc oxide, vanadium oxide or tungsten oxide. This route is with easy implementation, inexpensive and with large-scale production potentialities and leads to materials of high purity. The obtaining by this route of nanometric particles, however perfectly crystalline, has notably led to the possibility of doping these matrix materials with high doping ion concentrations (high solubility limits). Thus, Al3+ or Ga3+ doped-ZnO powder, with high doping rate in comparison with the literature, exhibits remarkable infrared absorption properties thanks to their high free carrier density. Note also that due to the narrow particle size distribution of the as-prepared nanometric doped-ZnO powder, the original correlation between crystallite size and unit-cell parameters have been established. Also, depending on the annealing atmosphere use to treat vanadium precursors, VO2, V2O3 or V2O5 oxides with thermochromic or electrochromic properties can be obtained without any impurity, despite the versatility of the oxidation state of vanadium. This is of more particular interest on vanadium dioxide, a relatively difficult-to-prepare oxide, whose first-order metal-insulator phase transition is widely explored in the literature for its thermochromic behavior (in smart windows with optimal thermal insulation). Finally, the reducing nature of the polyol solvents ensures the production of oxygen-deficient tungsten oxide, thus conferring to the nano-powders exotic colorimetric properties, as well as optimized photochromic and electrochromic behaviors.

Keywords: inorganic oxides, electrochromic, photochromic, thermochromic

Procedia PDF Downloads 196

Authors: T. Manesh, Abdalla A. Alameen, M. Mohemmed Sha, A. Mohamed Mustaq Ahmed

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Email and instant messaging applications are foremost and extensively used electronic communication methods in this era of information explosion. These applications are generally used for exchange of information using several frontend applications from various service providers by its users. Almost all such communications are now secured using SSL or TLS security over HTTP communication. At the same time, it is also noted that cyber criminals and terrorists have started exchanging information using these methods. Since communication is encrypted end-to-end, tracing significant forensic details and actual content of messages are found to be unattended and severe challenges by available forensic tools. These challenges seriously affect in procuring substantial evidences against such criminals from their working environments. This paper presents a vibrant forensic exploration and architectural framework which not only decrypts any communication or network session but also reconstructs actual message contents of email as well as instant messaging applications. The framework can be effectively used in proxy servers and individual computers and it aims to perform forensic reconstruction followed by analysis of webmail and ICQ messaging applications. This forensic framework exhibits a versatile nature as it is equipped with high speed packet capturing hardware, a well-designed packet manipulating algorithm. It regenerates message contents over regular as well as SSL encrypted SMTP, POP3 and IMAP protocols and catalyzes forensic presentation procedure for prosecution of cyber criminals by producing solid evidences of their actual communication as per court of law of specific countries.

Keywords: forensics, network sessions, packet reconstruction, packet reordering

Procedia PDF Downloads 306

Authors: Derjew Ayele Ejigu, Houde Song, Xiaojing Liu

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This work presents the particle swarm optimization trained neural network (PSO-NN) supervisory proportional integral derivative (PID) control method to monitor the pressurized water reactor (PWR) core power for safe operation. The proposed control approach is implemented on the transfer function of the PWR core, which is computed from the state-space model. The PWR core state-space model is designed from the neutronics, thermal-hydraulics, and reactivity models using perturbation around the equilibrium value. The proposed control approach computes the control rod speed to maneuver the core power to track the reference in a closed-loop scheme. The particle swarm optimization (PSO) algorithm is used to train the neural network (NN) and to tune the PID simultaneously. The controller performance is examined using integral absolute error, integral time absolute error, integral square error, and integral time square error functions, and the stability of the system is analyzed by using the Bode diagram. The simulation results indicated that the controller shows satisfactory performance to control and track the load power effectively and smoothly as compared to the PSO-PID control technique. This study will give benefit to design a supervisory controller for nuclear engineering research fields for control application.

Keywords: machine learning, neural network, pressurized water reactor, supervisory controller

Procedia PDF Downloads 129

Authors: Nyeng P. Gyang

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Even though past, current and future trends suggest that multicore and cloud computing systems are increasingly prevalent/ubiquitous, this class of parallel systems is nonetheless underutilized, in general, and barely used for research on employing parallel Delaunay triangulation for parallel surface modeling and generation, in particular. The performances, of actual/physical and virtual/cloud multicore systems/machines, at executing various algorithms, which implement various parallelization strategies of the incremental insertion technique of the Delaunay triangulation algorithm, were evaluated. T-tests were run on the data collected, in order to determine whether various performance metrics differences (including execution time, speedup and efficiency) were statistically significant. Results show that the actual machine is approximately twice faster than the virtual machine at executing the same programs for the various parallelization strategies. Results, which furnish the scalability behaviors of the various parallelization strategies, also show that some of the differences between the performances of these systems, during different runs of the algorithms on the systems, were statistically significant. A few pseudo superlinear speedup results, which were computed from the raw data collected, are not true superlinear speedup values. These pseudo superlinear speedup values, which arise as a result of one way of computing speedups, disappear and give way to asymmetric speedups, which are the accurate kind of speedups that occur in the experiments performed.

Keywords: cloud computing systems, multicore systems, parallel Delaunay triangulation, parallel surface modeling and generation

Procedia PDF Downloads 178

Authors: Afifa Hafidh, Fathi Touati, Ahmed Hichem Hamzaoui, Sayda Somrani

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The objective of the present study has been to synthesize and to characterize silica hybrid materials using sol-gel technic and to investigate their properties. Silica materials were successfully fabricated using various bi-functional 1,3,4-thiadiazoles and tetraethoxysilane (TEOS) as co-precursors via a facile one-pot sol-gel pathway. TEOS was introduced at room temperature with 1,3,4-thiadiazole 2,5-difunctiunal adducts, in ethanol as solvent and using HCl acid as catalyst. The sol-gel process lead to the formation of monolithic, coloured and transparent gels. TEOS was used as a principal network forming agent. The incorporation of 1,3,4-thiadiazole molecules was realized by attachment of these later onto a silica matrix. This allowed covalent linkage between organic and inorganic phases and lead to the formation of Si-N and Si-S bonds. The prepared hybrid materials were characterized by Fourier transform infrared, NMR ²⁹Si and ¹³C, scanning electron microscopy and nitrogen absorption-desorption measurements. The optic and magnetic properties of hybrids are studied respectively by ultra violet-visible spectroscopy and electron paramagnetic resonance. It was shown in this work, that heterocyclic moieties were successfully attached in the hybrid skeleton. The formation of the Si-network composed of cyclic units (Q3 structures) connected by oxygen bridges (Q4 structures) was proved by ²⁹Si NMR spectroscopy. The Brunauer-Elmet-Teller nitrogen adsorption-desorption method shows that all the prepared xerogels have isotherms type IV and are mesoporous solids. The specific surface area and pore volume of these materials are important. The obtained results show that all materials are paramagnetic semiconductors. The data obtained by Nuclear magnetic resonance ²⁹Si and Fourier transform infrared spectroscopy, show that Si-OH and Si-NH groups existing in silica hybrids can participate in adsorption interactions. The obtained materials containing reactive centers could exhibit adsorption properties of metal ions due to the presence of OH and NH functionality in the mesoporous frame work. Our design of a simple method to prepare hybrid materials may give interest of the development of mesoporous hybrid systems and their use within the domain of environment in the future.

Keywords: hybrid materials, sol-gel process, 1, 3, 4-thiadaizole, TEOS

Procedia PDF Downloads 150

Authors: Jocelyn B. Barbosa, Angeli L. Magbaril, Mariel T. Sabanal, John Paul T. Galario, Mikka P. Baldovino

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The use of technology has become ubiquitous in different areas of business today. With the advent of digital imaging and database technology, business owners have been motivated to integrate technology to their business operation ranging from small, medium to large enterprises. Technology has been found to have brought many benefits that can make a business grow. Hog or swine raising, for example, is a very popular enterprise in the Philippines, whose challenges in production monitoring can be addressed through technology integration. Swine production monitoring can become a tedious task as the enterprise goes larger. Specifically, problems like delayed and inconsistent reports are most likely to happen if counting of swine per pen of which building is done manually. In this study, we present iCount, which aims to ensure efficient swine detection and counting that hastens the swine production monitoring task. We develop a system that automatically detects and counts swine based on Sobel filter and ellipse fitting model, given the still photos of the group of swine captured in a pen. We improve the Sobel filter detection result through 8-neigbhorhood rule implementation. Ellipse fitting technique is then employed for proper swine detection. Furthermore, the system can generate periodic production reports and can identify the specific consumables to be served to the swine according to schedules. Experiments reveal that our algorithm provides an efficient way for detecting swine, thereby providing a significant amount of accuracy in production monitoring.

Keywords: automatic swine counting, swine detection, swine production monitoring, ellipse fitting model, sobel filter

Procedia PDF Downloads 289

Authors: Jifeng Zhang , Yongpeng Lei

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Continuous carbon fiber reinforced polyamide 6 (CF/PA6) composites are potential for application in the automotive industry due to their high specific strength and stiffness. However, PA6 resin is sensitive to the moisture in the hydrothermal environment and CF/PA6 composites might undergo several physical and chemical changes, such as plasticization, swelling, and hydrolysis, which induces a reduction of mechanical properties. So far, little research has been reported on the assessment of the effects of hydrothermal aging on the mechanical properties of continuous CF/PA6 composite. This study deals with the effects of hydrothermal aging on moisture absorption and mechanical properties of polyamide 6 (PA6) and polyamide 6 reinforced with continuous carbon fibers composites (CF/PA6) by immersion in distilled water at 30 ℃, 50 ℃, 70 ℃, and 90 ℃. Degradation of mechanical performance has been monitored, depending on the water absorption content and the aging temperature. The experimental results reveal that under the same aging condition, the PA6 resin absorbs more water than the CF/PA6 composite, while the water diffusion coefficient of CF/PA6 composite is higher than that of PA6 resin because of interfacial diffusion channel. In mechanical properties degradation process, an exponential reduction in tensile strength and elastic modulus are observed in PA6 resin as aging temperature and water absorption content increases. The degradation trend of flexural properties of CF/PA6 is the same as that of tensile properties of PA6 resin. Moreover, the water content plays a decisive role in mechanical degradation compared with aging temperature. In contrast, hydrothermal environment has mild effect on the tensile properties of CF/PA6 composites. The elongation at breakage of PA6 resin and CF/PA6 reaches the highest value when their water content reaches 6% and 4%, respectively. Dynamic mechanical analysis (DMA) and scanning electron microscope (SEM) were also used to explain the mechanism of mechanical properties alteration. After exposed to the hydrothermal environment, the Tg (glass transition temperature) of samples decreases dramatically with water content increase. This reduction can be ascribed to the plasticization effect of water. For the unaged specimens, the fibers surface is coated with resin and the main fracture mode is fiber breakage, indicating that a good adhesion between fiber and matrix. However, with absorbed water content increasing, the fracture mode transforms to fiber pullout. Finally, based on Arrhenius methodology, a predictive model with relate to the temperature and water content has been presented to estimate the retention of mechanical properties for PA6 and CF/PA6.

Keywords: continuous carbon fiber reinforced polyamide 6 composite, hydrothermal aging, Arrhenius methodology, interface

Procedia PDF Downloads 101

Authors: Marco Sewald

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Comparing present to past the numbers of Americans expatriating U. S. citizenship have risen. Even though these numbers are small compared to the immigrants, U. S. citizens expatriations have historically been much lower, making the uptick worrisome. In addition, the published lists and numbers from the U.S. government seems incomplete, with many not counted. Different branches of the U. S. government report different numbers and no one seems to know exactly how big the real number is, even though the IRS and the FBI both track and/or publish numbers of Americans who renounce. Since there is no single explanation, anecdotal evidence suggests this uptick is caused by global tax law and increased compliance burdens imposed by the U.S. lawmakers on U.S. citizens abroad. Within a research project the question arose about the reasons why a constant growing number of U.S. citizens are expatriating – the answers are believed helping to explain the underlying governmental policy rational, leading to such activities. While it is impossible to locate former U.S. citizens to conduct a survey on the reasons and the U.S. government is not commenting on the reasons given within the process of expatriation, the chosen methodology is Structural Equation Modeling (SEM), in the first step by re-using current surveys conducted by different researchers within the population of U. S. citizens residing abroad during the last years. Surveys questioning the personal situation in the context of tax, compliance, citizenship and likelihood to repatriate to the U. S. In general SEM allows: (1) Representing, estimating and validating a theoretical model with linear (unidirectional or not) relationships. (2) Modeling causal relationships between multiple predictors (exogenous) and multiple dependent variables (endogenous). (3) Including unobservable latent variables. (4) Modeling measurement error: the degree to which observable variables describe latent variables. Moreover SEM seems very appealing since the results can be represented either by matrix equations or graphically. Results: the observed variables (items) of the construct are caused by various latent variables. The given surveys delivered a high correlation and it is therefore impossible to identify the distinct effect of each indicator on the latent variable – which was one desired result. Since every SEM comprises two parts: (1) measurement model (outer model) and (2) structural model (inner model), it seems necessary to extend the given data by conducting additional research and surveys to validate the outer model to gain the desired results.

Keywords: expatriation of U. S. citizens, SEM, structural equation modeling, validating

Procedia PDF Downloads 186

Authors: Xiyang Li, Qi Yu, Lun Zhang

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In the face of natural disasters and other emergency situations, determining the optimal location of rescue centers is crucial for improving rescue efficiency and minimizing impact on affected populations. This paper proposes a method that integrates genetic algorithms (GA) and backpropagation neural networks (BPNN) to address the site selection optimization problem for emergency rescue centers. We utilize BPNN to accurately estimate the cost of delivering supplies from rescue centers to each temporary camp. Moreover, a genetic algorithm with a special partially matched crossover (PMX) strategy is employed to ensure that the number of temporary camps assigned to each rescue center adheres to predetermined limits. Using the population distribution data during the 2022 epidemic in Jiading District, Shanghai, as an experimental case, this paper verifies the effectiveness of the proposed method. The experimental results demonstrate that the BPNN-GA method proposed in this study outperforms existing algorithms in terms of computational efficiency and optimization performance. Especially considering the requirements for computational resources and response time in emergency situations, the proposed method shows its ability to achieve rapid convergence and optimal performance in the early and mid-stages. Future research could explore incorporating more real-world conditions and variables into the model to further improve its accuracy and applicability.

Keywords: emergency rescue centers, genetic algorithms, back-propagation neural networks, site selection optimization

Procedia PDF Downloads 39

Authors: Marvin Chibuzo Offiah, Susanne Rosenthal, Markus Borschbach

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Certain research projects in Computer Science often involve research on existing signal processing algorithms and developing improvements on them. Research budgets are usually limited, hence there is limited time for implementing the algorithms from scratch. It is therefore common practice, to use implementations provided by other researchers as a template. These are most commonly provided in a rapid development, i.e. 4th generation, programming language, usually Matlab. Rapid development is a common method in Computer Science research for quickly implementing and testing new developed algorithms, which is also a common task within agile project organization. The growing relevance of mobile devices in the computer market also gives rise to the need to demonstrate the successful executability and performance measurement of these algorithms on a mobile device operating system and processor, particularly on a smartphone. Open mobile systems such as Android, are most suitable for this task, which is to be performed most efficiently. Furthermore, efficiently implementing an interaction between the algorithm and a graphical user interface (GUI) that runs exclusively on the mobile device is necessary in cases where the project’s goal statement also includes such a task. This paper examines different proposed solutions for porting computer algorithms obtained through rapid development into a GUI-based smartphone Android app and evaluates their feasibilities. Accordingly, the feasible methods are tested and a short success report is given for each tested method.

Keywords: SMARTNAVI, Smartphone, App, Programming languages, Rapid Development, MATLAB, Octave, C/C++, Java, Android, NDK, SDK, Linux, Ubuntu, Emulation, GUI

Procedia PDF Downloads 458

Authors: Yu-ding Du, Qi-lian Bao, Nassim Bessaad, Lin Liu

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The Global Navigation Satellite System (GNSS) is regarded as an effective approach for the purpose of replacing the large amount used track-side balises in modern train localization systems. This paper describes a method based on the data fusion of a GNSS receiver sensor and an odometer sensor that can significantly improve the positioning accuracy. A digital track map is needed as another sensor to project two-dimensional GNSS position to one-dimensional along-track distance due to the fact that the train’s position can only be constrained on the track. A model trained by BP neural network is used to estimate the trend positioning error which is related to the specific location and proximate processing of the digital track map. Considering that in some conditions the satellite signal failure will lead to the increase of GNSS positioning error, a detection step for GNSS signal is applied. An adaptive weighted fusion algorithm is presented to reduce the standard deviation of train speed measurement. Finally an Extended Kalman Filter (EKF) is used for the fusion of the projected 1-D GNSS positioning data and the 1-D train speed data to get the estimate position. Experimental results suggest that the proposed method performs well, which can reduce positioning error notably.

Keywords: multi-sensor data fusion, train positioning, GNSS, odometer, digital track map, map matching, BP neural network, adaptive weighted fusion, Kalman filter

Procedia PDF Downloads 226

Authors: Sheikh Shams Azam, Manoj Raju, Venkatesh Pagidimarri, Vamsi Kasivajjala

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Over the past decade, there has been a steep rise in the data-driven analysis in major areas of medicine, such as clinical decision support system, survival analysis, patient similarity analysis, image analytics etc. Most of the data in the field are well-structured and available in numerical or categorical formats which can be used for experiments directly. But on the opposite end of the spectrum, there exists a wide expanse of data that is intractable for direct analysis owing to its unstructured nature which can be found in the form of discharge summaries, clinical notes, procedural notes which are in human written narrative format and neither have any relational model nor any standard grammatical structure. An important step in the utilization of these texts for such studies is to transform and process the data to retrieve structured information from the haystack of irrelevant data using information retrieval and data mining techniques. To address this problem, the authors present Q-Map in this paper, which is a simple yet robust system that can sift through massive datasets with unregulated formats to retrieve structured information aggressively and efficiently. It is backed by an effective mining technique which is based on a string matching algorithm that is indexed on curated knowledge sources, that is both fast and configurable. The authors also briefly examine its comparative performance with MetaMap, one of the most reputed tools for medical concepts retrieval and present the advantages the former displays over the latter.

Keywords: information retrieval, unified medical language system, syntax based analysis, natural language processing, medical informatics

Procedia PDF Downloads 107

Authors: Dileep Kumar Gupta, Rajendra Prasad, Pradeep Kumar, Varun Narayan Mishra, Ajeet Kumar Vishwakarma, Prashant K. Srivastava

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An approach was evaluated for the retrieval of soil moisture of bare soil surface using bistatic scatterometer data in the angular range of 200 to 700 at VV- and HH- polarization. The microwave data was acquired by specially designed X-band (10 GHz) bistatic scatterometer. The linear regression analysis was done between scattering coefficients and soil moisture content to select the suitable incidence angle for retrieval of soil moisture content. The 250 incidence angle was found more suitable. The support vector regression analysis was used to approximate the function described by the input-output relationship between the scattering coefficient and corresponding measured values of the soil moisture content. The performance of support vector regression algorithm was evaluated by comparing the observed and the estimated soil moisture content by statistical performance indices %Bias, root mean squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE). The values of %Bias, root mean squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were found 2.9451, 1.0986, and 0.9214, respectively at HH-polarization. At VV- polarization, the values of %Bias, root mean squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were found 3.6186, 0.9373, and 0.9428, respectively.

Keywords: bistatic scatterometer, soil moisture, support vector regression, RMSE, %Bias, NSE

Procedia PDF Downloads 394

Authors: Arwa Alzughaibi

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Human Motion detection is a challenging task due to a number of factors including variable appearance, posture and a wide range of illumination conditions and background. So, the first need of such a model is a reliable feature set that can discriminate between a human and a non-human form with a fair amount of confidence even under difficult conditions. By having richer representations, the classification task becomes easier and improved results can be achieved. The Aim of this paper is to investigate the reliable and accurate human motion detection models that are able to detect the human motions accurately under varying illumination levels and backgrounds. Different sets of features are tried and tested including Histogram of Oriented Gradients (HOG), Deformable Parts Model (DPM), Local Decorrelated Channel Feature (LDCF) and Aggregate Channel Feature (ACF). However, we propose an efficient and reliable human motion detection approach by combining Histogram of oriented gradients (HOG) and local phase quantization (LPQ) as the feature set, and implementing search pruning algorithm based on optical flow to reduce the number of false positive. Experimental results show the effectiveness of combining local phase quantization descriptor and the histogram of gradient to perform perfectly well for a large range of illumination conditions and backgrounds than the state-of-the-art human detectors. Areaunder th ROC Curve (AUC) of the proposed method achieved 0.781 for UCF dataset and 0.826 for CDW dataset which indicates that it performs comparably better than HOG, DPM, LDCF and ACF methods.

Keywords: human motion detection, histograms of oriented gradient, local phase quantization, local phase quantization

Procedia PDF Downloads 230

Authors: Misganaw Abebe Baye, Ji-Woo Park, Beom-Soo Kang

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The product quality of multi-point dieless forming (MDF) is identified to be dependent on the process parameters. Moreover, a certain variation of friction and material properties may have a substantially worse influence on the final product quality. This study proposed on how to compensate the MDF product defects by minimizing the sensitivity of noise parameter variations. This can be attained by reliability-based robust optimization (RRO) technique to obtain the optimal process setting of the controllable parameters. Initially two MDF Finite Element (FE) simulations of AA3003-H14 saddle shape showed a substantial amount of dimpling, wrinkling, and shape error. FE analyses are consequently applied on ABAQUS commercial software to obtain the correlation between the control process setting and noise variation with regard to the product defects. The best prediction models are chosen from the family of metamodels to swap the computational expensive FE simulation. Genetic algorithm (GA) is applied to determine the optimal process settings of the control parameters. Monte Carlo Analysis (MCA) is executed to determine how the noise parameter variation affects the final product quality. Finally, the RRO FE simulation and the experimental result show that the amendment of the control parameters in the final forming process leads to a considerably better-quality product.

Keywords: dimpling, multi-point dieless forming, reliability-based robust optimization, shape error, variation, wrinkling

Procedia PDF Downloads 223

Authors: Oluremi I. Bamgbade, Oluwayomi Babatunde

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Governments and nonprofits have been in the support of sustainability as the goal of businesses especially in the construction industry because of its considerable impacts on the environment, economy, and society. However, to measure the degree to which an organisation is being sustainable or pursuing sustainable growth can be difficult as a result of the clear sustainability strategy required to assume their commitment to the goal and competitive advantage. This research investigated the influence of organisational culture and organisational structure in achieving sustainable construction among South African construction firms. A total of 132 consultants from the nine provinces in South Africa participated in the survey. The data collected were initially screened using SPSS (version 21) while Partial Least Squares Structural Equation Modeling (PLS-SEM) algorithm and bootstrap techniques were employed to test the hypothesised paths. The empirical evidence also supported the hypothesised direct effects of organisational culture and organisational structure on sustainable construction. Similarly, the result regarding the relationship between organisational culture and organisational structure was supported. Therefore, construction industry can record a considerable level of construction sustainability and establish suitable cultures and structures within the construction organisations. Drawing upon organisational control theory, these findings supported the view that these organisational internal factors have a strong contingent effect on sustainability adoption in construction project execution. The paper makes theoretical, practical and methodological contributions within the domain of sustainable construction especially in the context of South Africa. Some limitations of the study are indicated, suggesting opportunities for future research.

Keywords: organisational culture, organisational structure, South African construction firms, sustainable construction

Procedia PDF Downloads 258

Authors: Edokpa Idemudia Waziri, Salisu S. Umar

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The Hotellng’s T^2 is a well-known statistic for detecting a shift in the mean vector of a multivariate normal distribution. Control charts based on T have been widely used in statistical process control for monitoring a multivariate process. Although it is a powerful tool, the T statistic is deficient when the shift to be detected in the mean vector of a multivariate process is small and consistent. The Multivariate Exponentially Weighted Moving Average (MEWMA) control chart is one of the control statistics used to overcome the drawback of the Hotellng’s T statistic. In this paper, the probability distribution of the Average Run Length (ARL) of the MEWMA control chart when the quality characteristics exhibit substantial cross correlation and when the process is in-control and out-of-control was derived using the Markov Chain algorithm. The derivation of the probability functions and the moments of the run length distribution were also obtained and they were consistent with some existing results for the in-control and out-of-control situation. By simulation process, the procedure identified a class of ARL for the MEWMA control when the process is in-control and out-of-control. From our study, it was observed that the MEWMA scheme is quite adequate for detecting a small shift and a good way to improve the quality of goods and services in a multivariate situation. It was also observed that as the in-control average run length ARL0¬ or the number of variables (p) increases, the optimum value of the ARL0pt increases asymptotically and as the magnitude of the shift σ increases, the optimal ARLopt decreases. Finally, we use the example from the literature to illustrate our method and demonstrate its efficiency.

Keywords: average run length, markov chain, multivariate exponentially weighted moving average, optimal smoothing parameter

Procedia PDF Downloads 393

Authors: Mihriban Korukluoglu, Goksen Arik, Cagla Erdogan, Selen Kocakoglu

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Kefir is a traditional fermented refreshing beverage which is known for its valuable and beneficial properties for human health. Mainly yeast species, lactic acid bacteria (LAB) strains and fewer acetic acid bacteria strains live together in a natural matrix named “kefir grain”, which is formed from various proteins and polysaccharides. Different microbial species live together in slimy kefir grain and it has been thought that synergetic effect could take place between microorganisms, which belong to different genera and species. In this research, yeast and LAB were isolated from kefir samples obtained from Uludag University Food Engineering Department. The cell morphology of isolates was screened by microscopic examination. Gram reactions of bacteria isolates were determined by Gram staining method, and as well catalase activity was examined. After observing the microscopic/morphological and physical, enzymatic properties of all isolates, they were divided into the groups as LAB and/or yeast according to their physicochemical responses to the applied examinations. As part of this research, the antagonistic/synergistic efficacy of the identified five LAB and five yeast strains to each other were determined individually by disk diffusion method. The antagonistic or synergistic effect is one of the most important properties in a co-culture system that different microorganisms are living together. The synergistic effect should be promoted, whereas the antagonistic effect is prevented to provide effective culture for fermentation of kefir. The aim of this study was to determine microbial interactions between identified yeast and LAB strains, and whether their effect is antagonistic or synergistic. Thus, if there is a strain which inhibits or retards the growth of other strains found in Kefir microflora, this circumstance shows the presence of antagonistic effect in the medium. Such negative influence should be prevented, whereas the microorganisms which have synergistic effect on each other should be promoted by combining them in kefir grain. Standardisation is the most desired property for industrial production. Each microorganism found in the microbial flora of a kefir grain should be identified individually. The members of the microbial community found in the glue-like kefir grain may be redesigned as a starter culture regarding efficacy of each microorganism to another in kefir processing. The main aim of this research was to shed light on more effective production of kefir grain and to contribute a standardisation of kefir processing in the food industry.

Keywords: antagonistic effect, kefir, lactic acid bacteria (LAB), synergistic, yeast

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Authors: Taisiia Gorshkova

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Since the 1980s, spatial correlation models have been used more often to model regional indicators. An increasingly popular method for studying regional indicators is modeling taking into account spatial relationships between objects that are part of the same economic zone. In 2000s the new class of model – spatial vector autoregressions was developed. The main difference between standard and spatial vector autoregressions is that in the spatial VAR (SpVAR), the values of indicators at time t may depend on the values of explanatory variables at the same time t in neighboring regions and on the values of explanatory variables at time t-k in neighboring regions. Thus, VAR is a special case of SpVAR in the absence of spatial lags, and the spatial panel data model is a special case of spatial VAR in the absence of time lags. Two specifications of SpVAR were applied to Russian regional data for 2000-2017. The values of GRP and regional CPI are used as endogenous variables. The lags of GRP, CPI and the unemployment rate were used as explanatory variables. For comparison purposes, the standard VAR without spatial correlation was used as “naïve” model. In the first specification of SpVAR the unemployment rate and the values of depending variables, GRP and CPI, in neighboring regions at the same moment of time t were included in equations for GRP and CPI respectively. To account for the values of indicators in neighboring regions, the adjacency weight matrix is used, in which regions with a common sea or land border are assigned a value of 1, and the rest - 0. In the second specification the values of depending variables in neighboring regions at the moment of time t were replaced by these values in the previous time moment t-1. According to the results obtained, when inflation and GRP of neighbors are added into the model both inflation and GRP are significantly affected by their previous values, and inflation is also positively affected by an increase in unemployment in the previous period and negatively affected by an increase in GRP in the previous period, which corresponds to economic theory. GRP is not affected by either the inflation lag or the unemployment lag. When the model takes into account lagged values of GRP and inflation in neighboring regions, the results of inflation modeling are practically unchanged: all indicators except the unemployment lag are significant at a 5% significance level. For GRP, in turn, GRP lags in neighboring regions also become significant at a 5% significance level. For both spatial and “naïve” VARs the RMSE were calculated. The minimum RMSE are obtained via SpVAR with lagged explanatory variables. Thus, according to the results of the study, it can be concluded that SpVARs can accurately model both the actual values of macro indicators (particularly CPI and GRP) and the general situation in the regions

Keywords: forecasting, regional data, spatial econometrics, vector autoregression

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Authors: Camila Hurtado, Maria A. Morales, Diego Torres, L.H. Reyes, Alejandro Maranon, Alicia Porras

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This work presents the physical and chemical characterization of beer brand fibers and the properties of novel composite materials made of these fibers and polylactic acid (PLA). Treated and untreated fibers were physically characterized in terms of their moisture content (ASTM D1348), density, and particle size (ASAE S319.2). A chemical analysis following TAPPI standards was performed to determine ash, extractives, lignin, and cellulose content on fibers. Thermal stability was determined by TGA analysis, and an FTIR was carried out to check the influence of the alkali treatment in fiber composition. An alkali treatment with NaOH (5%) of fibers was performed for 90 min, with the objective to improve the interfacial adhesion with polymeric matrix in composites. Composite materials based on either treated or untreated beer brand fibers and polylactic acid (PLA) were developed characterized in tension (ASTM D638), bending (ASTM D790) and impact (ASTM D256). Before composites manufacturing, PLA and brand beer fibers (10 wt.%) were mixed in a twin extruder with a temperature profile between 155°C and 180°C. Coupons were manufactured by compression molding (110 bar) at 190°C. Physical characterization showed that alkali treatment does not affect the moisture content (6.9%) and the density (0.48 g/cm³ for untreated fiber and 0.46 g/cm³ for the treated one). Chemical and FTIR analysis showed a slight decrease in ash and extractives. Also, a decrease of 47% and 50% for lignin and hemicellulose content was observed, coupled with an increase of 71% for cellulose content. Fiber thermal stability was improved with the alkali treatment at about 10°C. Tensile strength of composites was found to be between 42 and 44 MPa with no significant statistical difference between coupons with either treated or untreated fibers. However, compared to neat PLA, composites with beer bran fibers present a decrease in tensile strength of 27%. Young modulus increases by 10% with treated fiber, compared to neat PLA. Flexural strength decreases in coupons with treated fiber (67.7 MPa), while flexural modulus increases (3.2 GPa) compared to neat PLA (83.3 MPa and 2.8 GPa, respectively). Izod impact test results showed an improvement of 99.4% in coupons with treated fibers - compared with neat PLA.

Keywords: beer bran, characterization, green composite, polylactic acid, surface treatment

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Authors: Aurea C. L. Lacerda, Paulo R. H. Moreno, Bruna M. P. Vianna, Cristina H. R. Serra, Airton Martin, André R. Baby, Vladi O. Consiglieri, Telma M. Kaneko

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The modified Franz cell is the most widely used model for in vitro permeation studies, however it still presents some disadvantages. Thus, some alternative methods have been developed such as Skin PAMPA, which is a bio- artificial membrane that has been applied for skin penetration estimation of xenobiotics based on HT permeability model consisting. Skin PAMPA greatest advantage is to carry out more tests, in a fast and inexpensive way. The membrane system mimics the stratum corneum characteristics, which is the primary skin barrier. The barrier properties are given by corneocytes embedded in a multilamellar lipid matrix. This layer is the main penetration route through the paracellular permeation pathway and it consists of a mixture of cholesterol, ceramides, and fatty acids as the dominant components. However, there is no consensus on the membrane composition. The objective of this work was to compare the performance among different bio-artificial membranes for studying the permeation in skin PAMPA system. Material and methods: In order to mimetize the lipid composition`s present in the human stratum corneum six membranes were developed. The membrane composition was equimolar mixture of cholesterol, ceramides 1-O-C18:1, C22, and C20, plus fatty acids C20 and C24. The membrane integrity assay was based on the transport of Brilliant Cresyl Blue, which has a low permeability; and Lucifer Yellow with very poor permeability and should effectively be completely rejected. The membrane characterization was performed using Confocal Laser Raman Spectroscopy, using stabilized laser at 785 nm with 10 second integration time and 2 accumulations. The membrane behaviour results on the PAMPA system were statistically evaluated and all of the compositions have shown integrity and permeability. The confocal Raman spectra were obtained in the region of 800-1200 cm-1 that is associated with the C-C stretches of the carbon scaffold from the stratum corneum lipids showed similar pattern for all the membranes. The ceramides, long chain fatty acids and cholesterol in equimolar ratio permitted to obtain lipid mixtures with self-organization capability, similar to that occurring into the stratum corneum. Conclusion: The artificial biological membranes studied for Skin PAMPA showed to be similar and with comparable properties to the stratum corneum.

Keywords: bio-artificial membranes, comparison, confocal Raman, skin PAMPA

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Authors: Wanwadee Neamsakul

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VARK learning style is an effective method of learning that could enhance all skills of the students like visual (V), auditory (A), read/write (R), and kinesthetic (K). This learning style benefits the students in terms of professional competencies, critical thinking and lifelong learning which are the desirable characteristics of the nursing students. This study aimed to develop a model of teaching and learning clinical practicum at postpartum ward for nursing students by using VARK learning styles, and evaluate the nursing students’ opinions about the developmental model. A methodology used for this study was research and development (R&D). The model was developed by focus group discussion with five obstetric nursing instructors who have experiences teaching Maternal Newborn and Midwifery I subject. The activities related to practices in the postpartum (PP) ward including all skills of VARK were assigned into the matrix table. The researcher asked the experts to supervise the model and adjusted the model following the supervision. Subsequently, it was brought to be tried out with the nursing students who practiced on the PP ward. Thirty third year nursing students from one of the northern Nursing Colleges, Academic year 2015 were purposive sampling. The opinions about the satisfaction of the model were collected using a questionnaire which was tested for its validity and reliability. Data were analyzed using descriptive statistics. The developed model composed of 27 activities. Seven activities were developed as enhancement of visual skills for the nursing students (25.93%), five activities as auditory skills (18.52%), six activities as read and write skills (22.22%), and nine activities as kinesthetic skills (33.33%). Overall opinions about the model were reported at the highest level of average satisfaction (mean=4.63, S.D=0.45). In the aspects of visual skill (mean=4.80, S.D=0.45) was reported at the highest level of average satisfaction followed by auditory skill (mean=4.62, S.D=0.43), read and write skill (mean=4.57, S.D=0.46), and kinesthetic skill (mean=4.53, S.D=0.45) which were reported at the highest level of average satisfaction, respectively. The nursing students reported that the model could help them employ all of their skills during practicing and taking care of the postpartum women and newborn babies. They could establish self-confidence while providing care and felt proud of themselves by the benefits of the model. It can be said that using VARK learning style to develop the model could enhance both nursing students’ competencies and positive attitude towards the nursing profession. Consequently, they could provide quality care for postpartum women and newborn babies effectively in the long run.

Keywords: model, nursing students, postpartum ward, teaching and learning clinical practicum

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Authors: Iwan Cony Setiadi, Aulia M. T. Nasution

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The multispectral imaging (MSI) technique has been used for skin analysis, especially for distant mapping of in-vivo skin chromophores by analyzing spectral data at each reflected image pixel. For ergonomic purpose, our multispectral imaging system is decomposed in two parts: a light source compartment based on LED with 11 different wavelenghts and a monochromatic 8-Bit CCD camera with C-Mount Objective Lens. The software based on GUI MATLAB to control the system was also developed. Our system provides 11 monoband images and is coupled with a software reconstructing hyperspectral cubes from these multispectral images. In this paper, we proposed a new method to build a hyperspectral reflectance cube based on artificial neural network algorithm. After preliminary corrections, a neural network is trained using the 32 natural color from X-Rite Color Checker Passport. The learning procedure involves acquisition, by a spectrophotometer. This neural network is then used to retrieve a megapixel multispectral cube between 380 and 880 nm with a 5 nm resolution from a low-spectral-resolution multispectral acquisition. As hyperspectral cubes contain spectra for each pixel; comparison should be done between the theoretical values from the spectrophotometer and the reconstructed spectrum. To evaluate the performance of reconstruction, we used the Goodness of Fit Coefficient (GFC) and Root Mean Squared Error (RMSE). To validate reconstruction, the set of 8 colour patches reconstructed by our MSI system and the one recorded by the spectrophotometer were compared. The average GFC was 0.9990 (standard deviation = 0.0010) and the average RMSE is 0.2167 (standard deviation = 0.064).

Keywords: multispectral imaging, reflectance cube, spectral reconstruction, artificial neural network

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Authors: Lavanya Ratnabala, Robinroy Peter, E. Y. A. Charles

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This research paper addresses the challenges of exploration and navigation in unknown environments from an evolutionary swarm robotics perspective. Path formation plays a crucial role in enabling cooperative swarm robots to accomplish these tasks. The paper presents a method called the sub-goal-based path formation, which establishes a path between two different locations by exploiting visually connected sub-goals. Simulation experiments conducted in the Argos simulator demonstrate the successful formation of paths in the majority of trials. Furthermore, the paper tackles the problem of inter-collision (traffic) among a large number of robots engaged in path formation, which negatively impacts the performance of the sub-goal-based method. To mitigate this issue, a task allocation strategy is proposed, leveraging local communication protocols and light signal-based communication. The strategy evaluates the distance between points and determines the required number of robots for the path formation task, reducing unwanted exploration and traffic congestion. The performance of the sub-goal-based path formation and task allocation strategy is evaluated by comparing path length, time, and resource reduction against the A* algorithm. The simulation experiments demonstrate promising results, showcasing the scalability, robustness, and fault tolerance characteristics of the proposed approach.

Keywords: swarm, path formation, task allocation, Argos, exploration, navigation, sub-goal

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Authors: Khalid Hussein, Waleed Abdalati, Pakorn Petchprayoon, Khaula Alkaabi

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In this study, we integrated ASTER thermal data with an area-based spatial insolation model to identify and delineate geothermally active areas in Yellowstone National Park (YNP). Two pairs of L1B ASTER day- and nighttime scenes were used to calculate land surface temperature. We employed the Emissivity Normalization Algorithm which separates temperature from emissivity to calculate surface temperature. We calculated the incoming solar radiation for the area covered by each of the four ASTER scenes using an insolation model and used this information to compute temperature due to solar radiation. We then identified the statistical thermal anomalies using land surface temperature and the residuals calculated from modeled temperatures and ASTER-derived surface temperatures. Areas that had temperatures or temperature residuals greater than 2σ and between 1σ and 2σ were considered ASTER-modeled thermal anomalies. The areas identified as thermal anomalies were in strong agreement with the thermal areas obtained from the YNP GIS database. Also the YNP hot springs and geysers were located within areas identified as anomalous thermal areas. The consistency between our results and known geothermally active areas indicate that thermal remote sensing data, integrated with a spatial-based insolation model, provides an effective means for identifying and locating areas of geothermal activities over large areas and rough terrain.

Keywords: thermal remote sensing, insolation model, land surface temperature, geothermal anomalies

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Authors: Huijie Wang, Bin Zhang

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In China, the middle reaches of the Yangtze River are well-developed, boasting a wealth of different types of watershed landscape. In this regard, landscape character assessment (LCA) can serve as a basis for protection, management and planning of trans-regional watershed landscape types. For this study, we chose the middle reaches of the Yangtze River in Wuhan metropolitan area as our study site, wherein the water system consists of rich variety in landscape types. We analyzed trans-regional data to cluster and identify types of landscape characteristics at two levels. 55 basins were analyzed as variables with topography, land cover and river system features in order to identify the watershed landscape character types. For watershed landscape, drainage density and degree of curvature were specified as special variables to directly reflect the regional differences of river system features. Then, we used the principal component analysis (PCA) method and hierarchical clustering algorithm based on the geographic information system (GIS) and statistical products and services solution (SPSS) to obtain results for clusters of watershed landscape which were divided into 8 characteristic groups. These groups highlighted watershed landscape characteristics of different river systems as well as key landscape characteristics that can serve as a basis for targeted protection of watershed landscape characteristics, thus helping to rationally develop multi-value landscape resources and promote coordinated development of trans-regions.

Keywords: GIS, hierarchical clustering, landscape character, landscape typology, principal component analysis, watershed

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Authors: Ahmet Erten, Adil Mustafa, Ayşenur Eser, Özlem Yalçın

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We present a new viscometer based on a microfluidic chip with elastic high aspect ratio micropillar arrays. The displacement of pillar tips in flow direction can be used to analyze viscosity of liquid. In our work, Computational Fluid Dynamics (CFD) is used to analyze pillar displacement of various micropillar array configurations in flow direction at different viscosities. Following CFD optimization, micro-CNC based rapid prototyping is used to fabricate molds for microfluidic chips. Microfluidic chips are fabricated out of polydimethylsiloxane (PDMS) using soft lithography methods with molds machined out of aluminum. Tip displacements of micropillar array (300 µm in diameter and 1400 µm in height) in flow direction are recorded using a microscope mounted camera, and the displacements are analyzed using image processing with an algorithm written in MATLAB. Experiments are performed with water-glycerol solutions mixed at 4 different ratios to attain 1 cP, 5 cP, 10 cP and 15 cP viscosities at room temperature. The prepared solutions are injected into the microfluidic chips using a syringe pump at flow rates from 10-100 mL / hr and the displacement versus flow rate is plotted for different viscosities. A displacement of around 1.5 µm was observed for 15 cP solution at 60 mL / hr while only a 1 µm displacement was observed for 10 cP solution. The presented viscometer design optimization is still in progress for better sensitivity and accuracy. Our microfluidic viscometer platform has potential for tailor made microfluidic chips to enable real time observation and control of viscosity changes in biological or chemical reactions.

Keywords: Computational Fluid Dynamics (CFD), high aspect ratio, micropillar array, viscometer

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