Search results for: real time digital simulator

Authors: Akshay B. Pawar, Rohit Y. Parasnis

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Heart Rate Variability (HRV) is a widely used indicator of the regulation between the autonomic nervous system (ANS) and the cardiovascular system. Besides being non-invasive, it also has the potential to predict mortality in cases involving critical injuries. The gold standard method for determining HRV is based on the analysis of RR interval time series extracted from ECG signals. However, because it is much more convenient to obtain photoplethysmogramic (PPG) signals as compared to ECG signals (which require the attachment of several electrodes to the body), many researchers have used pulse cycle intervals instead of RR intervals to estimate HRV. They have also compared this method with the gold standard technique. Though most of their observations indicate a strong correlation between the two methods, recent studies show that in healthy subjects, except for a few parameters, the pulse-based method cannot be a surrogate for the standard RR interval- based method. Moreover, the former tends to overestimate short-term variability in heart rate. This calls for improvements in or alternatives to the pulse-cycle interval method. In this study, besides the systolic peak-peak interval method (PP method) that has been studied several times, four recent PPG-based techniques, namely the first derivative peak-peak interval method (P1D method), the second derivative peak-peak interval method (P2D method), the valley-valley interval method (VV method) and the tangent-intersection interval method (TI method) were compared with the gold standard technique. ECG and PPG signals were obtained from 10 young and healthy adults (consisting of both males and females) seated in the armchair position. In order to de-noise these signals and eliminate baseline drift, they were passed through certain digital filters. After filtering, the following HRV parameters were computed from PPG using each of the five methods and also from ECG using the gold standard method: time domain parameters (SDNN, pNN50 and RMSSD), frequency domain parameters (Very low-frequency power (VLF), Low-frequency power (LF), High-frequency power (HF) and Total power or “TP”). Besides, Poincaré plots were also plotted and their SD1/SD2 ratios determined. The resulting sets of parameters were compared with those yielded by the standard method using measures of statistical correlation (correlation coefficient) as well as statistical agreement (Bland-Altman plots). From the viewpoint of correlation, our results show that the best PPG-based methods for the determination of most parameters and Poincaré plots are the P2D method (shows more than 93% correlation with the standard method) and the PP method (mean correlation: 88%) whereas the TI, VV and P1D methods perform poorly (<70% correlation in most cases). However, our evaluation of statistical agreement using Bland-Altman plots shows that none of the five techniques agrees satisfactorily well with the gold standard method as far as time-domain parameters are concerned. In conclusion, excellent statistical correlation implies that certain PPG-based methods provide a good amount of information on the pattern of heart rate variation, whereas poor statistical agreement implies that PPG cannot completely replace ECG in the determination of HRV.

Keywords: photoplethysmography, heart rate variability, correlation coefficient, Bland-Altman plot

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Authors: Haifa Ben Saber, Mourad Elloumi

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In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of constant rows with a group of columns. This kind of clustering is called biclustering. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and needed. We introduce a new algorithm called, Enumerative tree (EnumTree) for biclustering of binary microarray data. is an algorithm adopting the approach of enumerating biclusters. This algorithm extracts all biclusters consistent good quality. The main idea of ​​EnumLat is the construction of a new tree structure to represent adequately different biclusters discovered during the process of enumeration. This algorithm adopts the strategy of all biclusters at a time. The performance of the proposed algorithm is assessed using both synthetic and real DNA micryarray data, our algorithm outperforms other biclustering algorithms for binary microarray data. Biclusters with different numbers of rows. Moreover, we test the biological significance using a gene annotation web tool to show that our proposed method is able to produce biologically relevent biclusters.

Keywords: DNA microarray, biclustering, gene expression data, tree, datamining.

Procedia PDF Downloads 369

Authors: Kersch-Counet C., Fransen K. H. S., Broyd M., Nyakayiru J. D. O. A., Schoemaker M. H., Mallee L. F., Bovee-Oudenhoven I. M. J.

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Background: Around one third of people, including athletes, suffer from feelings of gut discomfort. Fermentation of dairy is a process that has been associated with products that can improve gut health. However, insight in (potential) health benefits of most fermented foods is limited to chemical analyses and in-vitro models. Objective: The aim of this open-label, single-arm explorative trial was to investigate in a real life setting the effect of consumption of a fermented whey product for 3 weeks on self-perceived physical and mental wellbeing and digestive issues in 150 US recreational athletes (20-50 years of age) with self-reported gut complaints at enrolment. Methods: Participants living at the West-Coast of the US received for 3 weeks a daily powder of 15 g of BiotisTM Fermentis to be mixed in water using a supplied shaker. Weekly questionnaires were conducted by MMR research to study the effect on physical/mental health issues and self-perceived gut complaints. Non-parametric tests (e.g., Friedman test) were used to assess statistical differences over time while the Kruskal-Wallis and Wilcoxon signed-rank tests were used for sub-groups analysis. Results: Bloating, stress and anxiety were the top 3 issues of the US recreational athletes. Satisfaction of physical wellbeing increased significantly throughout the 3-weeks of fermented whey product consumption (p<0.0005). Combined digestive issues decreased significantly after 2- and 3-weeks of product consumption, with bloating showing a significant reduction (p<0.05). There was a trend that self-reported stress levels reduced after 3 weeks and participants said to significantly feel more active, energetic, and vital (p<0.05). Subgroup analysis showed that gender and habitual protein supplement consumption were associated with specific health issues and modulated the response to the fermented dairy product. Conclusion: Daily consumption of the fermented BiotisTM Fermentis product is associated with a reduction in self-perceived gastrointestinal symptoms and improved overall wellbeing and mood state in US recreational athletes. This large nutrition and health consumer study brings valuable insights in self-reported gut complaints of recreational athletes in the US and their response to a fermented dairy product. A controlled clinical trial in a targeted population is recommended to scientifically substantiate the product effect as observed in this explorative study.

Keywords: real-life study, digestive health, fermented whey, sports

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Authors: Rehema Kabare

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As urban development initiatives continue to emerge and are implemented amid rapid urbanization and climate change effects in the global south, the plight of women is only being noticed. The pandemic exposed the atrocities, violence and unsafety women and girls face daily both in their homes and in public urban spaces. This is a result of poorly implemented and managed urban structures, which women have been left out of during design and implementation for centuries. The UN Habitat’s HerCity toolkit provides a unique opportunity to change course for both governments and civil society actors where women and girls are onboarded onto urban development initiatives, with their designs and ideas being the focal point. This toolkit proves that when women and girls design, they design for everyone. The HerCity HerStreets, Public Space Improvement Initiative, resulted in a design that focused on two aspects: Streets are a shared resource, and Streets are public spaces. These two concepts illustrate that for streets to be experienced effectively as cultural spaces, they need to be user-friendly, safe and inclusive. This report demonstrates how the HerCity HerStreets as a pilot project can be a benchmark for designing urban spaces in African cities. The project focused on five dimensions to improve the air quality of the space, the space allocation to street vending and bodaboda (passenger motorcycle) stops parking and the green coverage. The process displays how digital tools such as Minecraft and Kobo Toolbox can be utilized to improve citizens’ participation in the development of public spaces, with a special focus on including vulnerable groups such as women, girls and youth.

Keywords: urban space, sustainable development, gender and the city, digital tools and urban development

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Authors: Carla Idely Palencia-Aguilar

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Artificial wetlands play an important role at Guasca Municipality in Colombia, not only because they are used for the agroindustry, but also because more than 45 species were found, some of which are endemic and migratory birds. Remote sensing was used to determine the changes in the area occupied by water of artificial wetlands by means of Aster and Modis images for different time periods. Evapotranspiration was also determined by three methods: Surface Energy Balance System-Su (SEBS) algorithm, Surface Energy Balance- Bastiaanssen (SEBAL) algorithm, and Potential Evapotranspiration- FAO. Empirical equations were also developed to determine the relationship between Normalized Difference Vegetation Index (NDVI) versus net radiation, ambient temperature and rain with an obtained R2 of 0.83. Groundwater level fluctuations on a daily basis were studied as well. Data from a piezometer placed next to the wetland were fitted with rain changes (with two weather stations located at the proximities of the wetlands) by means of multiple regression and time series analysis, the R2 from the calculated and measured values resulted was higher than 0.98. Information from nearby weather stations provided information for ordinary kriging as well as the results for the Digital Elevation Model (DEM) developed by using PCI software. Standard models (exponential, spherical, circular, gaussian, linear) to describe spatial variation were tested. Ordinary Cokriging between height and rain variables were also tested, to determine if the accuracy of the interpolation would increase. The results showed no significant differences giving the fact that the mean result of the spherical function for the rain samples after ordinary kriging was 58.06 and a standard deviation of 18.06. The cokriging using for the variable rain, a spherical function; for height variable, the power function and for the cross variable (rain and height), the spherical function had a mean of 57.58 and a standard deviation of 18.36. Threatens of eutrophication were also studied, given the unconsciousness of neighbours and government deficiency. Water quality was determined over the years; different parameters were studied to determine the chemical characteristics of water. In addition, 600 pesticides were studied by gas and liquid chromatography. Results showed that coliforms, nitrogen, phosphorous and prochloraz were the most significant contaminants.

Keywords: DEM, evapotranspiration, geostatistics, NDVI

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Authors: V. Ramu Reddy, Parijat Deshpande, Ranjan Dasgupta

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This paper presents different methodology for an acoustic source sensing and localization in an unknown environment. The developed methodology includes an acoustic based sensing and localization system, a converging target localization based on the recursive direction of arrival (DOA) error minimization, and a regressive obstacle avoidance function. Our method is able to augment the existing proven localization techniques and improve results incrementally by utilizing robot locomotion and is capable of converging to a position estimate with greater accuracy using fewer measurements. The results also evinced the DOA error minimization at each iteration, improvement in time for reaching the destination and the efficiency of this target localization method as gradually converging to the real target position. Initially, the system is tested using Kinect mounted on turntable with DOA markings which serve as a ground truth and then our approach is validated using a FireBird VI (FBVI) mobile robot on which Kinect is used to obtain bearing information.

Keywords: acoustic source localization, acoustic sensing, recursive direction of arrival, robot locomotion

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Authors: Alpesh Adeshara, Rajendrasinh Jadeja, Praghnesh Bhatt

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Implementation of advanced technologies requires sophisticated instruments that deal with the operation, control, restoration and protection of rapidly growing power system network under normal and abnormal conditions. Presently, the applications of Phasor Measurement Unit (PMU) are widely found in real time operation, monitoring, controlling and analysis of power system network as it eliminates the various limitations of Supervisory Control and Data Acquisition System (SCADA) conventionally used in power system. The use of PMU data is very rapidly increasing its importance for online and offline analysis. Wide Area Measurement System (WAMS) is developed as new technology by use of multiple PMUs in power system. The present paper proposes a model of MATLAB based PMU using Discrete Fourier Transform (DFT) algorithm and evaluation of its operation under different contingencies. In this paper, PMU based two bus system having WAMS network is presented as a case study.

Keywords: GPS global positioning system, PMU phasor measurement system, WAMS wide area monitoring system, DFT, PDC

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Authors: Chandra Mukherjee

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The present paper is a representation of the work done in the field of ECG signal analysis using MATLAB 7.1 Platform. An accurate and simple ECG feature extraction algorithm is presented in this paper and developed algorithm is validated using BIOPAC software. To detect the QRS peak, ECG signal is processed by following mentioned stages- First Derivative, Second Derivative and then squaring of that second derivative. Efficiency of developed algorithm is tested on ECG samples from different database and real time ECG signals acquired using BIOPAC system. Firstly we have lead wise specified threshold value the samples above that value is marked and in the original signal, where these marked samples face change of slope are spotted as R-peak. On the left and right side of the R-peak, faces change of slope identified as Q and S peak, respectively. Now the inbuilt Detection algorithm of BIOPAC software is performed on same output sample and both outputs are compared. ECG baseline modulation correction is done after detecting characteristics points. The efficiency of the algorithm is tested using some validation parameters like Sensitivity, Positive Predictivity and we got satisfied value of these parameters.

Keywords: first derivative, variable threshold, slope reversal, baseline modulation correction

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Authors: Ying Su, Morgan C. Wang

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Long-term time series forecasting is an important research area for automated machine learning (AutoML). Currently, forecasting based on either machine learning or statistical learning is usually built by experts, and it requires significant manual effort, from model construction, feature engineering, and hyper-parameter tuning to the construction of the time series model. Automation is not possible since there are too many human interventions. To overcome these limitations, this article proposed to use recurrent neural networks (RNN) through the memory state of RNN to perform long-term time series prediction. We have shown that this proposed approach is better than the traditional Autoregressive Integrated Moving Average (ARIMA). In addition, we also found it is better than other network systems, including Fully Connected Neural Networks (FNN), Convolutional Neural Networks (CNN), and Nonpooling Convolutional Neural Networks (NPCNN).

Keywords: automated machines learning, autoregressive integrated moving average, neural networks, time series analysis

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Authors: Hussaini Doko Ibrahim, Hamilton Cyprian Chinwenyi, Henrietta Nkem Ude

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In this paper, efforts were made to examine and compare the algorithmic iterative solutions of the conjugate gradient method as against other methods such as Gauss-Seidel and Jacobi approaches for solving systems of linear equations of the form Ax=b, where A is a real n×n symmetric and positive definite matrix. We performed algorithmic iterative steps and obtained analytical solutions of a typical 3×3 symmetric and positive definite matrix using the three methods described in this paper (Gauss-Seidel, Jacobi, and conjugate gradient methods), respectively. From the results obtained, we discovered that the conjugate gradient method converges faster to exact solutions in fewer iterative steps than the two other methods, which took many iterations, much time, and kept tending to the exact solutions.

Keywords: conjugate gradient, linear equations, symmetric and positive definite matrix, gauss-seidel, Jacobi, algorithm

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Authors: Shi-hong Lu, Chao-xun Liu, Yi-feng Zhu

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Ultrasonic shot peening (USP) on AALY12 sheet was studied. Several parameters (arc heights, surface roughness, surface topography and microhardness) with different USP process parameters were measured. The research proposes that the radius of curvature of shot peened sheet increases with time and electric current decreasing, while it increases with pin diameter increasing, and radius of curvature reaches a saturation level after a specific processing time and electric current. An empirical model of the relationship between radius of curvature and pin diameter, electric current, time was also obtained. The research shows that the increment of surface and vertical microhardness of material is more obvious with longer time and higher value of electric current, which can be up to 20% and 28% respectively.

Keywords: USP forming, surface properties, radius of curvature, residual stress

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Authors: Zsolt János Viharos, Krisztián Balázs Kis, Imre Paniti, Gábor Belső, Péter Németh, János Farkas

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The paper presents the results and industrial applications in the production setup period estimation based on industrial data inherited from the field of polymer cutting. The literature of polymer cutting is very limited considering the number of publications. The first polymer cutting machine is known since the second half of the 20th century; however, the production of polymer parts with this kind of technology is still a challenging research topic. The products of the applying industrial partner must met high technical requirements, as they are used in medical, measurement instrumentation and painting industry branches. Typically, 20% of these parts are new work, which means every five years almost the entire product portfolio is replaced in their low series manufacturing environment. Consequently, it requires a flexible production system, where the estimation of the frequent setup periods' lengths is one of the key success factors. In the investigation, several (input) parameters have been studied and grouped to create an adequate training information set for an artificial neural network as a base for the estimation of the individual setup periods. In the first group, product information is collected such as the product name and number of items. The second group contains material data like material type and colour. In the third group, surface quality and tolerance information are collected including the finest surface and tightest (or narrowest) tolerance. The fourth group contains the setup data like machine type and work shift. One source of these parameters is the Manufacturing Execution System (MES) but some data were also collected from Computer Aided Design (CAD) drawings. The number of the applied tools is one of the key factors on which the industrial partners’ estimations were based previously. The artificial neural network model was trained on several thousands of real industrial data. The mean estimation accuracy of the setup periods' lengths was improved by 30%, and in the same time the deviation of the prognosis was also improved by 50%. Furthermore, an investigation on the mentioned parameter groups considering the manufacturing order was also researched. The paper also highlights the manufacturing introduction experiences and further improvements of the proposed methods, both on the shop floor and on the quotation preparation fields. Every week more than 100 real industrial setup events are given and the related data are collected.

Keywords: artificial neural network, low series manufacturing, polymer cutting, setup period estimation

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Authors: Devatha Kalyan Kumar, R. Poovarasan

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In this paper, we have taken certain important factors and health parameters of diabetes patients especially among children by birth (pediatric congenital) where using the above three metrics methods we are going to assess the importance of each attributes in the dataset and thereby determining the most highly responsible and co-related attribute causing diabetics among young patients. We use cost optimization, control chart and Spearmen methodologies for the real-time application of finding the data efficiency in this diabetes dataset. The Spearmen methodology is the correlation methodologies used in software development process to identify the complexity between the various modules of the software. Identifying the complexity is important because if the complexity is higher, then there is a higher chance of occurrence of the risk in the software. With the use of control; chart mean, variance and standard deviation of data are calculated. With the use of Cost optimization model, we find to optimize the variables. Hence we choose the Spearmen, control chart and cost optimization methods to assess the data efficiency in diabetes datasets.

Keywords: correlation, congenital diabetics, linear relationship, monotonic function, ranking samples, pediatric

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Authors: Yuko Ishiwaka, Tomohiro Yoshida, Tadateru Itoh

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Human makes preparation called readiness potential unconsciously (RP) before awareness of their own decision. For example, when recognizing a button and pressing the button, the RP peaks are observed 200 ms before the initiation of the movement. It has been known that the preparatory movements are acquired before actual movements, but it has not been still well understood how humans can obtain the RP during their growth. On the proposition of why the brain must respond earlier, we assume that humans have to adopt the dangerous environment to survive and then obtain the behavior to cover the various time lags distributed in the body. Without RP, humans cannot take action quickly to avoid dangerous situations. In taking action, the brain makes decisions, and signals are transmitted through the Spinal Cord to the muscles to the body moves according to the laws of physics. Our research focuses on the time lag of the neuron signal transmitting from a brain to muscle via a spinal cord. This time lag is one of the essential factors for readiness potential. We propose a firing pattern controller model of a motor nerve system considered innervation and jitter, which produces time lag. In our simulation, we adopt innervation and jitter in our proposed muscle-skeleton model, because these two factors can create infinitesimal time lag. Q10 Hodgkin Huxley model to calculate action potentials is also adopted because the refractory period produces a more significant time lag for continuous firing. Keeping constant power of muscle requires cooperation firing of motor neurons because a refractory period stifles the continuous firing of a neuron. One more factor in producing time lag is slow or fast-twitch. The Expanded Hill Type model is adopted to calculate power and time lag. We will simulate our model of muscle skeleton model by controlling the firing pattern and discuss the relationship between the time lag of physics and neurons. For our discussion, we analyze the time lag with our simulation for knee bending. The law of inertia caused the most influential time lag. The next most crucial time lag was the time to generate the action potential induced by innervation and jitter. In our simulation, the time lag at the beginning of the knee movement is 202ms to 203.5ms. It means that readiness potential should be prepared more than 200ms before decision making.

Keywords: firing patterns, innervation, jitter, motor nerve system, readiness potential

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Authors: Salah A. Al-Garyani, Yousef Swesi

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Gas hydrates can be hazardous to upstream operations. On the other hand, the high gas storage capacity of hydrate may be utilized for natural gas storage and transport. Research on the promotion of hydrate formation, as related to natural gas storage and transport, has received relatively little attention. The primary objective of this study is to gain a better understanding of the effects of ionic surfactants, particularly their molecular structures and concentration, on the formation of tetrahydrofuran (THF) hydrate, which is often used as a model hydrate former for screening hydrate promoters or inhibitors. The surfactants studied were sodium n-dodecyl sulfate (SDS), sodium n-hexadecyl sulfate (SHS). Our results show that, at concentrations below the solubility limit, the induction time decreases with increasing surfactant concentration. At concentrations near or above the solubility, however, the surfactant concentration no longer has any effect on the induction time. These observations suggest that the effect of surfactant on THF hydrate formation is associated with surfactant monomers, not the formation of micelle as previously reported. The lowest induction time (141.25 ± 21 s, n = 4) was observed in a solution containing 7.5 mM SDS. The induction time decreases by a factor of three at concentrations near or above the solubility, compared to that without surfactant.

Keywords: tetrahydrofuran, hydrate, surfactant, induction time, monomers, micelle

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Authors: Waheeda Jabbar, Ali Gul, Rida Noor, Sania Kurd, Saba Gulzar

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Hundreds of miners die from mining accidents each year due to poisonous gases found underground mining areas. This paper proposed an idea to protect the precious lives of mining workers. A supervising system is designed which is based on ZigBee wireless technique along with the smart protective helmets to detect real-time surveillance and it gives early warnings on presence of different poisonous gases in order to save mineworkers from any danger caused by these poisonous gases. A wireless sensor network is established using ZigBee wireless technique by integrating sensors on the helmet, apart from this helmet have embedded heartbeat sensor to detect the pulse rate and be aware of the physical or mental strength of a mineworker to increase the potential safety. Radio frequency identification (RFID) technology is used to find the location of workers. A ZigBee based base station is set-upped to control the communication. The idea is implemented and results are verified through experiment.

Keywords: Arduino, gas sensor (MQ7), RFID, wireless ZigBee

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Authors: Iqra Yaseen

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One of the well-known areas of computer science and engineering, image processing in the context of computer vision has been essential to automation. In remote sensing, medical science, and many other fields, it has made it easier to uncover previously undiscovered facts. Grading of diverse items is now possible because of neural network algorithms, categorization, and digital image processing. Its use in the classification of agricultural products, particularly in the grading of seeds or grains and their cultivars, is widely recognized. A grading and sorting system enables the preservation of time, consistency, and uniformity. Global population growth has led to an increase in demand for food staples, biofuel, and other agricultural products. To meet this demand, available resources must be used and managed more effectively. Image processing is rapidly growing in the field of agriculture. Many applications have been developed using this approach for crop identification and classification, land and disease detection and for measuring other parameters of crop. Vegetation localization is the base of performing these task. Vegetation helps to identify the area where the crop is present. The productivity of the agriculture industry can be increased via image processing that is based upon Unmanned Aerial Vehicle photography and satellite. In this paper we use the machine learning techniques like Convolutional Neural Network, deep learning, image processing, classification, You Only Live Once to UAV imaging dataset to divide the crop into distinct groups and choose the best way to use it.

Keywords: image processing, UAV, YOLO, CNN, deep learning, classification

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Authors: Seyed Sadegh Naseralavi, Mohsen Khatibinia

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In structural optimizations through meta-heuristic algorithms, analyses of structures are performed for many times. For this reason, performing the analyses in a time saving way is precious. The importance of the point is more accentuated in time-history analyses which take much time. To this aim, peak picking methods also known as spectrum analyses are generally utilized. However, such methods do not have the required accuracy either done by square root of sum of squares (SRSS) or complete quadratic combination (CQC) rules. The paper presents an efficient technique for evaluating the dynamic responses during the optimization process with high speed and accuracy. In the method, first by using a static equivalent of the earthquake, an initial design is obtained. Then, the displacements in the modal coordinates are achieved. The displacements are herein called basic modal displacements (MBD). For each new design of the structure, the responses can be derived by well scaling each of the MBD along the time and amplitude and superposing them together using the corresponding modal matrices. To illustrate the efficiency of the method, an optimization problems is studied. The results show that the proposed approach is a suitable replacement for the conventional time history and spectrum analyses in such problems.

Keywords: basic modal displacements, earthquake, optimization, spectrum

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Authors: Abdul Khalique Baloch, Ali Okatan

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The quality analysis of food and vegetable from image is hot topic now a day, where researchers make them better then pervious findings through different technique and methods. In this research we have review the literature, and find gape from them, and suggest better proposed approach, design the algorithm, developed a software to measure the quality from images, where accuracy of image show better results, and compare the results with Perouse work done so for. The Application we uses an open-source dataset and python language with tensor flow lite framework. In this research we focus to sort food and vegetable from image, in the images, the application can sorts and make them grading after process the images, it could create less errors them human base sorting errors by manual grading. Digital pictures datasets were created. The collected images arranged by classes. The classification accuracy of the system was about 94%. As fruits and vegetables play main role in day-to-day life, the quality of fruits and vegetables is necessary in evaluating agricultural produce, the customer always buy good quality fruits and vegetables. This document is about quality detection of fruit and vegetables using images. Most of customers suffering due to unhealthy foods and vegetables by suppliers, so there is no proper quality measurement level followed by hotel managements. it have developed software to measure the quality of the fruits and vegetables by using images, it will tell you how is your fruits and vegetables are fresh or rotten. Some algorithms reviewed in this thesis including digital images, ResNet, VGG16, CNN and Transfer Learning grading feature extraction. This application used an open source dataset of images and language used python, and designs a framework of system.

Keywords: deep learning, computer vision, image processing, rotten fruit detection, fruits quality criteria, vegetables quality criteria

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Authors: Marc Sango, Olimpia Hoinaru, Christophe Gransart, Laurence Duchien

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Ontologies offer a means for representing and sharing information in many domains, particularly in complex domains. For example, it can be used for representing and sharing information of System Requirement Specification (SRS) of complex systems like the SRS of ERTMS/ETCS written in natural language. Since this system is a real-time and critical system, generic ontologies, such as OWL and generic ERTMS ontologies provide minimal support for modeling temporal information omnipresent in these SRS documents. To support the modeling of temporal information, one of the challenges is to enable representation of dynamic features evolving in time within a generic ontology with a minimal redesign of it. The separation of temporal information from other information can help to predict system runtime operation and to properly design and implement them. In addition, it is helpful to provide a reasoning and querying techniques to reason and query temporal information represented in the ontology in order to detect potential temporal inconsistencies. Indeed, a user operation, such as adding a new constraint on existing planning constraints can cause temporal inconsistencies, which can lead to system failures. To address this challenge, we propose a lightweight 3-layer temporal Quality of Service (QoS) ontology for representing, reasoning and querying over temporal and non-temporal information in a complex domain ontology. Representing QoS entities in separated layers can clarify the distinction between the non QoS entities and the QoS entities in an ontology. The upper generic layer of the proposed ontology provides an intuitive knowledge of domain components, specially ERTMS/ETCS components. The separation of the intermediate QoS layer from the lower QoS layer allows us to focus on specific QoS Characteristics, such as temporal or integrity characteristics. In this paper, we focus on temporal information that can be used to predict system runtime operation. To evaluate our approach, an example of the proposed domain ontology for handover operation, as well as a reasoning rule over temporal relations in this domain-specific ontology, are given.

Keywords: system requirement specification, ERTMS/ETCS, temporal ontologies, domain ontologies

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Authors: Wullapa Wongsinlatam

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Back propagation algorithm (BP) is a widely used technique in artificial neural network and has been used as a tool for solving the time series problems, such as decreasing training time, maximizing the ability to fall into local minima, and optimizing sensitivity of the initial weights and bias. This paper proposes an improvement of a BP technique which is called IM-COH algorithm (IM-COH). By combining IM-COH algorithm with cuckoo search algorithm (CS), the result is cuckoo search improved control output hidden layer algorithm (CS-IM-COH). This new algorithm has a better ability in optimizing sensitivity of the initial weights and bias than the original BP algorithm. In this research, the algorithm of CS-IM-COH is compared with the original BP, the IM-COH, and the original BP with CS (CS-BP). Furthermore, the selected benchmarks, four time series samples, are shown in this research for illustration. The research shows that the CS-IM-COH algorithm give the best forecasting results compared with the selected samples.

Keywords: artificial neural networks, back propagation algorithm, time series, local minima problem, metaheuristic optimization

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Authors: Eylem Koç, Hasan Arda Burhan

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Location selection presents a crucial decision problem in today’s business world where strategic decision making processes have critical importance. Thus, location selection has strategic importance for companies in boosting their strength regarding competition, increasing corporate performances and efficiency in addition to lowering production and transportation costs. A right choice in location selection has a direct impact on companies’ commercial success. In this study, a store location selection problem of Carglass Turkey which operates in vehicle glass branch is handled. As this problem includes both tangible and intangible criteria, Analytic Network Process (ANP) was accepted as the main methodology. The model consists of control hierarchy and BOCR subnetworks which include clusters of actors, alternatives and criteria. In accordance with the management’s choices, five different locations were selected. In addition to the literature review, a strict cooperation with the actor group was ensured and maintained while determining the criteria and during whole process. Obtained results were presented to the management as a report and its feasibility was confirmed accordingly.

Keywords: analytic network process (ANP), BOCR, multi-actor decision making, multi-criteria decision making, real-life problem, location selection

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Authors: Ifeoma Maryjane Iloamaeke, Kelvin Obazie, Mmesoma Offornze, Chiamaka Marysilvia Ifeaghalu, Cecilia Aduaka, Ugomma Chibuzo Onyeije, Claudine Ifunanaya Ogu, Ngozi Anastesia Okonkwo

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This work investigated the effects of pH, settling time, and coagulant dosages on the removal of color, turbidity, and heavy metals from paint industrial wastewater using the seed of Artocarpus heterophyllus (AH) by the coagulation-flocculation process. The paint effluent was physicochemically characterized, while AH coagulant was instrumentally characterized by Scanning Electron Microscope (SEM), Fourier Transform Infrared (FTIR), and X-ray diffraction (XRD). A Jar test experiment was used for the coagulation-flocculation process. The result showed that paint effluent was polluted with color, turbidity (36000 NTU), mercury (1.392 mg/L), lead (0.252 mg/L), arsenic (1.236 mg/L), TSS (63.40mg/L), and COD (121.70 mg/L). The maximum color removal efficiency was 94.33% at the dosage of 0.2 g/L, pH 2 at a constant time of 50 mins, and 74.67% at constant pH 2, coagulant dosage of 0.2 g/L and 50 mins. The highest turbidity removal efficiency was 99.94% at 0.2 g/L and 50 mins at constant pH 2 and 96.66% at pH 2 and 0.2 g/L at constant time of 50 mins. The mercury removal efficiency of 99.29% was achieved at the optimal condition of 0.8 g/L coagulant dosage, pH 8, and constant time of 50 mins and 99.57% at coagulant dosage of 0.8 g/L, time of 50 mins constant pH 8. The highest lead removal efficiency was 99.76% at a coagulant dosage of 10 g/L, time of 40 mins at constant pH 10, and 96.53% at pH 10, coagulant dosage of 10 g/L and constant time of 40 mins. For arsenic, the removal efficiency is 75.24 % at 0.8 g/L coagulant dosage, time of 40 mins, and constant pH of 8. XRD imaging before treatment showed that Artocarpus heterophyllus coagulant was crystalline and changed to amorphous after treatment. The SEM and FTIR results of the AH coagulant and sludge suggested there were changes in the surface morphology and functional groups before and after treatment. The reaction kinetics were modeled best in the second order.

Keywords: Artocarpus heterophyllus, coagulation-flocculation, coagulant dosages, setting time, paint effluent

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Authors: Phuc D. Nguyen, Kristy L. Hansen, Branko Zajamsek

Abstract:

The existence of special characteristics of wind farm noise such as amplitude modulation (AM) contributes significantly to annoyance, which could ultimately result in sleep disturbance and other adverse health effects for residents living near wind farms. In order to detect and quantify this phenomenon, several methods have been developed which can be separated into three types: time-domain, frequency-domain and hybrid methods. However, due to a lack of systematic validation of these methods, it is still difficult to select the best method for identifying AM. Furthermore, previous comparisons between AM methods have been predominantly qualitative or based on synthesised signals, which are not representative of the actual noise. In this study, a comparison between methods for detecting and quantifying AM has been carried out. The results are based on analysis of real noise data which were measured at a wind farm in South Australia. In order to evaluate the performance of these methods in terms of detecting AM, an approach has been developed to select the most successful method of AM detection. This approach uses a receiver operating characteristic (ROC) curve which is based on detection of AM in audio files by experts.

Keywords: amplitude modulation, wind farm noise, ROC curve

Procedia PDF Downloads 136

Authors: Ashlesha Khanapure, Harsh Kashyap, Abhinav Anand, Sanjana Habib, Anupama Bidargaddi

Abstract:

Technologies that are developing quickly are being developed daily in a variety of disciplines, particularly the medical field. For the purpose of detecting bone fractures in X-ray pictures of different body segments, our work compares the ResNet-50 and MobileNetV3 architectures. It evaluates accuracy and computing efficiency with X-rays of the elbow, hand, and shoulder from the MURA dataset. Through training and validation, the models are evaluated on normal and fractured images. While ResNet-50 showcases superior accuracy in fracture identification, MobileNetV3 showcases superior speed and resource optimization. Despite ResNet-50’s accuracy, MobileNetV3’s swifter inference makes it a viable choice for real-time clinical applications, emphasizing the importance of balancing computational efficiency and accuracy in medical imaging. We created a graphical user interface (GUI) for MobileNet V3 model bone fracture detection. This research underscores MobileNetV3’s potential to streamline bone fracture diagnoses, potentially revolutionizing orthopedic medical procedures and enhancing patient care.

Keywords: CNN, MobileNet V3, ResNet-50, healthcare, MURA, X-ray, fracture detection

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Authors: Ali. Marjani, M. Farsi, M. Rahimizadeh

Abstract:

Chickpea (Cicer arietinum L.) is one of the important grain legume crops in the world. However, drought stress is a serious threat to chickpea production, and development of drought-resistant varieties is a necessity. Field experiments were conducted to evaluate the response of 8 chickpea genotypes (MCC* 696, 537, 80, 283, 392, 361, 252, 397) and drought stress (S1: non-stress, S2: stress at vegetative growth stage, S3: stress at early bloom, S4: stress at early pod visible) at different growth stages. Experiment was arranged in split plot design with four replications. Difference among the drought stress time was found to be significant for investigated traits except biological yield. Differences were observed for genotypes in flowering time, pod information time, physiological maturation time and yield. Plant height reduced due to drought stress in vegetative growth stage. Stem dry weight reduced due to drought stress in pod visibly. Flowering time, maturation time, pod number, number of seed per plant and yield cause of drought stress in flowering was also reduced. The correlation between yield and number of seed per plant and biological yield was positive. The MCC283 and MCC696 were the high-tolerance genotypes. These results demonstrated that drought stress delayed phonological growth in chickpea and that flowering stage is sensitive.

Keywords: chickpea, drought stress, growth stage, tolerance

Procedia PDF Downloads 256

Authors: Han Kuikui

Abstract:

Adolescents, serving as the reserve force for technological innovation talents, possess STEM creativity that is not only pivotal to achieving STEM education goals but also provides a viable path for reforming science curricula in compulsory education and cultivating innovative talents in China. To investigate the relationship among adolescents' grit, creative self-efficacy, future time perspective, and STEM creativity, a survey was conducted in 2023 using stratified random sampling. A total of 1263 junior high school students from the main urban areas of Chongqing, from grade 7 to grade 9, were sampled. The results indicated that (1) Grit positively predicts adolescents' creative self-efficacy and STEM creativity significantly; (2) Creative self-efficacy mediates the positive relationship between grit and adolescents' STEM creativity; (3) The mediating role of creative self-efficacy is moderated by future time perspective, such that with a higher future time perspective, the positive predictive effect of grit on creative self-efficacy is more substantial, which in turn positively affects their STEM creativity.

Keywords: grit, stem creativity, creative self-efficacy, future time perspective

Procedia PDF Downloads 48

Authors: Hsiao-Wen Hsu

Abstract:

It is generally believed that the teacher’s use of motivational strategies can enhance student motivation, especially in a place like Taiwan where teacher usually dominates student EFL learning. However, only little empirical studies support this claim. This study examined the connection between teachers’ use of motivational teaching practice and observed student motivated behavior in rural junior high schools in Taiwan. The use of motivational strategies by 12 teachers in five recognized rural junior high schools was investigated observed using a classroom observation instrument, the Motivation Orientation of Language Teaching. Meanwhile, post-lesson teacher evaluations accomplished by both the researcher and the teacher were functioning as part of the measure of teacher motivational practice. The data collected through observation scheme follows the real-time coding principle to examine observable teacher motivational practice and learner motivated behaviors. The results support the previous research findings that teachers’ use of motivational strategies is associated with the student motivated behaviors as well as the students’ level of motivation regarding English learning.

Keywords: English learning, motivational strategies, student motivation, teacher motivational practices

Procedia PDF Downloads 404

Authors: M. Khaled Abduesslam, Mohammed Ali, Basher H. Alsdai, Muhammad Nizam Inayati

Abstract:

This paper presents the voltage problem location classification using performance of Least Squares Support Vector Machine (LS-SVM) and Learning Vector Quantization (LVQ) in electrical power system for proper voltage problem location implemented by IEEE 39 bus New-England. The data was collected from the time domain simulation by using Power System Analysis Toolbox (PSAT). Outputs from simulation data such as voltage, phase angle, real power and reactive power were taken as input to estimate voltage stability at particular buses based on Power Transfer Stability Index (PTSI).The simulation data was carried out on the IEEE 39 bus test system by considering load bus increased on the system. To verify of the proposed LS-SVM its performance was compared to Learning Vector Quantization (LVQ). The results showed that LS-SVM is faster and better as compared to LVQ. The results also demonstrated that the LS-SVM was estimated by 0% misclassification whereas LVQ had 7.69% misclassification.

Keywords: IEEE 39 bus, least squares support vector machine, learning vector quantization, voltage collapse

Procedia PDF Downloads 435

Authors: Fatima Zohra Rahou, A. Guen Bouazza, B. Bouazza

Abstract:

Multi-gate SOI MOSFETs has shown better results in subthreshold performances. The replacement of SiO2 by high-k dielectric can fulfill the requirements of Multi-gate MOSFETS with a scaling trend in device dimensions. The advancement in fabrication technology has also boosted the use of different high -k dielectric materials as oxide layer at different places in MOSFET structures. One of the most important multi-gate structures is square GAA SOI MOSFET that is a strong candidate for the next generation nanoscale devices; show an even stronger control of short channel effects. In this paper, GAA SOI MOSFET structure with using high -k dielectrics materials Al2O3 (k~9), HfO2 (k~20), La2O3 (k~30) and metal gate TiN are simulated by using 3-D device simulator DevEdit and Atlas of SILVACO TCAD tools. Square GAA SOI MOSFET transistor with High-k HfO2 gate dielectrics and TiN metal gate exhibits significant improvements performances compared to Al2O3 and La2O3 dielectrics for the same structure. Simulation results of GAA SOI MOSFET transistor with HfO2 dielectric show the increase in saturation current and Ion/Ioff ratio while leakage current, subthreshold slope and DIBL effect are decreased.

Keywords: technology SOI, short-channel effects (SCEs), multi-gate SOI MOSFET, square GAA SOI MOSFET, high-k dielectric, Silvaco software

Procedia PDF Downloads 255