Search results for: machine harvested grapes

Authors: Kassahun Zewdie

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-- A field experiment was conducted at Holetta research center and farmers fields during 2017 and 2018 to determine the effects of haulauxifen-methyl + florasulam (QULEX 200 WG) on broadleaf weeds in wheat. The design was a Randomized Complete Block with three replications. The treatments were included haulauxifen-Methyl + florasulam @ 25gm, 50gm and 75gm ha-1, (King-D) 2, 4-D dimethyl amine @1.0 L ha-1, 2, 4-Dichlorophenoxy acetic acid @1.0 L ha-1 rate (standard check), farmers practice twice hand weeding (25-30 and 55-60) days after sowing and weedy check. Herbicides were applied with knapsack sprayer with a spray volume of 200 L ha-1. The wheat variety “Denda” was sown at 20 cm spacing. The recommended rate of fertilizer was applied. Weed density and biomass were recorded at (25-30 and 55-60) days after sowing. The results revealed that post emergence application of haulauxifen-methyl + florasulam @50gm ha-1 had a significant (P<0.05) effect on Guizotia scabra, Polygonum nepalense, Plantago lanceolata, Galinsoga parviflora, Sonchus spp., Galium spurium, Amaranthus hybridus, Raphanus raphanistrum and Medicago polymorpha population. The magnitude ranged from two to four folds when comparing with weed densities recorded in the unweeded plot. The grain yield harvested from the untreated check plot was significantly lower than the rest treatments. The grain yield was improved by 17.3% over the standard check with better performance.

Keywords: broadleaf, grass, weeds, control

Procedia PDF Downloads 166

Authors: Lei Zhu, Nan Li

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Cold stamping has been widely applied in the automotive industry for the mass production of a great range of automotive panels. Predicting the springback to ensure the dimensional accuracy of the cold-stamped components is a critical step. The main approaches for the prediction and compensation of springback in cold stamping include running Finite Element (FE) simulations and conducting experiments, which require forming process expertise and can be time-consuming and expensive for the design of cold stamping tools. Machine learning technologies have been proven and successfully applied in learning complex system behaviours using presentative samples. These technologies exhibit the promising potential to be used as supporting design tools for metal forming technologies. This study, for the first time, presents a novel application of a Convolutional Neural Network (CNN) based surrogate model to predict the springback fields for variable U-shape cold bending geometries. A dataset is created based on the U-shape cold bending geometries and the corresponding FE simulations results. The dataset is then applied to train the CNN surrogate model. The result shows that the surrogate model can achieve near indistinguishable full-field predictions in real-time when compared with the FE simulation results. The application of CNN in efficient springback prediction can be adopted in industrial settings to aid both conceptual and final component designs for designers without having manufacturing knowledge.

Keywords: springback, cold stamping, convolutional neural networks, machine learning

Procedia PDF Downloads 131

Authors: Gurjeet Singh, M. K. Pradhan, Ajay Verma

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The plastic industry plays very important role in the economy of any country. It is generally among the leading share of the economy of the country. Since metals and their alloys are very rarely available on the earth. So to produce plastic products and components, which finds application in many industrial as well as household consumer products is beneficial. Since 50% plastic products are manufactured by injection moulding process. For production of better quality product, we have to control quality characteristics and performance of the product. The process parameters plays a significant role in production of plastic, hence the control of process parameter is essential. In this paper the effect of the parameters selection on injection moulding process has been described. It is to define suitable parameters in producing plastic product. Selecting the process parameter by trial and error is neither desirable nor acceptable, as it is often tends to increase the cost and time. Hence optimization of processing parameter of injection moulding process is essential. The experiments were designed with Taguchi’s orthogonal array to achieve the result with least number of experiments. Here Plastic material polypropylene is studied. Tensile strength test of material is done on universal testing machine, which is produced by injection moulding machine. By using Taguchi technique with the help of MiniTab-14 software the best value of injection pressure, melt temperature, packing pressure and packing time is obtained. We found that process parameter packing pressure contribute more in production of good tensile plastic product.

Keywords: injection moulding, tensile strength, poly-propylene, Taguchi

Procedia PDF Downloads 262

Authors: Muhammad Umair, Yasir Nawab, Khubab Shaker, Muhammad Maqsood, Adeel Zulfiqar, Danish Mahmood Baitab

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Fabric is a flexible woven material consisting of a network of natural or artificial fibers often referred to as thread or yarn. Today fabrics are produced by weaving, braiding, knitting, tufting and non-woven. Weaving is a method of fabric production in which warp and weft yarns are interlaced perpendicular to each other. There is infinite number of ways for the interlacing of warp and weft yarn. Each way produces a different fabric structure. The yarns parallel to the machine direction are called warp yarns and the yarns perpendicular to the machine direction are called weft or filling yarns. Air jet weaving is the modern method of weft insertion and considered as high speed loom. The twist loss in air jet during weft insertion affects the strength. The aim of this study was to investigate the effect of twist change in weft yarn during air-jet weft insertion. A total number of 8 samples were produced using 1/1 plain and 3/1 twill weave design with two fabric widths having same loom settings. Two different types of yarns like cotton and PC blend were used. The effect of material type, weave design and fabric width on twist change of weft yarn was measured and discussed. Twist change in the different types of weft yarn and weave design was measured and compared the twist change in the weft yarn with the yarn before weft yarn insertion and twist loss is measured. Wider fabric leads to higher twist loss in the yarn.

Keywords: air jet loom, twist per inch, twist loss, weft yarn

Procedia PDF Downloads 382

Authors: Bencheikh Abdellah

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In this paper, we present a mathematical model dedicated to the simulation breaks bars in a three-phase cage induction motor. This model is based on a mesh circuit representing the rotor cage. The tested simulation allowed us to demonstrate the effectiveness of this model to describe the behavior of the machine in a healthy state, failure.

Keywords: AC motors, squirrel cage, diagnostics, MATLAB, SIMULINK

Procedia PDF Downloads 423

Authors: Bilel Chalghaf, Mathieu Varin

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Forest characterization in Quebec, Canada, is usually assessed based on photo-interpretation at the stand level. For species identification, this often results in a lack of precision. Very high spatial resolution imagery, such as DigitalGlobe, and Light Detection and Ranging (LiDAR), have the potential to overcome the limitations of aerial imagery. To date, few studies have used that data to map a large number of species at the tree level using machine learning techniques. The main objective of this study is to map 11 individual high tree species ( > 17m) at the tree level using an object-based approach in the broadleaf forest of Kenauk Nature, Quebec. For the individual tree crown segmentation, three canopy-height models (CHMs) from LiDAR data were assessed: 1) the original, 2) a filtered, and 3) a corrected model. The corrected CHM gave the best accuracy and was then coupled with imagery to refine tree species crown identification. When compared with photo-interpretation, 90% of the objects represented a single species. For modeling, 313 variables were derived from 16-band WorldView-3 imagery and LiDAR data, using radiance, reflectance, pixel, and object-based calculation techniques. Variable selection procedures were employed to reduce their number from 313 to 16, using only 11 bands to aid reproducibility. For classification, a global approach using all 11 species was compared to a semi-hierarchical hybrid classification approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were used: (1) support vector machine (SVM), (2) classification and regression tree (CART), (3) random forest (RF), (4) k-nearest neighbors (k-NN), and (5) linear discriminant analysis (LDA). Each model was tuned separately for all approaches and levels. For the global approach, the best model was the SVM using eight variables (overall accuracy (OA): 80%, Kappa: 0.77). With the semi-hierarchical hybrid approach, at the tree type level, the best model was the k-NN using six variables (OA: 100% and Kappa: 1.00). At the level of identifying broadleaf and conifer species, the best model was the SVM, with OA of 80% and 97% and Kappa values of 0.74 and 0.97, respectively, using seven variables for both models. This paper demonstrates that a hybrid classification approach gives better results and that using 16-band WorldView-3 with LiDAR data leads to more precise predictions for tree segmentation and classification, especially when the number of tree species is large.

Keywords: tree species, object-based, classification, multispectral, machine learning, WorldView-3, LiDAR

Procedia PDF Downloads 118

Authors: Mohamad R. Moshtagh, Ahmad Bagheri

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Accurate fault detection in rotating machinery is of utmost importance to ensure optimal performance and prevent costly downtime in industrial applications. This study presents a robust fault detection system based on vibration data collected from rotating gears under various operating conditions. The considered scenarios include: (1) both gears being healthy, (2) one healthy gear and one faulty gear, and (3) introducing an imbalanced condition to a healthy gear. Vibration data was acquired using a Hentek 1008 device and stored in a CSV file. Python code implemented in the Spider environment was used for data preprocessing and analysis. Winner features were extracted using the Wiener feature selection method. These features were then employed in multiple machine learning algorithms, including Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), and Random Forest, to evaluate their performance in detecting and classifying faults in both the training and validation datasets. The comparative analysis of the methods revealed the superior performance of the Wiener-CNN approach. The Wiener-CNN method achieved a remarkable accuracy of 100% for both the two-class (healthy gear and faulty gear) and three-class (healthy gear, faulty gear, and imbalanced) scenarios in the training and validation datasets. In contrast, the other methods exhibited varying levels of accuracy. The Wiener-MLP method attained 100% accuracy for the two-class training dataset and 100% for the validation dataset. For the three-class scenario, the Wiener-MLP method demonstrated 100% accuracy in the training dataset and 95.3% accuracy in the validation dataset. The Wiener-KNN method yielded 96.3% accuracy for the two-class training dataset and 94.5% for the validation dataset. In the three-class scenario, it achieved 85.3% accuracy in the training dataset and 77.2% in the validation dataset. The Wiener-Random Forest method achieved 100% accuracy for the two-class training dataset and 85% for the validation dataset, while in the three-class training dataset, it attained 100% accuracy and 90.8% accuracy for the validation dataset. The exceptional accuracy demonstrated by the Wiener-CNN method underscores its effectiveness in accurately identifying and classifying fault conditions in rotating machinery. The proposed fault detection system utilizes vibration data analysis and advanced machine learning techniques to improve operational reliability and productivity. By adopting the Wiener-CNN method, industrial systems can benefit from enhanced fault detection capabilities, facilitating proactive maintenance and reducing equipment downtime.

Keywords: fault detection, gearbox, machine learning, wiener method

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Authors: Ka C. Lam, Oluwafunmibi S. Idowu

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Uncertainty in cost is a major factor affecting performance of construction projects. To our knowledge, several conceptual cost models have been developed with varying degrees of accuracy. Incorporating conceptual quantities into conceptual cost models could improve the accuracy of early predesign cost estimates. Hence, the development of quantity models for estimating conceptual quantities of framed reinforced concrete structures using supervised machine learning is the aim of the current research. Using measured quantities of structural elements and design variables such as live loads and soil bearing pressures, response and predictor variables were defined and used for constructing conceptual quantities models. Twenty-four models were developed for comparison using a combination of non-parametric support vector regression, linear regression, and bootstrap resampling techniques. R programming language was used for data analysis and model implementation. Gross soil bearing pressure and gross floor loading were discovered to have a major influence on the quantities of concrete and reinforcement used for foundations. Building footprint and gross floor loading had a similar influence on beams and slabs. Future research could explore the modelling of other conceptual quantities for walls, finishes, and services using machine learning techniques. Estimation of conceptual quantities would assist construction planners in early resource planning and enable detailed performance evaluation of early cost predictions.

Keywords: bootstrapping, conceptual quantities, modelling, reinforced concrete, support vector regression

Procedia PDF Downloads 199

Authors: Yitao Lei, Xingxiang Zhai, Burra Venkata Durga Kumar

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As the quantity and complexity of computing in large-scale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes high-performance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NP-hard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decision-making ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling, and proposes the challenges and improvement directions for DRL-based resource scheduling algorithms.

Keywords: resource scheduling, deep reinforcement learning, distributed system, artificial intelligence

Procedia PDF Downloads 93

Authors: Olusegun D. Badewa, E. K. Tsado, A. S. Gana, K. D. Tolorunse, R. U. Okechukwu, P. Iluebbey, S. Ibrahim

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Farmers are faced with varying production challenges ranging from unstable weather due to climate change, low yield, malnutrition, cattle invasion, and bush fires that have always affected their livelihood. Research effort must therefore be centered on improving farmers’ livelihood, nutrition, and health by providing early bulking biofortified cassava varieties that could be harvested earlier with reasonable root yield and thereby preventing long stay of the crop on their farmland. This study evaluated cassava genotypes at different harvesting months of 3, 6, 9, and 12 months after planting in order to evaluate their bulking rate at different agroecology of Mokwa and Ubiaja. Data were collected on fresh storage root yield, Harvest index, and Dry matter content. It was shown from the study that traits FSRY, HI, and DM were significant for genotype and months after planting and variable among the genotype while location had no effect on the yield traits. Early bulking genotypes were not high yielding and showed discontinuity at some point across the months. The retrogression in yield performance across months had no effect on the highest yielding. Also, for all the genotypes and across evaluated months, FSRY reduces at 9 MAP due to a reduction in dry matter content during the same month, and the best performing genotype was the genotype IBA90581, followed by IBA120036, IBA130896, and IBA980581 while the least performing was genotype IBA130818.

Keywords: early bulking, dry mater, harvest index, high yielding, root yield

Procedia PDF Downloads 208

Authors: Renata Shitakubo, Hanyu Yangcheng, Jay-lin Jane, Fernanda Peroni Okita, Beatriz Cordenunsi

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In the degradation transitory starch model, the enzymatic activity of glucan/water dikinase (GWD) and phosphoglucan/water dikinase (PWD) are essential for the granule degradation. GWD and PWD phosphorylate glucose molecules in the positions C6 and C3, respectively, in the amylopectin chains. This action is essential to allow that β-amylase degrade starch granules without previous action of α-amylase. During banana starch degradation, as part of banana ripening, both α- and β-amylases activities and proteins were already detected and, it is also known that there is a GWD and PWD protein bounded to the starch granule. Therefore, the aim of this study was to quantify both Gluc-6P and Gluc-3P in order to estimate the importance of the GWD-PWD-β-amylase pathway in banana starch degradation. Starch granules were isolated as described by Peroni-Okita et al (Carbohydrate Polymers, 81:291-299, 2010), from banana fruit at different stages of ripening, green (20.7%), intermediate (18.2%) and ripe (6.2%). Total phosphorus content was determinate following the Smith and Caruso method (1964). Gluc-6P and Gluc-3P quantifications were performed as described by Lim et al (Cereal Chemistry, 71(5):488-493, 1994). Total phosphorous content in green banana starch is found as 0.009%, intermediary banana starch 0.006% and ripe banana starch 0.004%, both by the colorimetric method and 31P-NMR. The NMR analysis showed the phosphorus content in C6 and C3. The results by NMR indicate that the amylopectin is phosphorylate by GWD and PWD before the bananas become ripen. Since both the total content of phosphorus and phosphorylated glucose molecules at positions C3 and C6 decrease with the starch degradation, it can be concluded that this phosphorylation occurs only in the surface of the starch granule and before the fruit be harvested.

Keywords: starch, GWD, PWD, 31P-NMR

Procedia PDF Downloads 443

Authors: Sidiqat A. Aderinoye-Abdulwahab, Lateef L. Adefalu, Jubril O. Animashaun

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Pastoralists in Nigeria are situated in dry regions - where water and pasture for livestock are particularly scarce, as well as areas with poor availability of social amenities and infrastructure. This study therefore explored how extension service could be used to reduce the exposure of nomads to effects of seasonality, climate change, and the poor environmental conditions. The study was carried out in Northern guinea Savannah region of Nigeria because pastoralists have settled there in large numbers due to desertification and low rainfall in the arid regions. A multi-stage sampling procedure was used to arrive at the selection of two states (Kwara and Nassarawa) in the region. A total of 63 respondents were randomly chosen using simple random sampling. Focus group discussions and questionnaire were used to gather information while the data was analysed using content analysis. The facilities required by the sampled households are milking machine, cheese making machine, and preservatives to increase the shelf life of cheese. Whilst, the extension service required are demonstration on cheese making, training and seminars on animal husbandry. Additionally, livestock of pastoralists often encroach on farmers’ plots which usually result in pastoralist-farmer conflicts. The study thus recommends diversification of economic activity from livestock to non-livestock related activities as well as creation of grazing routes to reduce pastoralist/farmer conflict.

Keywords: arid region, coping strategies, livestock, livelihood

Procedia PDF Downloads 370

Authors: Iyabode A. Kehinde, Temitope A. Oyedele, Clement G. Afolabi

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One of the limitations of African yam bean (AYB) (Sphenostylis sternocarpa) is poor storage ability due to the adverse effect of seed-borne fungi. This study was conducted to examine the effects of storage methods on the nutritive composition of AYB seeds stored in three types of storage materials viz; Jute bags, Polypropylene bags, and Plastic Bowls. Freshly harvested seeds of AYB seeds were stored in all the storage materials for 6 months using 2 × 3 factorial (2 AYB cultivars and 3 storage methods) in 3 replicates. The proximate analysis of the stored AYB seeds was carried out at 3 and 6 months after storage using standard methods. The temperature and relative humidity of the storeroom was recorded monthly with Kestrel pocket weather tracker 4000. Seeds stored in jute bags gave the best values for crude protein (24.87%), ash (5.69%) and fat content (6.64%) but recorded least values for crude fibre (2.55%), carbohydrate (50.86%) and moisture content (12.68%) at the 6th month of storage. The temperature of the storeroom decreased from 32.9ºC - 28.3ºC, while the relative humidity increased from 78% - 86%. Decreased incidence of field fungi namely: Rhizopus oryzae, Aspergillus flavus, Geotricum candidum, Aspergillus fumigatus and Mucor meihei was accompanied by the increase in storage fungi viz: Apergillus niger, Mucor hiemalis, Penicillium espansum and Penicillium atrovenetum with prolonged storage. The study showed that of the three storage materials jute bag was more effective at preserving AYB seeds.

Keywords: storage methods, proximate composition, African Yam Bean, fungi

Procedia PDF Downloads 115

Authors: Deniz Peksen

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This thesis aims to predict trend movement of closing price of stock and to maximize portfolio by utilizing the predictions. In this context, the study aims to define a stock portfolio strategy from models created by using Logistic Regression, Gradient Boosting and Random Forest. Recently, predicting the trend of stock price has gained a significance role in making buy and sell decisions and generating returns with investment strategies formed by machine learning basis decisions. There are plenty of studies in the literature on the prediction of stock prices in capital markets using machine learning methods but most of them focus on closing prices instead of the direction of price trend. Our study differs from literature in terms of target definition. Ours is a classification problem which is focusing on the market trend in next 20 trading days. To predict trend direction, fourteen years of data were used for training. Following three years were used for validation. Finally, last three years were used for testing. Training data are between 2002-06-18 and 2016-12-30 Validation data are between 2017-01-02 and 2019-12-31 Testing data are between 2020-01-02 and 2022-03-17 We determine Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate as benchmarks which we should outperform. We compared our machine learning basis portfolio return on test data with return of Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate. We assessed our model performance with the help of roc-auc score and lift charts. We use logistic regression, Gradient Boosting and Random Forest with grid search approach to fine-tune hyper-parameters. As a result of the empirical study, the existence of uptrend and downtrend of five stocks could not be predicted by the models. When we use these predictions to define buy and sell decisions in order to generate model-based-portfolio, model-based-portfolio fails in test dataset. It was found that Model-based buy and sell decisions generated a stock portfolio strategy whose returns can not outperform non-model portfolio strategies on test dataset. We found that any effort for predicting the trend which is formulated on stock price is a challenge. We found same results as Random Walk Theory claims which says that stock price or price changes are unpredictable. Our model iterations failed on test dataset. Although, we built up several good models on validation dataset, we failed on test dataset. We implemented Random Forest, Gradient Boosting and Logistic Regression. We discovered that complex models did not provide advantage or additional performance while comparing them with Logistic Regression. More complexity did not lead us to reach better performance. Using a complex model is not an answer to figure out the stock-related prediction problem. Our approach was to predict the trend instead of the price. This approach converted our problem into classification. However, this label approach does not lead us to solve the stock prediction problem and deny or refute the accuracy of the Random Walk Theory for the stock price.

Keywords: stock prediction, portfolio optimization, data science, machine learning

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Authors: Cristina Diaz, Lucia Perez, Simone Visigalli, Giuseppe Di Florio, Gonzalo Fuentes, Roberto Canziani, Paolo Gronchi

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During the last 50 years, the selection of materials is one of the main decisions in machine design for different industrial applications. It is due to numerous physical, chemical, mechanical and technological factors to consider in it. Corrosion effects are related with all of these factors and impact in the life cycle, machine incidences and the costs for the life of the machine. Corrosion affects the deterioration or destruction of metals due to the reaction with the environment, generally wet. In food industry, dewatering industry, concrete industry, paper industry, etc. corrosion is an unsolved problem and it might introduce some alterations of some characteristics in the final product. Nowadays, depending on the selected metal, its surface and its environment of work, corrosion prevention might be a change of metal, use a coating, cathodic protection, use of corrosion inhibitors, etc. In the vast majority of the situations, use of a corrosion resistant material or in its defect, a corrosion protection coating is the solution. Stainless steels are widely used in machine design, because of their strength, easily cleaned capacity, corrosion resistance and appearance. Typical used are AISI 304 and AISI 316. However, their benefits don’t fit every application, and some coatings are required against corrosion such as some paintings, galvanizing, chrome plating, SiO₂, TiO₂ or ZrO₂ coatings, etc. In this work, some coatings based in a bilayer made of Titanium-Tantalum, Titanium-Niobium, Titanium-Hafnium or Titanium-Zirconium, have been developed used magnetron sputtering configuration by PVD (Physical Vapor Deposition) technology, for trying to reduce corrosion effects on AISI 304, AISI 316 and comparing it with Titanium alloy substrates. Ti alloy display exceptional corrosion resistance to chlorides, sour and oxidising acidic media and seawater. In this study, Ti alloy (99%) has been included for comparison with coated AISI 304 and AISI 316 stainless steel. Corrosion tests were conducted by a Gamry Instrument under ASTM G5-94 standard, using different electrolytes such as tomato salsa, wine, olive oil, wet compost, a mix of sand and concrete with water and NaCl for testing corrosion in different industrial environments. In general, in all tested environments, the results showed an improvement of corrosion resistance of all coated AISI 304 and AISI 316 stainless steel substrates when they were compared to uncoated stainless steel substrates. After that, comparing these results with corrosion studies on uncoated Ti alloy substrate, it was observed that in some cases, coated stainless steel substrates, reached similar current density that uncoated Ti alloy. Moreover, Titanium-Zirconium and Titanium-Tantalum coatings showed for all substrates in study including coated Ti alloy substrates, a reduction in current density more than two order in magnitude. As conclusion, Ti-Ta, Ti-Zr, Ti-Nb and Ti-Hf coatings have been developed for improving corrosion resistance of AISI 304 and AISI 316 materials. After corrosion tests in several industry environments, substrates have shown improvements on corrosion resistance. Similar processes have been carried out in Ti alloy (99%) substrates. Coated AISI 304 and AISI 316 stainless steel, might reach similar corrosion protection on the surface than uncoated Ti alloy (99%). Moreover, coated Ti Alloy (99%) might increase its corrosion resistance using these coatings.

Keywords: coatings, corrosion, PVD, stainless steel

Procedia PDF Downloads 143

Authors: Adira Nofeadri Ryofi, Alvin Andrianus, Anna Khairunnisa, Anugrah Cahyo Widodo, Arbhyando Tri Putrananda, Arsy Imanda N. Raswati, Gita Rahmaningsih, Ina Agustina

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The consumption level of fresh bananas from 2005 until 2010, increased from 8.2 to 10 kg/capita/year. The commercial scale of banana generally harvested when it still green to make the banana avoid physical damage, chemical, and disease after harvest and ripe fruit. That first metabolism activity can be used as a synthesis reaction. Ripening fruit was influenced by ethylene hormone that synthesized in fruit which is experiencing ripe and including hormone in the ripening fruit process in klimaterik phase. This ethylene hormone is affected by the respiration level that would speed up the restructuring of carbohydrates inside the fruit, so the weighting of fruit will be decreased. Compared to other klimaterik fruit, banana is a fruit that has a medium ethylene production rate and the rate of respiration is low. The salicylic acid can regulate the result number of the growth process or the development of fruits and plants. Salicylic acid serves to hinder biosynthesis ethylene and delay senses. The research aims to understand the influence of salicylic acid concentration that derived from the waste of avocado skin in inhibition process to ethylene production that the maturation can be controlled, so it can keep the quality of banana for storage. It is also to increase the potential value of the waste of avocado skin that were still used in industrial cosmetics.

Keywords: ethylene hormone, extract avocado skin, inhibitor, salicylic acid

Procedia PDF Downloads 221

Authors: Bilal Bhat, M. Masroor A. Khan, Moin Uddin, M. Naeem

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Aromatic herb, peppermint (Mentha piperita L.), is a natural hybrid (M. aquatica × M. spicata) with immense therapeutic uses, apart from other potential uses. Peppermint oil is one of the most popular and widely used essential oil (EO), because of its main components menthol and menthone. In view of enhancing growth, yield and quality of this medicinally important herb, a pot experiment was conducted in the net-house of the department. The experiment was aimed at studying the effect of graded levels of N, P, and K on growth, biochemical characteristics, and content and composition of EO in Mentha piperita L. Six NPK treatments (viz. N0P0K0, N20P20K20, N40P40K40, N20+20 P20+20 K20+20, N60P60K60, and N30+30 P30+30 K30+30) were tested. The plants were harvested 150 days after transplanting. The crop performance was assessed in terms of growth attributes, physiological activities, herbage yield and content as well as yield of active constituents of Mentha piperita L. Biochemical parameters were analyzed spectrophotometrically. The EO was extracted using Clevenger’s apparatus and the active constituents of the oil were determined using Gas Chromatography. Split-dose application of N, P and K (N30+30 P30+30 K30+30) ameliorated most of the parameters significantly including, fresh and dry weight of plant, NPK content, chlorophyll and carotenoids content, and the activities of carbonic anhydrase and nitrate reductase in the leaves. It also enhanced the EO content (44.0%), EO yield (91.0%), menthol content (14.1%), menthone content (34.0%), menthyl acetate content (16.9%) and 1, 8-cineole content (43.7%) but decreased the pulegone content (36.8%). Conclusively, the fertilization proved useful in enhancing the EO content, yield and other EO components of the plant. Thus, the yield and quality of EO of peppermint may be improved by this agricultural strategy.

Keywords: mentha piperita, menthol, menthone, EO

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Authors: Nyeng P. Gyang

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

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

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Authors: B. Padmaja, V. V. Rama Prasad, K. V. N. Sunitha, E. Krishna Rao Patro

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Stress is a psychological condition that reduces the quality of sleep and affects every facet of life. Constant exposure to stress is detrimental not only for mind but also body. Nevertheless, to cope with stress, one should first identify it. This paper provides an effective method for the cognitive stress level detection by using data provided from a physical activity tracker device Fitbit. This device gathers people’s daily activities of food, weight, sleep, heart rate, and physical activities. In this paper, four major stressors like physical activities, sleep patterns, working hours and change in heart rate are used to assess the stress levels of individuals. The main motive of this system is to use machine learning approach in stress detection with the help of Smartphone sensor technology. Individually, the effect of each stressor is evaluated using logistic regression and then combined model is built and assessed using variants of ordinal logistic regression models like logit, probit and complementary log-log. Then the quality of each model is evaluated using Akaike Information Criterion (AIC) and probit is assessed as the more suitable model for our dataset. This system is experimented and evaluated in a real time environment by taking data from adults working in IT and other sectors in India. The novelty of this work lies in the fact that stress detection system should be less invasive as possible for the users.

Keywords: physical activity tracker, sleep pattern, working hours, heart rate, smartphone sensor

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Authors: Shakila N. Senarath, Dinesh Asanka, Janaka Wijayanayake

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The necessity of cosmetic products is arising to fulfill consumer needs of personality appearance and hygiene. A cosmetic product consists of various chemical ingredients which may help to keep the skin healthy or may lead to damages. Every chemical ingredient in a cosmetic product does not perform on every human. The most appropriate way to select a healthy cosmetic product is to identify the texture of the body first and select the most suitable product with safe ingredients. Therefore, the selection process of cosmetic products is complicated. Consumer surveys have shown most of the time, the selection process of cosmetic products is done in an improper way by consumers. From this study, a content-based system is suggested that recommends cosmetic products for the human factors. To such an extent, the skin type, gender and price range will be considered as human factors. The proposed system will be implemented by using Machine Learning. Consumer skin type, gender and price range will be taken as inputs to the system. The skin type of consumer will be derived by using the Baumann Skin Type Questionnaire, which is a value-based approach that includes several numbers of questions to derive the user’s skin type to one of the 16 skin types according to the Bauman Skin Type indicator (BSTI). Two datasets are collected for further research proceedings. The user data set was collected using a questionnaire given to the public. Those are the user dataset and the cosmetic dataset. Product details are included in the cosmetic dataset, which belongs to 5 different kinds of product categories (Moisturizer, Cleanser, Sun protector, Face Mask, Eye Cream). An alternate approach of TF-IDF (Term Frequency – Inverse Document Frequency) is applied to vectorize cosmetic ingredients in the generic cosmetic products dataset and user-preferred dataset. Using the IF-IPF vectors, each user-preferred products dataset and generic cosmetic products dataset can be represented as sparse vectors. The similarity between each user-preferred product and generic cosmetic product will be calculated using the cosine similarity method. For the recommendation process, a similarity matrix can be used. Higher the similarity, higher the match for consumer. Sorting a user column from similarity matrix in a descending order, the recommended products can be retrieved in ascending order. Even though results return a list of similar products, and since the user information has been gathered, such as gender and the price ranges for product purchasing, further optimization can be done by considering and giving weights for those parameters once after a set of recommended products for a user has been retrieved.

Keywords: content-based filtering, cosmetics, machine learning, recommendation system

Procedia PDF Downloads 120

Authors: Nathaniel Carpenter

Abstract:

Web-enabled systems for supporting youth mental health management in Australia are pioneering in their field; however, with their success, these systems are experiencing exponential growth in demand which is straining an already stretched service. Supporting youth mental is critical as the lack of support is associated with significant and lasting negative consequences. To meet this growing demand, and provide critical support, investigations are needed on evaluating and improving existing online support services. Improvements should focus on developing frameworks capable of augmenting and scaling service provisions. There are few investigations informing best-practice frameworks when implementing e-mental health support systems for youth mental health; there are fewer which implement machine learning or artificially intelligent systems to facilitate the delivering of services. This investigation will use a case study methodology to highlight the design features which are important for systems to enable young people to self-manage their mental health. The investigation will also highlight the current information system challenges, to include challenges associated with service quality, provisioning, and scaling. This work will propose methods of meeting these challenges through improved design, service augmentation and automation, service quality, and through artificially intelligent inspired solutions. The results of this study will inform a framework for supporting youth mental health with intelligent and scalable web-enabled technologies to support an ever-growing user base.

Keywords: artificial intelligence, information systems, machine learning, youth mental health

Procedia PDF Downloads 95

Authors: Sung Uk Park, Young Su Kang, Sangmo Kang, Young Kweon Suh

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Mineral product, waste concrete (fine aggregates), waste in the optical field, industry, and construction employ separators to separate solids and classify them according to their size. Various sorting machines are used in the industrial field such as those operating under electrical properties, centrifugal force, wind power, vibration, and magnetic force. Study on separators has been carried out to contribute to the environmental industry. In this study, we perform CFD analysis for understanding the basic mechanism of the separation of waste concrete (fine aggregate) particles from air with a machine built with a rotor with blades. In CFD, we first performed two-dimensional particle tracking for various particle sizes for the model with 1 degree, 1.5 degree, and 2 degree angle between each blade to verify the boundary conditions and the method of rotating domain method to be used in 3D. Then we developed 3D numerical model with ANSYS CFX to calculate the air flow and track the particles. We judged the capability of particle separation for given size by counting the number of particles escaping from the domain toward the exit among 10 particles issued at the inlet. We confirm that particles experience stagnant behavior near the exit of the rotating blades where the centrifugal force acting on the particles is in balance with the air drag force. It was also found that the minimum particle size that can be separated by the machine with the rotor is determined by its capability to stay at the outlet of the rotor channels.

Keywords: environmental industry, separator, CFD, fine aggregate

Procedia PDF Downloads 580

Authors: Uche C. Okafor, Nwanneka M. Okpokwu, Felix Nwafor, Carl E. A. Okezie

Abstract:

Securidaca longepedunculata Fresen (Violet tree) belongs to the family Polygalaceae characterised by papillionaceous purplish flowers. This medicinally valued plant disappears at an alarming rate due to intensified anthropopressure particularly the unregulated manner of subterranean plant parts' collection from natural stands. Some indiscriminately harvested plants bear seeds containing both mature and immature zygotic embryos that are often discarded. Here, such seeds are collected for this experiment. Seeds were collected, washed, de-coated, and dipped in 70 % (v/v) ethanol for 30 s followed by rising in 5 % solution sodium hypochlorite, containing two drops of tween 20, for another 25 min. Mature zygotic embryos (MZEs) were excised from seeds and cultured in two basal media (MS and B5), three carbon sources (sucrose, glucose and fructose) at five concentrations (0-40 g/L) while immature zygotic embryos (iMZEs) were composed on similar basal media and carbon source supplemented with 0-2 mg/L Benzylaminopurine (BAP) and 0-2 mg/L Indole acetic acid (IAA). MZEs cultured on MS + 30g/L sucrose differed significantly from other treatments at p≤0.05 with maximum percent sprouting (85.24± 5.67 %) and shoot length (7.53±0.67 cm). MZEs culture had the maximum percent sprouting (85.24± 5.67 %) and shoot length (7.53±0.67 cm) in medium containing MS+ 30g L-1 sucrose. iMZEs on the other hand had maximum growth on MS + 40g/L sucrose supplemented with 1.5 mg/L IAA+ 1.0 mg/L BAP. This study is a geared towards creating an alternative path for the maximum production of plants in vitro, thereby, preventing the plants from disappearing.

Keywords: Gamborg's medium, Murashige and Skoog medium, Securidaca longepedunculata, zygotic embryos

Procedia PDF Downloads 143

Authors: John Gibbs, Benjamin Flanders, Dylan Pozorski, Weixuan Liu

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Weight Painting–adjusting the influence a skeletal joint has on a given vertex in a character mesh–is an arduous and time con- suming part of the 3D animation pipeline. This process generally requires a trained technical animator and many hours of work to complete. Our skiNNer plug-in, which works within Autodesk’s Maya 3D animation software, uses Machine Learning and data pro- cessing techniques to create a deep neural network model that can accomplish the weight painting task in seconds rather than hours for bipedal quasi-humanoid character meshes. In order to create a properly trained network, a number of challenges were overcome, including curating an appropriately large data library, managing an arbitrary 3D mesh size, handling arbitrary skeletal architectures, accounting for extreme numeric values (most data points are near 0 or 1 for weight maps), and constructing an appropriate neural network model that can properly capture the high frequency alter- ation between high weight values (near 1.0) and low weight values (near 0.0). The arrived at neural network model is a cross between a traditional CNN, deep residual network, and fully dense network. The resultant network captures the unusually hard-edged features of a weight map matrix, and produces excellent results on many bipedal models.

Keywords: 3d animation, animation, character, rigging, skinning, weight painting, machine learning, artificial intelligence, neural network, deep neural network

Procedia PDF Downloads 251

Authors: Arfan Ali Nagra, Tariq Shahzad, Meshal Alharbi, Khalid Masood Khan, Muhammad Mugees Asif, Taher M. Ghazal, Khmaies Ouahada

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Gene selection is an essential step for the classification of microarray cancer data. Gene expression cancer data (DNA microarray) facilitates computing the robust and concurrent expression of various genes. Particle swarm optimization (PSO) requires simple operators and less number of parameters for tuning the model in gene selection. The selection of a prognostic gene with small redundancy is a great challenge for the researcher as there are a few complications in PSO based selection method. In this research, a new variant of PSO (Self-inertia weight adaptive PSO) has been proposed. In the proposed algorithm, SIW-APSO-ELM is explored to achieve gene selection prediction accuracies. This new algorithm balances the exploration capabilities of the improved inertia weight adaptive particle swarm optimization and the exploitation. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) is used to search the solution. The SIW-APSO is updated with an evolutionary process in such a way that each particle iteratively improves its velocities and positions. The extreme learning machine (ELM) has been designed for the selection procedure. The proposed method has been to identify a number of genes in the cancer dataset. The classification algorithm contains ELM, K- centroid nearest neighbor (KCNN), and support vector machine (SVM) to attain high forecast accuracy as compared to the start-of-the-art methods on microarray cancer datasets that show the effectiveness of the proposed method.

Keywords: microarray cancer, improved PSO, ELM, SVM, evolutionary algorithms

Procedia PDF Downloads 66

Authors: Phillip Kearney, Constantina Lekakou, Stephen Belcher, Alessandro Sordon

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The focus in the automotive industry is to reduce human operator and machine interaction, so manufacturing becomes more automated and safer. The aim is to lower part cost and construction time as well as defects in the parts, sometimes occurring due to the physical limitations of human operators. A move to automate the layup of reinforcement material in composites manufacturing has resulted in the use of tapes that are placed in position by a robotic deposition head, also described as Automated Fibre Placement (AFP). The process of AFP is limited with respect to the finite amount of material that can be loaded into the machine at any one time. Joining two batches of tape material together involves a splice to secure the ends of the finishing tape to the starting edge of the new tape. The splicing method of choice for the majority of prepreg applications is a hand stich method, and as the name suggests requires human input to achieve. This investigation explores three methods for automated splicing, namely, adhesive, binding and stitching. The adhesive technique uses an additional adhesive placed on the tape ends to be joined. Binding uses the binding agent that is already impregnated onto the tape through the application of heat. The stitching method is used as a baseline to compare the new splicing methods to the traditional technique currently in use. As the methods will be used within a High Speed Automated Fibre Placement (HSAFP) process, this meant the parameters of the splices have to meet certain specifications: (a) the splice must be able to endure a load of 50 N in tension applied at a rate of 1 mm/s; (b) the splice must be created in less than 6 seconds, dictated by the capacity of the tape accumulator within the system. The samples for experimentation were manufactured with controlled overlaps, alignment and splicing parameters, these were then tested in tension using a tensile testing machine. Initial analysis explored the use of the impregnated binding agent present on the tape, as in the binding splicing technique. It analysed the effect of temperature and overlap on the strength of the splice. It was found that the optimum splicing temperature was at the higher end of the activation range of the binding agent, 100 °C. The optimum overlap was found to be 25 mm; it was found that there was no improvement in bond strength from 25 mm to 30 mm overlap. The final analysis compared the different splicing methods to the baseline of a stitched bond. It was found that the addition of an adhesive was the best splicing method, achieving a maximum load of over 500 N compared to the 26 N load achieved by a stitching splice and 94 N by the binding method.

Keywords: analysis, automated fibre placement, high speed, splicing

Procedia PDF Downloads 139

Authors: Muhammad Umair, Yuri Nikolaev, Denis Artemov, Ighor Uzhinsky

Abstract:

A flexible manufacturing system is an integral part of a smart factory of industry 4.0 in which every machine is interconnected and works autonomously. Robots are in the process of replacing humans in every industrial sector. As the cyber-physical-system (CPS) and artificial intelligence (AI) are advancing, the manufacturing industry is getting more dependent on computers than human brains. This modernization has boosted the production with high quality and accuracy and shifted from classic production to smart manufacturing systems. However, material handling for such automated productions is a challenge and needs to be addressed with the best possible solution. Conventional clamping systems are designed for manual work and not suitable for highly automated production systems. Researchers and engineers are trying to find the most economical solution for loading/unloading and transportation workpieces from a warehouse to a machine shop for machining operations and back to the warehouse without human involvement. This work aims to propose an advanced multi-shape tooling solution for highly automated manufacturing systems. The currently obtained result shows that it could function well with automated guided vehicles (AGVs) and modern conveyor belts. The proposed solution is following requirements to be automation-friendly, universal for different part geometry and production operations. We used a bottom-up approach in this work, starting with studying different case scenarios and their limitations and finishing with the general solution.

Keywords: artificial intelligence, cyber physics system, Industry 4.0, material handling, smart factory, flexible manufacturing system

Procedia PDF Downloads 121

Authors: Yingisani Chabalala, Elhadi Adam, Khalid Adem Ali

Abstract:

Mapping smallholder fruit plantations using optical data is challenging due to morphological landscape heterogeneity and crop types having overlapped spectral signatures. Furthermore, cloud covers limit the use of optical sensing, especially in subtropical climates where they are persistent. This research assessed the effectiveness of Sentinel-1 (S1) and Sentinel-2 (S2) data for mapping fruit trees and co-existing land-use types by using support vector machine (SVM) and random forest (RF) classifiers independently. These classifiers were also applied to fused data from the two sensors. Feature ranks were extracted using the RF mean decrease accuracy (MDA) and forward variable selection (FVS) to identify optimal spectral windows to classify fruit trees. Based on RF MDA and FVS, the SVM classifier resulted in relatively high classification accuracy with overall accuracy (OA) = 0.91.6% and kappa coefficient = 0.91% when applied to the fused satellite data. Application of SVM to S1, S2, S2 selected variables and S1S2 fusion independently produced OA = 27.64, Kappa coefficient = 0.13%; OA= 87%, Kappa coefficient = 86.89%; OA = 69.33, Kappa coefficient = 69. %; OA = 87.01%, Kappa coefficient = 87%, respectively. Results also indicated that the optimal spectral bands for fruit tree mapping are green (B3) and SWIR_2 (B10) for S2, whereas for S1, the vertical-horizontal (VH) polarization band. Including the textural metrics from the VV channel improved crop discrimination and co-existing land use cover types. The fusion approach proved robust and well-suited for accurate smallholder fruit plantation mapping.

Keywords: smallholder agriculture, fruit trees, data fusion, precision agriculture

Procedia PDF Downloads 36

Authors: Gaurav Kumar Sinha

Abstract:

The oil refining industry, significant in its energy consumption and carbon emissions, faces increasing pressure to reduce its environmental footprint. This article explores the application of energy efficiency and sustainability analytics as crucial tools for reducing carbon emissions in oil refineries. Through a comprehensive review of current practices and technologies, this study highlights innovative analytical approaches that can significantly enhance energy efficiency. We focus on the integration of advanced data analytics, including machine learning and predictive modeling, to optimize process controls and energy use. These technologies are examined for their potential to not only lower energy consumption but also reduce greenhouse gas emissions. Additionally, the article discusses the implementation of sustainability analytics to monitor and improve environmental performance across various operational facets of oil refineries. We explore case studies where predictive analytics have successfully identified opportunities for reducing energy use and emissions, providing a template for industry-wide application. The challenges associated with deploying these analytics, such as data integration and the need for skilled personnel, are also addressed. The paper concludes with strategic recommendations for oil refineries aiming to enhance their sustainability practices through the adoption of targeted analytics. By implementing these measures, refineries can achieve significant reductions in carbon emissions, aligning with global environmental goals and regulatory requirements.

Keywords: energy efficiency, sustainability analytics, carbon emissions, oil refineries, data analytics, machine learning, predictive modeling, process optimization, greenhouse gas reduction, environmental performance

Procedia PDF Downloads 15

Authors: Serge Tshibangu, Turgay Celik, Zenzo Ncube

Abstract:

Road crashes kill nearly over a million people every year, and leave millions more injured or permanently disabled. Various annual reports reveal that the percentage of fatal crashes due to fatigue/driver falling asleep comes directly after the percentage of fatal crashes due to intoxicated drivers. This percentage is higher than the combined percentage of fatal crashes due to illegal/Un-Safe U-turn and illegal/Un-Safe reversing. Although a relatively small percentage of police reports on road accidents highlights drowsiness and fatigue, the importance of these factors is greater than we might think, hidden by the undercounting of their events. Some scenarios show that these factors are significant in accidents with killed and injured people. Thus the need for an automatic drivers fatigue detection system in order to considerably reduce the number of accidents owing to fatigue.This research approaches the drivers fatigue detection problem in an innovative way by combining cues collected from both temporal analysis of drivers’ faces and environment. Monotony in driving environment is inter-related with visual symptoms of fatigue on drivers’ faces to achieve fatigue detection. Optical and infrared (IR) sensors are used to analyse the monotony in driving environment and to detect the visual symptoms of fatigue on human face. Internal cues from drivers faces and external cues from environment are combined together using machine learning algorithms to automatically detect fatigue.

Keywords: intelligent transportation systems, bayesian networks, yawning computing, machine learning algorithms

Procedia PDF Downloads 443