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Authors: Lawrence Koech, Ray Everson, Hein Neomagus, Hilary Rutto

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Wet Flue gas desulphurization (FGD) systems are commonly used to remove sulphur dioxide from flue gas by contacting it with limestone in aqueous phase which is obtained by dissolution. Dissolution is important as it affects the overall performance of a wet FGD system. In the present study, effects of pH, stirring speed, solid to liquid ratio and acid concentration on the dissolution of limestone using an organic acid (adipic acid) were investigated. This was investigated using the pH stat apparatus. Calcium ions were analyzed at the end of each experiment using Atomic Absorption (AAS) machine.

Keywords: desulphurization, limestone, dissolution, pH stat apparatus

Procedia PDF Downloads 461

Authors: Xabier Olaz Moratinos

Abstract:

Drone navigation is critical, particularly during the initial phases, such as the initial ascension, where pilots may fail due to strong external interferences that could potentially lead to a crash. In this ongoing work, a drone has been successfully trained to perform an ascent of up to 6 meters at speeds with external disturbances pushing it up to 24 mph, with the DQN algorithm managing external forces affecting the system. It has been demonstrated that the system can control its height, position, and stability in all three axes (roll, pitch, and yaw) throughout the process. The learning process is carried out in the Gazebo simulator, which emulates interferences, while ROS is used to communicate with the agent.

Keywords: machine learning, DQN, gazebo, navigation

Procedia PDF Downloads 113

Authors: Dong Seop Lee, Byung Sik Kim

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In the 4th Industrial Revolution, various intelligent technologies have been developed in many fields. These artificial intelligence technologies are applied in various services, including disaster management. Disaster information management does not just support disaster work, but it is also the foundation of smart disaster management. Furthermore, it gets historical disaster information using artificial intelligence technology. Disaster information is one of important elements of entire disaster cycle. Disaster information management refers to the act of managing and processing electronic data about disaster cycle from its’ occurrence to progress, response, and plan. However, information about status control, response, recovery from natural and social disaster events, etc. is mainly managed in the structured and unstructured form of reports. Those exist as handouts or hard-copies of reports. Such unstructured form of data is often lost or destroyed due to inefficient management. It is necessary to manage unstructured data for disaster information. In this paper, the Optical Character Recognition approach is used to convert handout, hard-copies, images or reports, which is printed or generated by scanners, etc. into electronic documents. Following that, the converted disaster data is organized into the disaster code system as disaster information. Those data are stored in the disaster database system. Gathering and creating disaster information based on Optical Character Recognition for unstructured data is important element as realm of the smart disaster management. In this paper, Korean characters were improved to over 90% character recognition rate by using upgraded OCR. In the case of character recognition, the recognition rate depends on the fonts, size, and special symbols of character. We improved it through the machine learning algorithm. These converted structured data is managed in a standardized disaster information form connected with the disaster code system. The disaster code system is covered that the structured information is stored and retrieve on entire disaster cycle such as historical disaster progress, damages, response, and recovery. The expected effect of this research will be able to apply it to smart disaster management and decision making by combining artificial intelligence technologies and historical big data.

Keywords: disaster information management, unstructured data, optical character recognition, machine learning

Procedia PDF Downloads 129

Authors: Rosine Angelique Uwacu

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Poor household sanitation is rife in Uganda, especially in Kampala. This study was carried out with he goal of establishing the main causes and effects of poor household sanitation in Kansanga parish. The study objectively sought to: To identify various ways through which wastes are generated and disposed of in Kansanga parish, identify different hygiene procedures/behaviors of waste handling in Kansanga parish and assess health effects of poor household sanitation and suggest the recommended appropriate measures of addressing cases of lack of hygiene in Kansanga parish. The study used a survey method where cluster sampling was employed. This is because there is no register of population or sufficient information, or geographic distribution of individuals is widely scattered. Data was collected through the use of interviews accompanied by observation and questionnaires. The study involved a sample of 100 households. The study revealed that; some households use wheeled bin collection, skip hire and roll on/off contained others take their wastes to refuse collection vehicles. Surprisingly, majority of the households submitted that they use polythene bags 'Kavera' and at times plastic sacs to dispose of their wastes which are dumped in drainage patterns or dustbins and other illegal dumping site. The study showed that washing hands with small jerrycans after using the toilet was being adopted by most households as there were no or few other alternatives. The study revealed that the common health effects that come as a result of poor household sanitation in Kansanga Parish are diseases outbreaks such as malaria, typhoid and diarrhea. Finally, the study gave a number of recommendations or suggestions on maintaining and achieving an adequate household sanitation in Kansanga Parish such as sensitization of community members by their leaders like Local Counselors could help to improve the situation, establishment of community sanitation days for people to collectively and voluntarily carry out good sanitation practices like digging trenches, burning garbage and proper waste management and disposal. Authorities like Kampala Capital City Authority should distribute dumping containers or allocate dumping sites where people can dispose of their wastes preferably at a minimum cost for proper management.

Keywords: household sanitation, kansanga parish, Uganda, waste

Procedia PDF Downloads 190

Authors: Anupam Sibal

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Apollo Quality Program(AQP) was launched across the Apollo Group of Hospitals to address the four patient safety areas; Safety during Clinical Handovers, Medication Safety, Surgical Safety and the six International Patient Safety Goals(IPSGs) of JCI. A measurable, online, quality dashboard covering 20 process and outcome parameters was devised for monthly monitoring. The expected outcomes were also defined and categorized into green, yellow and red ranges. An audit methodology was also devised to check the processes for the measurable dashboard. Documented clinical handovers were introduced for the first time at many locations for in-house patient transfer, nursing-handover, and physician-handover. Prototype forms using the SBAR format were made. Patient-identifiers, read-back for verbal orders, safety of high-alert medications, site marking and time-outs and falls risk-assessment were introduced for all hospitals irrespective of accreditation status. Measurement of Surgical-Site-Infection (SSI) for 30 days postoperatively, was done. All hospitals now tracked the time of administration of antimicrobial prophylaxis before surgery. Situations with high risk of retention of foreign body were delineated and precautionary measures instituted. Audit of medications prescribed in the discharge summaries was made uniform. Formularies, prescription-audits and other means for reduction of medication errors were implemented. There is a marked increase in the compliance to processes and patient safety outcomes. Compliance to read-back for verbal orders rose from 86.83% in April’11 to 96.95% in June’15, to policy for high alert medications from 87.83% to 98.82%, to use of measures to prevent wrong-site, wrong-patient, wrong procedure surgery from 85.75% to 97.66%, to hand-washing from 69.18% to 92.54%, to antimicrobial prophylaxis within one hour before incision from 79.43% to 93.46%. Percentage of patients excluded from SSI calculation due to lack of follow-up for the requisite time frame decreased from 21.25% to 10.25%. The average AQP scores for all Apollo Hospitals improved from 62 in April’11 to 87.7 in Jun’15.

Keywords: clinical handovers, international patient safety goals, medication safety, surgical safety

Procedia PDF Downloads 257

Authors: Mpho Mokoatle, Darlington Mapiye, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

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Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on $k$-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms.

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 167

Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

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Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 159

Authors: Walid A. M. Ghoneim, Hamdy A. Ashour, Asmaa E. Abdo

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In this paper, linear analysis of a Switched Reluctance Generator (SRG) model is applied on the most common configurations (4/2, 6/4 and 8/6) for both conventional short-pitched and fully-pitched designs, in order to determine the optimum stator/rotor pole angles at which the maximum output voltage is generated per unit excitation current. This study is focused on SRG analysis and design as a proposed solution for renewable energy applications, such as wind energy conversion systems. The world’s potential to develop the renewable energy technologies through dedicated scientific researches was the motive behind this study due to its positive impact on economy and environment. In addition, the problem of rare earth metals (Permanent magnet) caused by mining limitations, banned export by top producers and environment restrictions leads to the unavailability of materials used for rotating machines manufacturing. This challenge gave authors the opportunity to study, analyze and determine the optimum design of the SRG that has the benefit to be free from permanent magnets, rotor windings, with flexible control system and compatible with any application that requires variable-speed operation. In addition, SRG has been proved to be very efficient and reliable in both low-speed or high-speed applications. Linear analysis was performed using MATLAB simulations based on the (Modified generalized matrix approach) of Switched Reluctance Machine (SRM). About 90 different pole angles combinations and excitation patterns were simulated through this study, and the optimum output results for each case were recorded and presented in detail. This procedure has been proved to be applicable for any SRG configuration, dimension and excitation pattern. The delivered results of this study provide evidence for using the 4-phase 8/6 fully pitched SRG as the main optimum configuration for the same machine dimensions at the same angular speed.

Keywords: generalized matrix approach, linear analysis, renewable applications, switched reluctance generator

Procedia PDF Downloads 198

Authors: Manpreet Kaur, Cheol-Hong Min

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The increasing advancement in the robotics equipped with machine vision sensors applied to precision agriculture is a demanding solution for various problems in the agricultural farms. An important issue related with the machine vision system concerns crop row and weed detection. This paper proposes an automatic furrow detection system based on real-time processing for identifying crop rows in maize fields in the presence of weed. This vision system is designed to be installed on the farming vehicles, that is, submitted to gyros, vibration and other undesired movements. The images are captured under image perspective, being affected by above undesired effects. The goal is to identify crop rows for vehicle navigation which includes weed removal, where weeds are identified as plants outside the crop rows. The images quality is affected by different lighting conditions and gaps along the crop rows due to lack of germination and wrong plantation. The proposed image processing method consists of four different processes. First, image segmentation based on HSV (Hue, Saturation, Value) decision tree. The proposed algorithm used HSV color space to discriminate crops, weeds and soil. The region of interest is defined by filtering each of the HSV channels between maximum and minimum threshold values. Then the noises in the images were eliminated by the means of hybrid median filter. Further, mathematical morphological processes, i.e., erosion to remove smaller objects followed by dilation to gradually enlarge the boundaries of regions of foreground pixels was applied. It enhances the image contrast. To accurately detect the position of crop rows, the region of interest is defined by creating a binary mask. The edge detection and Hough transform were applied to detect lines represented in polar coordinates and furrow directions as accumulations on the angle axis in the Hough space. The experimental results show that the method is effective.

Keywords: furrow detection, morphological, HSV, Hough transform

Procedia PDF Downloads 231

Authors: Abdulmalek Ahmed

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Formation pressure is the main function that affects drilling operation economically and efficiently. Knowing the pore pressure and the parameters that affect it will help to reduce the cost of drilling process. Many empirical models reported in the literature were used to calculate the formation pressure based on different parameters. Some of these models used only drilling parameters to estimate pore pressure. Other models predicted the formation pressure based on log data. All of these models required different trends such as normal or abnormal to predict the pore pressure. Few researchers applied artificial intelligence (AI) techniques to predict the formation pressure by only one method or a maximum of two methods of AI. The objective of this research is to predict the pore pressure based on both drilling parameters and log data namely; weight on bit, rotary speed, rate of penetration, mud weight, bulk density, porosity and delta sonic time. A real field data is used to predict the formation pressure using five different artificial intelligence (AI) methods such as; artificial neural networks (ANN), radial basis function (RBF), fuzzy logic (FL), support vector machine (SVM) and functional networks (FN). All AI tools were compared with different empirical models. AI methods estimated the formation pressure by a high accuracy (high correlation coefficient and low average absolute percentage error) and outperformed all previous. The advantage of the new technique is its simplicity, which represented from its estimation of pore pressure without the need of different trends as compared to other models which require a two different trend (normal or abnormal pressure). Moreover, by comparing the AI tools with each other, the results indicate that SVM has the advantage of pore pressure prediction by its fast processing speed and high performance (a high correlation coefficient of 0.997 and a low average absolute percentage error of 0.14%). In the end, a new empirical correlation for formation pressure was developed using ANN method that can estimate pore pressure with a high precision (correlation coefficient of 0.998 and average absolute percentage error of 0.17%).

Keywords: Artificial Intelligence (AI), Formation pressure, Artificial Neural Networks (ANN), Fuzzy Logic (FL), Support Vector Machine (SVM), Functional Networks (FN), Radial Basis Function (RBF)

Procedia PDF Downloads 149

Authors: Luo Jiaoyang, Yu Hongyang

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In this paper, we explore a method that can be used in the working scene of intelligent industrial robots to confirm the identity information of operators to ensure that the robot executes instructions in a sufficiently safe environment. This approach uses three information modalities, namely visible light, depth, and sound. We explored a variety of fusion modes for the three modalities and finally used the joint feature learning method to improve the performance of the model in the case of noise compared with the single-modal case, making the maximum noise in the experiment. It can also maintain an accuracy rate of more than 90%.

Keywords: multimodal, kinect, machine learning, distance image

Procedia PDF Downloads 79

Authors: A. Sopharak, B. Uyyanonvara, S. Barman

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Diabetic retinopathy is characterized by the development of retinal microaneurysms. The damage can be prevented if disease is treated in its early stages. In this paper, we are comparing Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers for automatic microaneurysm detection in images acquired through non-dilated pupils. The Nearest Neighbor classifier is used as a baseline for comparison. Detected microaneurysms are validated with expert ophthalmologists’ hand-drawn ground-truths. The sensitivity, specificity, precision and accuracy of each method are also compared.

Keywords: diabetic retinopathy, microaneurysm, naive Bayes classifier, SVM classifier

Procedia PDF Downloads 329

Authors: Swarupa Shashi Senivarapu Vemuri

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Curry leaf (Murraya koeinigii (L.)) exported from India had insecticide residues above maximum residue limits, which are hazardous to consumer health and caused rejection of the commodity at the point of entry in Europe and middle east resulting in a check on export of curry leaf. Hence to study current pesticide usage patterns in major curry leaf growing areas, a survey on pesticide use pattern was carried out in curry leaf growing areas in Guntur districts of Andhra Pradesh during 2014-15, by interviewing farmers growing curry leaf utilizing the questionnaire to assess their knowledge and practices on crop cultivation, general awareness on pesticide recommendations and use. Education levels of farmers are less, where 13.96 per cent were only high school educated, and 13.96% were illiterates. 18.60% farmers were found cultivating curry leaf crop in less than 1 acre of land, 32.56% in 2-5 acres, 20.93% in 5-10 acres and 27.91% of the farmers in more than 10 acres of land. Majority of the curry leaf farmers (93.03%) used pesticide mixtures rather than applying single pesticide at a time, basically to save time, labour, money and to combat two or more pests with single spray. About 53.48% of farmers applied pesticides at 2 days interval followed by 34.89% of the farmers at 4 days interval, and about 11.63% of the farmers sprayed at weekly intervals. Only 27.91% of farmers thought that the quantity of pesticides used at their farm is adequate, 90.69% of farmers had perception that pesticides are helpful in getting good returns. 83.72% of farmers felt that crop change is the only way to control sucking pests which damages whole crop. About 4.65% of the curry leaf farmers opined that integrated pest management practices are alternative to pesticides and only 11.63% of farmers felt natural control as an alternative to pesticides. About 65.12% of farmers had perception that high pesticide dose will give higher yields. However, in general, Curry leaf farmers preferred to contact pesticide dealers (100%) and were not interested in contacting either agricultural officer or a scientist. Farmers were aware of endosulfan ban 93.04%), in contrast, only 65.12, per cent of farmers knew about the ban of monocrotophos on vegetables. Very few farmers knew about pesticide residues and decontamination by washing. Extension educational interventions are necessary to produce fresh curry leaf free from pesticide residues.

Keywords: Curry leaf, decontamination, endosulfan, leaf roller, psyllids, tetranychid mite

Procedia PDF Downloads 335

Authors: Nidhi Gopal

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Routing is the process of moving a packet of data from source to destination and enables messages to pass from one computer to another and eventually reach the target machine. A router is a networking device that forwards data packets between computer networks. It is connected to two or more data lines from different networks (as opposed to a network switch, which connects data lines from one single network). This paper mainly emphasizes upon the study of router device, its top level architecture, and how various sub-modules of router i.e. Register, FIFO, FSM and Synchronizer are synthesized, and simulated and finally connected to its top module.

Keywords: data packets, networking, router, routing

Procedia PDF Downloads 814

Authors: Debasish Das, Mainak Mitra, A.Chaudhuri

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For the purpose of producing moisture vapor permeable waterproof cotton fabric to be used for protective apparel against rain, cotton fabric was coated with the blend of natural rubber and chloroprene rubber containing ammonium acetate as the water-soluble salt, employing a calendar coating technique. Rubber formulations also contained filler, homogenizer, and a typical sulphur curing system. Natural rubber and chloroprene blend in the blend ratio of 30: 70, containing 25 parts of sodium acetate per hundred parts of rubber was coated on the fabric. The coated fabric was vulcanized thereafter at 140oC for 3 h. Coated and vulcanized fabric was subsequently dipped in water for 45 min, followed by drying in air. Such set of treatments produced optimum results. Coated, vulcanized, washed and dried cotton fabric showed optimum developments in the property profiles in respect of waterproofness, breathability as revealed by moisture vapor transmission rate, coating adhesion, tensile properties, abrasion resistance, flex endurance and fire retardancy. Incorporation of highly water-soluble ammonium acetate salt in the coating formulation and their subsequent removal from vulcanized coated layer affected by post washing in consequent to dipping in the water-bath produced holes of only a few microns in the coating matrix of the fabric. Such microporous membrane formed on the cotton fabric allowed only transportation of moisture vapor through them, giving a moisture vapor transmission rate of 3734 g/m2/24h, while acting as a barrier for large liquid water droplet resisting 120cm of the water column in the hydrostatic water-head tester, rendering the coated cotton fabric waterproof. Examination of surface morphology of vulcanized coating by scanning electron microscopy supported the mechanism proposed for development of breathable waterproof layer on cotton fabric by the process employed above. Such process provides an easy and cost-effective route for achieving moisture vapor permeable waterproof cotton.

Keywords: moisture vapour permeability, waterproofness, chloroprene, calendar coating, coating adhesion, fire retardancy

Procedia PDF Downloads 254

Authors: Lou J. Pino, Mark Campbell, Matthew J. Kennedy, Ashleigh C. Kennedy

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A concussion is complex and nuanced, with cognitive rest being a key component of recovery. Cognitive overexertion during rehabilitation from a concussion is associated with delayed recovery. However, daily living imposes cognitive demands that may be unavoidable and difficult to quantify. Therefore, a portable tool capable of alerting patients before cognitive overexertion occurs could allow patients to maintain their quality of life while preventing symptoms and recovery setbacks. EEG allows for a sensitive measure of cognitive exertion. Clinical 32-lead EEG headsets are not practical for day-to-day concussion rehabilitation management. However, there are now commercially available and affordable portable EEG headsets. Thus, these headsets can potentially be used to continuously monitor cognitive exertion during mental tasks to alert the wearer of overexertion, with the aim of preventing the occurrence of symptoms to speed recovery times. The objective of this study was to test an algorithm for predicting cognitive exertion from EEG data collected from a portable headset. EEG data were acquired from 10 participants (5 males, 5 females). Each participant wore a portable 4 channel EEG headband while completing 10 tasks: rest (eyes closed), rest (eyes open), three levels of the increasing difficulty of logic puzzles, three levels of increasing difficulty in multiplication questions, rest (eyes open), and rest (eyes closed). After each task, the participant was asked to report their perceived level of cognitive exertion using the NASA Task Load Index (TLX). Each participant then completed a second session on a different day. A customized machine learning model was created using data from the first session. The performance of each model was then tested using data from the second session. The mean correlation coefficient between TLX scores and predicted cognitive exertion was 0.75 ± 0.16. The results support the efficacy of the algorithm for predicting cognitive exertion. This demonstrates that the algorithms developed in this study used with portable EEG devices have the potential to aid in the concussion recovery process by monitoring and warning patients of cognitive overexertion. Preventing cognitive overexertion during recovery may reduce the number of symptoms a patient experiences and may help speed the recovery process.

Keywords: cognitive activity, EEG, machine learning, personalized recovery

Procedia PDF Downloads 220

Authors: Farah Al-Fityani, Aljohara Alsowail, Shatha Bindawood, Heba Balrbeah

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Locket is a popular app that lets users share spontaneous photos with a close circle of friends. The app offers a unique way to stay connected with loved ones by allowing users to see glimpses of their day through photos displayed on a widget on their home screen. This summary outlines the process of developing an app like Locket, highlighting the importance of user privacy and security. It also details the findings of a study on user engagement with the Locket app, revealing positive sentiment towards its features and concept but also identifying areas for improvement. Overall, the summary portrays Locket as a successful app that is changing the way people connect on social media.

Keywords: locket, app, machine learning, connect

Procedia PDF Downloads 46

Authors: Xiaoli Zhang, Xiongfei Li, Yuncong Feng

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ROC curves, as a widely used evaluating tool in machine learning field, are the tradeoff of true positive rate and negative rate. However, they are blamed for ignoring some vital information in the evaluation process, such as the amount of information about the target that each instance carries, predicted score given by each classification model to each instance. Hence, in this paper, a new classification performance method is proposed by extending the Receiver Operator Characteristic (ROC) curves to 3D space, which is denoted as 3D ROC Histogram. In the histogram, the

Keywords: classification, performance evaluation, receiver operating characteristic histogram, hardness prediction

Procedia PDF Downloads 314

Authors: Sisaynew Tesfaw Admassu

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The strengthening of timber beams can be necessary for several reasons including the increase of live loads (possible in a historical building for a change of destination of use or upgrading to meet new requirements), the reduction of the resistant cross-sections following deterioration (attacks of biological agents such as fungi, and insects) or traumatic events (fires) and the excess of deflection in the members. The main purpose of strengthening an element is not merely to repair it, but also to prevent and minimize the appearance of future problems. This study did an experimental investigation on the behavior of reference and strengthened solid timber beams. The strengthening materials used in this study were CSM-450 glass fiber and steel materials for both flexural and shear strengthening techniques. Twenty-two solid timber beams of Juniperus procera (TID) species with the dimensions of 60 x 90 x 780 mm were used in the present study. The binding material to bond the strengthening materials with timber was general-purpose resin with Luperox® K10 MEKP catalyst. Three beams were used as control beams (unstrengthen beams) while the remaining nineteen beams were strengthened using the strengthening materials for flexure and shear. All the beams were tested for three points loading to failure by using a Universal Testing Machine, UTM-600kN machine. The experimental results showed that the strengthened beams performed better than the unstrengthen beams. The experimental result of flexural strengthened beams showed that the load-bearing capacity of strengthened beams increased between 16.34 – 42.55%. Four layers of Glass Fiber Reinforced polymer on the tension side of the beams was shown to be the most effective way to enhance load-bearing capacity. The strengthened beams also have an enhancement in their flexural stiffness. The stiffness of flexural strengthened beams was increased between 1.18 – 65.53% as compared to the control beams. The highest increment in stiffness has occurred on beams strengthened using 2x60 mm steel plates. The shear-strengthened beams showed a relatively small amount of performance as compared to flexural-strengthened beams; the reason is that the beams are sufficient for shear. The polyester resin used in the experimental work showed good performance in bonding agents between materials. The resin showed more effectiveness in GFRP materials than steel materials.

Keywords: heritage structures, strengthening, stiffness, adhesive, polyester resin, steel plates

Procedia PDF Downloads 74

Authors: Xabier Olaz Moratinos

Abstract:

Drone navigation is critical, particularly during the initial phases, such as the initial ascension, where pilots may fail due to strong external interferences that could potentially lead to a crash. In this ongoing work, a drone has been successfully trained to perform an ascent of up to 6 meters at speeds with external disturbances pushing it up to 24 mph, with the DQN algorithm managing external forces affecting the system. It has been demonstrated that the system can control its height, position, and stability in all three axes (roll, pitch, and yaw) throughout the process. The learning process is carried out in the Gazebo simulator, which emulates interferences, while ROS is used to communicate with the agent.

Keywords: machine learning, DQN, Gazebo, navigation

Procedia PDF Downloads 77

Authors: R. A. C. H. Seneviratne, M. Gunawardana, R. P. N. P. Rajapakse

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To address the micronutrient deficiencies in the Asian region, the World Food Program in its current mandate highlights the requirement of employing efficient fortification of micronutrients in rice, under the program 'Scaling-up Rice Fortification in Asia'. The current industrial methods of rice fortification with micronutrients are not promising due to poor permeation or retention of fortificants. This study was carried out to develop a method to improve fortification of micronutrients in rice by removing the air barriers for diffusing micronutrients through the husk. For the purpose, soaking stage of paddy was coupled with vacuum (- 0.6 bar) for different time periods. Both long and short grain varieties of paddy (BG 352 and BG 358, respectively) initially tested for water uptake during hot soaking (70 °C) under vacuum (28.5 and 26.15%, respectively) were significantly (P < 0.05) higher than that of non-vacuum conditions (25.24 and 25.45% respectively), exhibiting the effectiveness of water diffusion into the rice grains through the cleared pores under negative pressure. To fortify the selected micronutrients (iron and zinc), paddy was vacuum-soaked in Fe2+ or Zn2+ solutions (500 ppm) separately for one hour, and continued soaking for another 3.5 h without vacuum. Significantly (P<0.05) higher amounts of Fe2+ and Zn2+ were observed throughout the soaking period, in both short and long grain varieties of rice compared to rice treated without vacuum. To achieve the recommended limits of World Food Program standards for fortified iron (40-48 mg/kg) and zinc (60-72 mg/kg) in rice, soaking was done with different concentrations of Fe2+ or Zn2+ for varying time periods. For both iron and zinc fortifications, hot soaking (70 °C) in 400 ppm solutions under vacuum (- 0.6 bar) during the first hour followed by 2.5 h under atmospheric pressure exhibited the optimum fortification (Fe2+: 46.59±0.37 ppm and Zn2+: 67.24±1.36 ppm) with a greater significance (P < 0.05) compared to the controls (Fe2+: 38.84±0.62 ppm and Zn2+: 52.55±0.55 ppm). This finding was further confirmed by the XRF images, clearly showing a greater fixation of Fe2+ and Zn2+ in the rice grains under vacuum treatment. Moreover, there were no significant (P>0.05) differences among both Fe2+ and Zn2+ contents in fortified rice even after polishing and washing, confirming their greater retention. A seven point hedonic scale showed that the overall acceptability for both iron and zinc fortified rice were significantly (P < 0.05) higher than the parboiled rice without fortificants. With all the drawbacks eliminated, per kilogram cost will be less than US$ 1 for both iron and zinc fortified rice. The new method of rice fortification studied and developed in this research, can be claimed as the best method in comparison to other rice fortification methods currently deployed.

Keywords: fortification, vacuum assisted diffusion, micronutrients, parboiling

Procedia PDF Downloads 253

Authors: Filip Rusak, Yulia Arzhaeva, Dadong Wang

Abstract:

Machine learning based Computer-Aided Diagnosis (CAD) is gaining much popularity in medical imaging and diagnostic radiology. However, it requires a large amount of high quality and labeled training image datasets. The training images may come from different sources and be acquired from different radiography machines produced by different manufacturers, digital or digitized copies of film radiographs, with various sizes as well as different pixel intensity distributions. In this paper, a content-aware image augmentation method is presented to deal with these variations. The results of the proposed method have been validated graphically by plotting the removed and added seams of pixels on original images. Two different chest X-ray (CXR) datasets are used in the experiments. The CXRs in the datasets defer in size, some are digital CXRs while the others are digitized from analog CXR films. With the proposed content-aware augmentation method, the Seam Carving algorithm is employed to resize CXRs and the corresponding labels in the form of image masks, followed by histogram matching used to normalize the pixel intensities of digital radiography, based on the pixel intensity values of digitized radiographs. We implemented the algorithms, resized the well-known Montgomery dataset, to the size of the most frequently used Japanese Society of Radiological Technology (JSRT) dataset and normalized our digital CXRs for testing. This work resulted in the unified off-the-shelf CXR dataset composed of radiographs included in both, Montgomery and JSRT datasets. The experimental results show that even though the amount of augmentation is large, our algorithm can preserve the important information in lung fields, local structures, and global visual effect adequately. The proposed method can be used to augment training and testing image data sets so that the trained machine learning model can be used to process CXRs from various sources, and it can be potentially used broadly in any medical imaging applications.

Keywords: computer-aided diagnosis, image augmentation, lung segmentation, medical imaging, seam carving

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Authors: Rosa Figueroa, Christopher Flores

Abstract:

Text categorization is the problem of assigning a new document to a set of predetermined categories, on the basis of a training set of free-text data that contains documents whose category membership is known. To train a classification model, it is necessary to extract characteristics in the form of tokens that facilitate the learning and classification process. In text categorization, the feature extraction process involves the use of word sequences also known as N-grams. In general, it is expected that documents belonging to the same category share similar features. The Smith-Waterman (SW) algorithm is a dynamic programming algorithm that performs a local sequence alignment in order to determine similar regions between two strings or protein sequences. This work explores the use of SW algorithm as an alternative to feature extraction in text categorization. The dataset used for this purpose, contains 2,610 annotated documents with the classes Obese/Non-Obese. This dataset was represented in a matrix form using the Bag of Word approach. The score selected to represent the occurrence of the tokens in each document was the term frequency-inverse document frequency (TF-IDF). In order to extract features for classification, four experiments were conducted: the first experiment used SW to extract features, the second one used unigrams (single word), the third one used bigrams (two word sequence) and the last experiment used a combination of unigrams and bigrams to extract features for classification. To test the effectiveness of the extracted feature set for the four experiments, a Support Vector Machine (SVM) classifier was tuned using 20% of the dataset. The remaining 80% of the dataset together with 5-Fold Cross Validation were used to evaluate and compare the performance of the four experiments of feature extraction. Results from the tuning process suggest that SW performs better than the N-gram based feature extraction. These results were confirmed by using the remaining 80% of the dataset, where SW performed the best (accuracy = 97.10%, weighted average F-measure = 97.07%). The second best was obtained by the combination of unigrams-bigrams (accuracy = 96.04, weighted average F-measure = 95.97) closely followed by the bigrams (accuracy = 94.56%, weighted average F-measure = 94.46%) and finally unigrams (accuracy = 92.96%, weighted average F-measure = 92.90%).

Keywords: comorbidities, machine learning, obesity, Smith-Waterman algorithm

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Authors: Zafer Yüce, Paşa Yayla, Alev Taşkın

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There are heaps of analytical methods to estimate the crack growth life of a component. Soft computing methods have an increasing trend in predicting fatigue life. Their ability to build complex relationships and capability to handle huge amounts of data are motivating researchers and industry professionals to employ them for challenging problems. This study focuses on soft computing methods, especially random forest regressors and artificial neural networks with hyperparameter optimization algorithms such as grid search and random grid search, to estimate the crack growth life of an aircraft wing splice joint under variable amplitude loading. TensorFlow and Scikit-learn libraries of Python are used to build the machine learning models for this study. The material considered in this work is 7050-T7451 aluminum, which is commonly preferred as a structural element in the aerospace industry, and regarding the crack type; corner crack is used. A finite element model is built for the joint to calculate fastener loads and stresses on the structure. Since finite element model results are validated with analytical calculations, findings of the finite element model are fed to AFGROW software to calculate analytical crack growth lives. Based on Fighter Aircraft Loading Standard for Fatigue (FALSTAFF), 90 unique fatigue loading spectra are developed for various load levels, and then, these spectrums are utilized as inputs to the artificial neural network and random forest regression models for predicting crack growth life. Finally, the crack growth life predictions of the machine learning models are compared with analytical calculations. According to the findings, a good correlation is observed between analytical and predicted crack growth lives.

Keywords: aircraft, fatigue, joint, life, optimization, prediction.

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Authors: Monapat Sasingha, Suttisak Soralump

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This research presents the behavior of slope of the road along the canal stabilized by short piles. In this investigation, the centrifuge machine was used, modelling the condition of the water levels in the canal. The centrifuge tests were performed at 35 g. To observe the movement of the soil, visual analysis was performed to evaluate the failure behavior. Conclusively, the use of short piles to stabilize the canal slope proved to be an effective solution. However, the certain amount of settlement was found behind the short pile rows.

Keywords: centrifuge test, slope failure, embankment, stability of slope

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Authors: Paulina Zavistanaviciute, Vita Krungleviciute, Elena Bartkiene

Abstract:

Lactic acid bacteria (LAB) are microbial supplements that increase the nutritional, therapeutic, and safety value of food and feed. Often LAB strains are incubated in an expensive commercially available de Man-Rogosa-Sharpe (MRS) medium; the cultures are centrifuged, and the cells are washing with sterile water. Potato juice and apple juice industry bio-products are industrial wastes which may constitute a source of digestible nutrients for microorganisms. Due to their low cost and good chemical composition, potato juice and apple juice production bio- products could have a potential application in LAB encapsulation. In this study, pure LAB (P. acidilactici and P. pentosaceus) were multiplied in a crushed potato juice and apple juice industry bio-products medium. Before using, bio-products were sterilized and filtered. No additives were added to mass, except apple juice industry bioproducts were diluted with sterile water (1/5; v/v). The tap of sterilised mass, and LAB cell suspension (5 mL), containing of 8.9 log10 colony-forming units (cfu) per mL of the P. acidilactici and P. pentosaceus was used to multiply the LAB for 72 h. The final colony number in the potato juice and apple juice bio- products substrate was on average 9.60 log10 cfu/g. In order to stabilize the LAB, several methods of dehydration have been tested: lyophilisation (MilrockKieffer Lane, Kingston, USA) and dehydration in spray drying system (SD-06, Keison, Great Britain). Into the spray drying system multiplied LAB in a crushed potato juice and apple juice bio-products medium was injected in peristaltic way (inlet temperature +60 °C, inlet air temperature +150° C, outgoing air temperature +80 °C, air flow 200 m3/h). After lyophilisation (-48 °C) and spray drying (+150 °C) the viable cell concentration in the fermented potato juice powder was 9.18 ± 0.09 log10 cfu/g and 9.04 ± 0.07 log10 cfu/g, respectively, and in apple mass powder 8.03 ± 0.04 log10 cfu/g and 7.03 ± 0.03 log10 cfu/g, respectively. Results indicated that during the storage (after 12 months) at room temperature (22 +/- 2 ºC) LAB count in dehydrated products was 5.18 log10 cfu/g and 7.00 log10 cfu/g (in spray dried and lyophilized potato juice powder, respectively), and 3.05 log10 cfu/g and 4.10 log10 cfu/g (in spray dried and lyophilized apple juice industry bio-products powder, respectively). According to obtained results, potato juice could be used as alternative substrate for P. acidilactici and P. pentosaceus cultivation, and by drying received powders can be used in food/feed industry as the LAB starters. Therefore, apple juice industry by- products before spray drying and lyophilisation should be modified (i. e. by using different starches) in order to improve its encapsulation.

Keywords: bio-products, encapsulation, lactic acid bacteria, sustainability

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Authors: Abdulatif Abdulsalam, Mohamed Shaban

Abstract:

This paper introduces interfacing and replication of electronic tools based on the MATLAB/ SIMULINK mock-up package. Mock-up components contain dc-dc converters, power issue rectifiers, motivation machines, dc gear, synchronous gear, and more entire systems. Power issue rectifier model includes solid state device models. The tools are the clear-cut structure and mock-up of complex energetic systems connecting with power electronic machines.

Keywords: power electronics, machine, MATLAB, simulink

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Authors: Hoda Abugharsa

Abstract:

In this paper we propose to design a sorter circuit using insertion sorting algorithm. The circuit will be designed using Algorithmic State Machines (ASM) method. That means converting the insertion sorting flowchart into an ASM chart. Then the ASM chart will be used to design the sorter circuit and the control unit.

Keywords: insert sorting algorithm, ASM chart, sorter circuit, state machine, control unit

Procedia PDF Downloads 445

Authors: Essam Al Daoud

Abstract:

Gradient boosting methods have been proven to be a very important strategy. Many successful machine learning solutions were developed using the XGBoost and its derivatives. The aim of this study is to investigate and compare the efficiency of three gradient methods. Home credit dataset is used in this work which contains 219 features and 356251 records. However, new features are generated and several techniques are used to rank and select the best features. The implementation indicates that the LightGBM is faster and more accurate than CatBoost and XGBoost using variant number of features and records.

Keywords: gradient boosting, XGBoost, LightGBM, CatBoost, home credit

Procedia PDF Downloads 171

Authors: Sanjay Dhiraaj, Puneet Goyal, Aditya Kapoor, Gaurav Misra

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In electrocardiography, an ECG artefact is used to indicate something that is not heart-made. Although technological advancements have produced monitors with the potential of providing accurate information and reliable heart rate alarms, despite this, interference of the displayed electrocardiogram still occurs. These interferences can be from the various electrical gadgets present in the operating room or electrical signals from other parts of the body. Artefacts may also occur due to poor electrode contact with the body or due to machine malfunction. Knowing these artefacts is of utmost importance so as to avoid unnecessary and unwarranted diagnostic as well as interventional procedures. We report a case of ECG artefacts occurring due to patient warming blanket and its consequences. A 20-year-old male with a preoperative diagnosis of exstrophy epispadias complex was posted for surgery under epidural and general anaesthesia. Just after endotracheal intubation, we observed nonspecific ECG changes on the monitor. At a first glance, the monitor strip revealed broad QRs complexes suggesting a ventricular bigeminal rhythm. Closer analysis revealed these to be artefacts because although the complexes were looking broad on the first glance there was clear presence of normal sinus complexes which were immediately followed by 'broad complexes' or artefacts produced by some device or connection. These broad complexes were labeled as artefacts as they were originating in the absolute refractory period of the previous normal sinus beat. It would be physiologically impossible for the myocardium to depolarize so rapidly as to produce a second QRS complex. A search for the possible reason for the artefacts was made and after deepening the plane of anaesthesia, ruling out any possible electrolyte abnormalities, checking of ECG leads and its connections, changing monitors, checking all other monitoring connections, checking for proper grounding of anaesthesia machine and OT table, we found that after switching off the patient’s warming apparatus the rhythm returned to a normal sinus one and the 'broad complexes' or artefacts disappeared. As misdiagnosis of ECG artefacts may subject patients to unnecessary diagnostic and therapeutic interventions so a thorough knowledge of the patient and monitors allow for a quick interpretation and resolution of the problem.

Keywords: ECG artefacts, patient warming blanket, peri-operative arrhythmias, mobile messaging services

Procedia PDF Downloads 272