Search results for: dataset production

Authors: Shraddha Chaudhary, Raksha Agarwal, Niladri Chatterjee

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This work demonstrates a web crawler-based generalized end-to-end open domain Question Answering (QA) system. An efficient QA system requires a significant amount of domain knowledge to answer any question with the aim to find an exact and correct answer in the form of a number, a noun, a short phrase, or a brief piece of text for the user's questions. Analysis of the question, searching the relevant document, and choosing an answer are three important steps in a QA system. This work uses a web scraper (Beautiful Soup) to extract K-documents from the web. The value of K can be calibrated on the basis of a trade-off between time and accuracy. This is followed by a passage ranking process using the MS-Marco dataset trained on 500K queries to extract the most relevant text passage, to shorten the lengthy documents. Further, a QA system is used to extract the answers from the shortened documents based on the query and return the top 3 answers. For evaluation of such systems, accuracy is judged by the exact match between predicted answers and gold answers. But automatic evaluation methods fail due to the linguistic ambiguities inherent in the questions. Moreover, reference answers are often not exhaustive or are out of date. Hence correct answers predicted by the system are often judged incorrect according to the automated metrics. One such scenario arises from the original Google Natural Question (GNQ) dataset which was collected and made available in the year 2016. Use of any such dataset proves to be inefficient with respect to any questions that have time-varying answers. For illustration, if the query is where will be the next Olympics? Gold Answer for the above query as given in the GNQ dataset is “Tokyo”. Since the dataset was collected in the year 2016, and the next Olympics after 2016 were in 2020 that was in Tokyo which is absolutely correct. But if the same question is asked in 2022 then the answer is “Paris, 2024”. Consequently, any evaluation based on the GNQ dataset will be incorrect. Such erroneous predictions are usually given to human evaluators for further validation which is quite expensive and time-consuming. To address this erroneous evaluation, the present work proposes an automated approach for evaluating time-dependent question-answer pairs. In particular, it proposes a metric using the current timestamp along with top-n predicted answers from a given QA system. To test the proposed approach GNQ dataset has been used and the system achieved an accuracy of 78% for a test dataset comprising 100 QA pairs. This test data was automatically extracted using an analysis-based approach from 10K QA pairs of the GNQ dataset. The results obtained are encouraging. The proposed technique appears to have the possibility of developing into a useful scheme for gathering precise, reliable, and specific information in a real-time and efficient manner. Our subsequent experiments will be guided towards establishing the efficacy of the above system for a larger set of time-dependent QA pairs.

Keywords: web-based information retrieval, open domain question answering system, time-varying QA, QA evaluation

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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

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Authors: Shengnan Chen, Shuhua Wang

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Successful production of hydrocarbon from unconventional tight oil reserves has changed the energy landscape in North America. The oil contained within these reservoirs typically will not flow to the wellbore at economic rates without assistance from advanced horizontal well and multi-stage hydraulic fracturing. Efficient and economic development of these reserves is a priority of society, government, and industry, especially under the current low oil prices. Meanwhile, society needs technological and process innovations to enhance oil recovery while concurrently reducing environmental impacts. Recently, big data analysis and artificial intelligence become very popular, developing data-driven insights for better designs and decisions in various engineering disciplines. However, the application of data mining in petroleum engineering is still in its infancy. The objective of this research aims to apply intelligent data analysis and data-driven models to exploit unconventional oil reserves both efficiently and economically. More specifically, a comprehensive database including the reservoir geological data, reservoir geophysical data, well completion data and production data for thousands of wells is firstly established to discover the valuable insights and knowledge related to tight oil reserves development. Several data analysis methods are introduced to analysis such a huge dataset. For example, K-means clustering is used to partition all observations into clusters; principle component analysis is applied to emphasize the variation and bring out strong patterns in the dataset, making the big data easy to explore and visualize; exploratory factor analysis (EFA) is used to identify the complex interrelationships between well completion data and well production data. Different data mining techniques, such as artificial neural network, fuzzy logic, and machine learning technique are then summarized, and appropriate ones are selected to analyze the database based on the prediction accuracy, model robustness, and reproducibility. Advanced knowledge and patterned are finally recognized and integrated into a modified self-adaptive differential evolution optimization workflow to enhance the oil recovery and maximize the net present value (NPV) of the unconventional oil resources. This research will advance the knowledge in the development of unconventional oil reserves and bridge the gap between the big data and performance optimizations in these formations. The newly developed data-driven optimization workflow is a powerful approach to guide field operation, which leads to better designs, higher oil recovery and economic return of future wells in the unconventional oil reserves.

Keywords: big data, artificial intelligence, enhance oil recovery, unconventional oil reserves

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Authors: Esha Banerjee, Atul Kumar Ojha, Girish Nath Jha

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This study aims to develop an intonation labeled database for Hindi. Although no single standard for prosody labeling exists in Hindi, researchers in the past have employed perceptual and statistical methods in literature to draw inferences about the behavior of prosody patterns in Hindi. Based on such existing research and largely agreed upon intonational theories in Hindi, this study attempts to develop a manually annotated prosodic corpus of Hindi speech data, which can be used for training speech models for natural-sounding speech in the future. 100 sentences ( 500 words) each for declarative and interrogative types have been labeled using Praat.

Keywords: speech dataset, Hindi, intonation, labeled corpus

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Authors: Gehad S. Kaseb, Mona F. Ahmed

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Arabic Sentiment Analysis (SA) is one of the most common research fields with many open areas. Few studies apply SA to Arabic dialects. This paper proposes different pre-processing steps and a modified methodology to improve the accuracy using normal Support Vector Machine (SVM) classification. The paper works on two datasets, Arabic Sentiment Tweets Dataset (ASTD) and Extended Arabic Tweets Sentiment Dataset (Extended-AATSD), which are publicly available for academic use. The results show that the classification accuracy approaches 86%.

Keywords: Arabic, classification, sentiment analysis, tweets

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Authors: Ersin Karakaya, Halit Tutar

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Reliable foods must be consumed in terms of healthy nutrition. The production and dissemination of diatom products in Turkey is rapidly evolving on the basis of preserving ecological balance, ensuring sustainability in agriculture and offering quality, reliable products to consumers. In this study, year in Turkey as (2002- 2015) to determine values of such as cultivated land of organic vegetable production, production levels, production quantity, number of products, number of farmers. It is intended to make the econometric analysis of the factors affecting the production of organic vegetable production (Number of products, Number of farmers and cultivated land). The main material of the study has created secondary data in relation to the 2002-2015 period as organic vegetable production in Turkey and regression analysis of the factors affecting the value of production of organic vegetable is determined by the Least Squares Method with EViews statistical software package.

Keywords: number of farmers, cultivated land, Eviews, Turkey

Procedia PDF Downloads 267

Authors: Yash Jain

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The US Center for Disease Control has recommended wearing masks to slow the spread of the virus. The research uses a video feed from a camera to conduct real-time classifications of whether or not a human is correctly wearing a mask, incorrectly wearing a mask, or not wearing a mask at all. Utilizing two distinct datasets from the open-source website Kaggle, a mask detection network had been trained. The first dataset that was used to train the model was titled 'Face Mask Detection' on Kaggle, where the dataset was retrieved from and the second dataset was titled 'Face Mask Dataset, which provided the data in a (YOLO Format)' so that the TinyYoloV3 model could be trained. Based on the data from Kaggle, two machine learning models were implemented and trained: a Tiny YoloV3 Real-time model and a two-stage neural network classifier. The two-stage neural network classifier had a first step of identifying distinct faces within the image, and the second step was a classifier to detect the state of the mask on the face and whether it was worn correctly, incorrectly, or no mask at all. The TinyYoloV3 was used for the live feed as well as for a comparison standpoint against the previous two-stage classifier and was trained using the darknet neural network framework. The two-stage classifier attained a mean average precision (MAP) of 80%, while the model trained using TinyYoloV3 real-time detection had a mean average precision (MAP) of 59%. Overall, both models were able to correctly classify stages/scenarios of no mask, mask, and incorrectly worn masks.

Keywords: datasets, classifier, mask-detection, real-time, TinyYoloV3, two-stage neural network classifier

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Authors: Reina Kawase

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Japan set the goal of 80% reduction in GHG emissions by 2050. To consider countermeasures for reducing GHG emission, the production estimation of energy intensive materials, such as steel, is essential. About 50% of steel production is exported in Japan, so it is necessary to consider steel production including export. Steel productions from 2005-2050 in Japan were estimated under various global assumptions based on combination of scenarios such as goods trade scenarios and steel making process selection scenarios. Process selection scenarios decide volume of steel production by process (basic oxygen furnace and electric arc furnace) with considering steel consumption projection, supply-demand balance of steel, and scrap surplus. The range of steel production by process was analyzed. Maximum steel production was estimated under the scenario which consumes scrap in domestic steel production at maximum level. In 2035, steel production reaches 149 million ton because of increase in electric arc furnace steel. However, it decreases towards 2050 and amounts to 120 million ton, which is almost same as a current level. Minimum steel production is under the scenario which assumes technology progress in steel making and supply-demand balance consideration in each region. Steel production decreases from base year and is 44 million ton in 2050.

Keywords: goods trade scenario, steel making process selection scenario, steel production, global warming

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Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

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Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: classification algorithms, data mining, knowledge discovery, tourism

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Authors: Ayodeji O. Falade, Leonard V. Mabinya, Uchechukwu U. Nwodo, Anthony I. Okoh

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Given the high-utility of peroxidase, its production in large amount is of utmost importance. Over the years, actinomycetes have been the major peroxidase-producing bacteria. Consequently, other classes of bacteria with peroxidase production potentials are underexplored. This study, therefore, sought to enhance peroxidase production by a Raoultella species, a new ligninolytic proteobacteria strain, by determining the optimum culture conditions (initial pH, incubation temperature and agitation speed) for peroxidase production under submerged fermentation using the classical process of one variable at a time and supplementing the fermentation medium with some lignin model and inorganic nitrogen compounds. Subsequently, the time-course assay was carried out under optimized conditions. Then, some agricultural residues were valorized for peroxidase production under solid state fermentation. Peroxidase production was optimal at initial pH 5, incubation temperature of 35 °C and agitation speed of 150 rpm with guaiacol and ammonium chloride as the best inducer and nitrogen supplement respectively. Peroxidase production by the Raoultella species was optimal at 72 h with specific productivity of 16.48 ± 0.89 U mg⁻¹. A simultaneous production of a non-peroxide dependent extracellular enzyme which suggests probable laccase production was observed with specific productivity of 13.63 ± 0.45 U mg⁻¹ while sawdust gave the best peroxidase yield under solid state fermentation. In conclusion, peroxidase production by the Raoultella species was increased by 3.40-fold.

Keywords: enzyme production, ligninolytic bacteria, peroxidase, proteobacteria

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Authors: Megha Gupta, Nupur Prakash

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Identification of plant diseases has been performed using machine learning and deep learning models on the datasets containing images of healthy and diseased plant leaves. The current study carries out an evaluation of some of the deep learning models based on convolutional neural network (CNN) architectures for identification of plant diseases. For this purpose, the publicly available New Plant Diseases Dataset, an augmented version of PlantVillage dataset, available on Kaggle platform, containing 87,900 images has been used. The dataset contained images of 26 diseases of 14 different plants and images of 12 healthy plants. The CNN models selected for the study presented in this paper are AlexNet, ZFNet, VGGNet (four models), GoogLeNet, and ResNet (three models). The selected models are trained using PyTorch, an open-source machine learning library, on Google Colaboratory. A comparative study has been carried out to analyze the high degree of accuracy achieved using these models. The highest test accuracy and F1-score of 99.59% and 0.996, respectively, were achieved by using GoogLeNet with Mini-batch momentum based gradient descent learning algorithm.

Keywords: comparative analysis, convolutional neural networks, deep learning, plant disease identification

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Authors: Seyyedeh Faezeh Hassani Ziabari, Sadegh Eskandari, Maziar Salahi

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Infinite Feature Selection (IFS) algorithm is an efficient feature selection algorithm that selects a subset of features of all sizes (including infinity). In this paper, we present an improved version of it, called clustering IFS (CIFS), by clustering the dataset in advance. To do so, first, we apply the K-means algorithm to cluster the dataset, then we apply IFS. In the CIFS method, the spatial and temporal complexities are reduced compared to the IFS method. Experimental results on 6 datasets show the superiority of CIFS compared to IFS in terms of accuracy, running time, and memory consumption.

Keywords: feature selection, infinite feature selection, clustering, graph

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Authors: Jonjaua Ranogajec, Ognjen Rudic, Snezana Pasalic, Snezana Vucetic, Damir Cjepa

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The paper involves a chain of activities from synthesis, establishment of the methodology for characterization and testing of novel protective materials through the pilot production and application on model supports. It summarizes the results regarding the development of the pilot production protocol for newly developed self-cleaning materials. The optimization of the production parameters was completed in order to improve the most important functional properties (mineralogy characteristics, particle size, self-cleaning properties and photocatalytic activity) of the newly designed nanocomposite material.

Keywords: pilot production, self-cleaning materials, compatibility, cultural heritage

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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

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Authors: H. D. Duong, J. I. Rhee

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In this work, gold nanoparticles in star shape (called gold nanostars, GNS) were synthesized and coated by N-(3-aminopropyl) methacrylamide hydrochloride (PA) and mercaptopropionic acid (MPA) for functionalizing their surface by amine and carboxyl groups and then investigated for ROS production. The GNS with big size and multi-tips seem to be superior in singlet oxygen production as compared with that of small GNS and less tips. However, the functioned GNS in small size could also enhance efficiency of singlet oxygen production about double as compared with that of the intact GNS. In combination with methylene blue (MB+), the functioned GNS could enhance the singlet oxygen production of MB+ after 1h of LED750 irradiation and no difference between small size and big size in this reaction was observed. In combination with 5-aminolevulinic acid (ALA), only GNS coated PA could enhance the singlet oxygen production of ALA and the small size of GNS coated PA was a little higher effect than that of the bigger size. However, GNS coated MPA with small size had strong effect on hydroxyl radical production of ALA.

Keywords: 5-aminolevulinic acid, gold nanostars, methylene blue, ROS production

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Authors: M. Novak, E. Munoz, A. Jana, M. Nelemans

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Cities and local governments the world over are interested to employ circular strategies as a means to bring about food security, create employment and increase resilience. The selection and implementation of circular strategies is facilitated by modeling the effects of strategies locally and understanding the impacts such strategies have had in other (comparable) cities and how that would translate locally. Urban areas are heterogeneous because of their geographic, economic, social characteristics, governance, and culture. In order to better understand the effect of circular strategies on urban systems, we create a dataset for over 300 cities around the world designed to facilitate circular strategy scenario modeling. This new dataset integrates data from over 20 prominent global national and urban data sources, such as the Global Human Settlements layer and International Labour Organisation, as well as incorporating employment data from over 150 cities collected bottom up from local departments and data providers. The dataset is made to be reproducible. Various clustering techniques are explored in the paper. The result is sets of clusters of cities, which can be used for further research, analysis, and support comparative, regional, and national policy making on circular cities.

Keywords: data integration, urban innovation, cluster analysis, circular economy, city profiles, scenario modelling

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Authors: Michael Kenomore, Mohamed Hassan, Amjad Shah, Hom Dhakal

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Shale gas reservoirs have been of greater importance compared to shale oil reservoirs since 2009 and with the current nature of the oil market, understanding the technical and economic performance of shale gas reservoirs is of importance. Using the Barnett shale as a case study, an economic model was developed to quantify the effect of finding and development costs and gas prices on the basins in the Barnett shale using net present value as an evaluation parameter. A rate of return of 20% and a payback period of 60 months or less was used as the investment hurdle in the model. The Barnett was split into four basins (Strawn Basin, Ouachita Folded Belt, Forth-worth Syncline and Bend-arch Basin) with analysis conducted on each of the basin to provide a holistic outlook. The dataset consisted of only horizontal wells that started production from 2008 to at most 2015 with 1835 wells coming from the strawn basin, 137 wells from the Ouachita folded belt, 55 wells from the bend-arch basin and 724 wells from the forth-worth syncline. The data was analyzed initially on Microsoft Excel to determine the estimated ultimate recoverable (EUR). The range of EUR from each basin were loaded in the Palisade Risk software and a log normal distribution typical of Barnett shale wells was fitted to the dataset. Monte Carlo simulation was then carried out over a 1000 iterations to obtain a cumulative distribution plot showing the probabilistic distribution of EUR for each basin. From the cumulative distribution plot, the P10, P50 and P90 EUR values for each basin were used in the economic model. Gas production from an individual well with a EUR similar to the calculated EUR was chosen and rescaled to fit the calculated EUR values for each basin at the respective percentiles i.e. P10, P50 and P90. The rescaled production was entered into the economic model to determine the effect of the finding and development cost and gas price on the net present value (10% discount rate/year) as well as also determine the scenario that satisfied the proposed investment hurdle. The finding and development costs used in this paper (assumed to consist only of the drilling and completion costs) were £1 million, £2 million and £4 million while the gas price was varied from $2/MCF-$13/MCF based on Henry Hub spot prices from 2008-2015. One of the major findings in this study was that wells in the bend-arch basin were least economic, higher gas prices are needed in basins containing non-core counties and 90% of the Barnet shale wells were not economic at all finding and development costs irrespective of the gas price in all the basins. This study helps to determine the percentage of wells that are economic at different range of costs and gas prices, determine the basins that are most economic and the wells that satisfy the investment hurdle.

Keywords: shale gas, Barnett shale, unconventional gas, estimated ultimate recoverable

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Authors: Rhadinia Tayag-Relanes, Felina C. Young

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This study was conducted to analyze the noodle production process at Yan Hu Food Manufacturing for the basis of production improvement. The study utilized the PDCA approach and record review in the gathering of data for the calendar year 2019 from August to October data of the noodle products miki, canton, and misua. Causal-comparative research was used in this study; it attempts to establish cause-effect relationships among the variables such as descriptive statistics and correlation, both were used to compute the data gathered. The study found that miki, canton, and misua production has different cycle time sets for each production and has different production outputs in every set of its production process and a different number of wastages. The company has not yet established its allowable rejection rate/ wastage; instead, this paper used a 1% wastage limit. The researcher recommended the following: machines used for each process of the noodle product must be consistently maintained and monitored; an assessment of all the production operators by checking their performance statistically based on the output and the machine performance; a root cause analysis for finding the solution must be conducted; and an improvement on the recording system of the input and output of the production process of noodle product should be established to eliminate the poor recording of data.

Keywords: production, continuous improvement, process, operations, PDCA

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Authors: Brock Buckingham, Zhe Lin, Wenxuan Guo

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Accurate elevation data is critical in deriving topographic attributes for the precision management of crop inputs, especially water and nutrients. Traditional ground-based elevation data acquisition is time consuming, labor intensive, and often inconvenient at the field scale. Various unmanned aerial systems (UAS) provide the capability of generating digital elevation data from high-resolution images. The objective of this study was to compare the performance of two UAS with different global positioning system (GPS) receivers in determining elevation at the field scale. A DJI Phantom 4 Pro and a DJI Phantom 4 RTK(real-time kinematic) were applied to acquire images at three heights, including 40m, 80m, and 120m above ground. Forty ground control panels were placed in the field, and their geographic coordinates were determined using an RTK GPS survey unit. For each image acquisition using a UAS at a particular height, two elevation datasets were generated using the Pix4D stitching software: a calibrated dataset using the surveyed coordinates of the ground control panels and an uncalibrated dataset without using the surveyed coordinates of the ground control panels. Elevation values for each panel derived from the elevation model of each dataset were compared to the corresponding coordinates of the ground control panels. The coefficient of the determination (R²) and the root mean squared error (RMSE) were used as evaluation metrics to assess the performance of each image acquisition scenario. RMSE values for the uncalibrated elevation dataset were 26.613 m, 31.141 m, and 25.135 m for images acquired at 120 m, 80 m, and 40 m, respectively, using the Phantom 4 Pro UAS. With calibration for the same UAS, the accuracies were significantly improved with RMSE values of 0.161 m, 0.165, and 0.030 m, respectively. The best results showed an RMSE of 0.032 m and an R² of 0.998 for calibrated dataset generated using the Phantom 4 RTK UAS at 40m height. The accuracy of elevation determination decreased as the flight height increased for both UAS, with RMSE values greater than 0.160 m for the datasets acquired at 80 m and 160 m. The results of this study show that calibration with ground control panels improves the accuracy of elevation determination, especially for the UAS with a regular GPS receiver. The Phantom 4 Pro provides accurate elevation data with substantial surveyed ground control panels for the 40 m dataset. The Phantom 4 Pro RTK UAS provides accurate elevation at 40 m without calibration for practical precision agriculture applications. This study provides valuable information on selecting appropriate UAS and flight heights in determining elevation for precision agriculture applications.

Keywords: unmanned aerial system, elevation, precision agriculture, real-time kinematic (RTK)

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Authors: Ulrich Wake, Eniman Syamsuddin

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The model for camera model identificaiton is trained using pretrained model ResNet43 and ResNet50. The dataset consists of 500 photos of each phone. Dataset is divided into 1280 photos for training, 320 photos for validation and 400 photos for testing. The model is trained using One Cycle Policy Method and tested using Test-Time Augmentation. Furthermore, the model is trained for 50 epoch using regularization such as drop out and early stopping. The result is 90% accuracy for validation set and above 85% for Test-Time Augmentation using ResNet50. Every model is also trained by slightly updating the pretrained model’s weights

Keywords: ​ One Cycle Policy, ResNet34, ResNet50, Test-Time Agumentation

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Authors: Insaf Ajili, Malik Mallem, Jean-Yves Didier

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Human motion recognition has been extensively increased in recent years due to its importance in a wide range of applications, such as human-computer interaction, intelligent surveillance, augmented reality, content-based video compression and retrieval, etc. However, it is still regarded as a challenging task especially in realistic scenarios. It can be seen as a general machine learning problem which requires an effective human motion representation and an efficient learning method. In this work, we introduce a descriptor based on Laban Movement Analysis technique, a formal and universal language for human movement, to capture both quantitative and qualitative aspects of movement. We use Discrete Hidden Markov Model (DHMM) for training and classification motions. We improve the classification algorithm by proposing two DHMMs for each motion class to process the motion sequence in two different directions, forward and backward. Such modification allows avoiding the misclassification that can happen when recognizing similar motions. Two experiments are conducted. In the first one, we evaluate our method on a public dataset, the Microsoft Research Cambridge-12 Kinect gesture data set (MSRC-12) which is a widely used dataset for evaluating action/gesture recognition methods. In the second experiment, we build a dataset composed of 10 gestures(Introduce yourself, waving, Dance, move, turn left, turn right, stop, sit down, increase velocity, decrease velocity) performed by 20 persons. The evaluation of the system includes testing the efficiency of our descriptor vector based on LMA with basic DHMM method and comparing the recognition results of the modified DHMM with the original one. Experiment results demonstrate that our method outperforms most of existing methods that used the MSRC-12 dataset, and a near perfect classification rate in our dataset.

Keywords: human motion recognition, motion representation, Laban Movement Analysis, Discrete Hidden Markov Model

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Authors: S. F. Wong, W. I. Ho, B. Lin, Q. Huang

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ERP has been used in many enterprises for management, the accuracy of the production forecasting module is vital to the decision making of the enterprise, and the profit is affected directly. Therefore, enhancing the accuracy of the production forecasting module can also increase the efficiency and profitability. To deal with a lot of data, a suitable, reliable and accurate statistics model is necessary. LSSVM and Grey System are two main models to be studied in this paper, and a case study is used to demonstrate how the combination model is effective to the result of forecasting.

Keywords: ERP, grey system, LSSVM, production forecasting

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Authors: Ajab G. Majidi, Bilal Bingölbali, Adem Akpınar

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This study aims to investigate the amount of energy (economic wave energy potential) that can be obtained from the existing wave energy converters in the high wave energy potential region of the Black Sea in terms of wave energy potential and their performance at different depths in the region. The data needed for this purpose were obtained using the calibrated nested layered SWAN wave modeling program version 41.01AB, which was forced with Climate Forecast System Reanalysis (CFSR) winds from 1979 to 2009. The wave dataset at a time interval of 2 hours was accumulated for a sub-grid domain for around Karaburun beach in Arnavutkoy, a district of Istanbul city. The annual sea state characteristic matrices for the five different depths along with a vertical line to the coastline were calculated for 31 years. According to the power matrices of different wave energy converter systems and characteristic matrices for each possible installation depth, the probability distribution tables of the specified mean wave period or wave energy period and significant wave height were calculated. Then, by using the relationship between these distribution tables, according to the present wave climate, the energy that the wave energy converter systems at each depth can produce was determined. Thus, the economically feasible potential of the relevant coastal zone was revealed, and the effect of different depths on energy converter systems is presented. The Oceantic at 50, 75 and 100 m depths and Oyster at 5 and 25 m depths presents the best performance. In the 31-year long period 1998 the most and 1989 is the least dynamic year.

Keywords: annual power production, Black Sea, efficiency, power production performance, wave energy converter

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Authors: Shamm Kidane, Ibrahim Abdella, Fitsum Gaim, Simon Mulugeta, Sirak Asmerom, Natnael Ambasager, Yoel Ghebrihiwot

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The call for natural language processing (NLP) systems for low-resource languages has become more apparent than ever in the past few years, with the arduous challenges still present in preparing such systems. This paper presents an improved dataset version of the Nagaoka Tigrinya Corpus for Parts-of-Speech (POS) classification system in the Tigrinya language. The size of the initial Nagaoka dataset was incremented, totaling the new tagged corpus to 118K tokens, which comprised the 12 basic POS annotations used previously. The additional content was also annotated manually in a stringent manner, followed similar rules to the former dataset and was formatted in CONLL format. The system made use of the novel approach in NLP tasks and use of the monolingually pre-trained TiELECTRA, TiBERT and TiRoBERTa transformer models. The highest achieved score is an impressive weighted F1-score of 94.2%, which surpassed the previous systems by a significant measure. The system will prove useful in the progress of NLP-related tasks for Tigrinya and similarly related low-resource languages with room for cross-referencing higher-resource languages.

Keywords: Tigrinya POS corpus, TiBERT, TiRoBERTa, conditional random fields

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Authors: Saba Ebrahimi, Hedieh Ashrafi

Abstract:

Predicting the cover type of forests is a challenge for natural resource managers. In this project, we aim to perform a comprehensive comparative study of two well-known classification methods, support vector machine (SVM) and decision tree (DT). The comparison is first performed among different types of each classifier, and then the best of each classifier will be compared by considering different evaluation metrics. The effect of boosting and bagging for decision trees is also explored. Furthermore, the effect of principal component analysis (PCA) and feature selection is also investigated. During the project, the forest cover-type dataset from the remote sensing and GIS program is used in all computations.

Keywords: classification methods, support vector machine, decision tree, forest cover-type dataset

Procedia PDF Downloads 178

Authors: Donglan Zha, Jiansong Si

Abstract:

Because of its flexibility, CES attracts much interest in economic growth and programming models, and the macroeconomics or micro-macro models. This paper focuses on the development, estimating methods of CES production function considering energy as an input. We leave for future research work of relaxing the assumption of constant returns to scale, the introduction of potential input factors, and the generalization method of the optimal nested form of multi-factor production functions.

Keywords: bias of technical change, CES production function, elasticity of substitution, energy input

Procedia PDF Downloads 254

Authors: Hasan Vural

Abstract:

The purpose of this study was to determine energy equivalents of inputs and output in tomato production in Bursa province. The data in this study were collected from tomato farms in Bursa province, Karacabey and Mustafakemalpasa district. Questionnaires were administered through face-to-face interview in 2011-2012. The results of the study show that diesel have the highest rate of energy equivalency of all the inputs used in tomato production at 60,07%. The energy equivalent rate of electricity is 4,26% and the energy equivalent rate of water is 0,87%. The energy equivalent rates for human power, machinery, chemicals and water for irrigation were determined to be low in tomato production. According to the output/input ratio calculated, the energy ratio is 1,50 in tomato production in the research area. This ratio implies that the inputs used in tomato production have not been used effectively. Ineffective use of these resources also causes environmental problems.

Keywords: Tomato production, energy ratio, energy input, Turkey

Procedia PDF Downloads 202

Authors: Shimaa Holail, Tamer Saleh, Xiongwu Xiao

Abstract:

Building change detection (BCD) from satellite imagery is an essential topic in urbanization monitoring, agricultural land management, and updating geospatial databases. Recently, methods for detecting changes based on deep learning have made significant progress and impressive results. However, it has the problem of being insensitive to changes in buildings with complex spectral differences, and the features being extracted are not discriminatory enough, resulting in incomplete buildings and irregular boundaries. To overcome these problems, we propose a dual Siamese network based on the Unet model with the addition of a deep supervision strategy (DS) in this paper. This network consists of a backbone (encoder) based on ImageNet pre-training, a fusion block, and feature pyramid networks (FPN) to enhance the step-by-step information of the changing regions and obtain a more accurate BCD map. To train the proposed method, we created a new dataset (EGY-BCD) of high-resolution and multi-temporal aerial images captured over New Cairo in Egypt to detect building changes for this purpose. The experimental results showed that the proposed method is effective and performs well with the EGY-BCD dataset regarding the overall accuracy, F1-score, and mIoU, which were 91.6 %, 80.1 %, and 73.5 %, respectively.

Keywords: building change detection, deep supervision, semantic segmentation, EGY-BCD dataset

Procedia PDF Downloads 74

Authors: Harshan Baskar, Anirudh S. Chakravarthy, Prateek Garg, Divyam Goel, Abhijith S. Raj, Kshitij Kumar, Lakshya, Ravichandra Parvatham, V. Sushant, Bijay Kumar Rout

Abstract:

In this paper, we introduce a new computer vision task called nighttime dehaze-enhancement. This task aims to jointly perform dehazing and lightness enhancement. Our task fundamentally differs from nighttime dehazing – our goal is to jointly dehaze and enhance scenes, while nighttime dehazing aims to dehaze scenes under a nighttime setting. In order to facilitate further research on this task, we release a new benchmark dataset called Reside-β Night dataset, consisting of 4122 nighttime hazed images from 2061 scenes and 2061 ground truth images. Moreover, we also propose a new network called NDENet (Nighttime Dehaze-Enhancement Network), which jointly performs dehazing and low-light enhancement in an end-to-end manner. We evaluate our method on the proposed benchmark and achieve SSIM of 0.8962 and PSNR of 26.25. We also compare our network with other baseline networks on our benchmark to demonstrate the effectiveness of our approach. We believe that nighttime dehaze-enhancement is an essential task, particularly for autonomous navigation applications, and we hope that our work will open up new frontiers in research. Our dataset and code will be made publicly available upon acceptance of our paper.

Keywords: dehazing, image enhancement, nighttime, computer vision

Procedia PDF Downloads 118

Authors: Ivan Ramos Sampe Immanuel, Linung Kresno Adikusumo, Liston Sitanggang

Abstract:

Achieving the production targets of oil and gas in an upstream oil and gas company represents a complex undertaking necessitating collaborative engagement from a multidisciplinary team. In addition to conducting exploration activities and executing well intervention programs, an upstream oil and gas enterprise must assess the feasibility of attaining predetermined production goals. The monitoring of production performance serves as a critical activity to ensure organizational progress towards the established oil and gas performance targets. Subsequently, decisions within the upstream oil and gas management team are informed by the received information pertaining to the respective production performance. To augment the decision-making process, the implementation of a production performance dashboard emerges as a viable solution, providing an integrated and centralized tool. The deployment of a production performance dashboard manifests as an instrumental mechanism fostering a user-friendly interface for monitoring production performance, while concurrently preserving the intrinsic characteristics of granular data. The integration of diverse data sources into a unified production performance dashboard establishes a singular veritable source, thereby enhancing the organization's capacity to uphold a consolidated and authoritative foundation for its business requisites. Additionally, the heightened accessibility of the production performance dashboard to business users constitutes a compelling substantiation of its consequential impact on facilitating the monitoring of organizational targets.

Keywords: production, performance, dashboard, data analytics

Procedia PDF Downloads 33