Search results for: machine readable format

Authors: Ghada S. Moussa

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Vehicle classification is an important task in traffic surveillance and intelligent transportation systems. Classification of vehicle images is facing several problems such as: high intra-class vehicle variations, occlusion, shadow, illumination. These problems and others must be considered to develop a reliable vehicle classification system. In this study, a reliable multi-type vehicle classification system based on Bag-of-Words (BoW) paradigm is developed. Our proposed system used and compared four well-known classifiers; Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-Nearest Neighbour (KNN), and Decision Tree to classify vehicles into four categories: motorcycles, small, medium and large. Experiments on a large dataset show that our approach is efficient and reliable in classifying vehicles with accuracy of 95.7%. The SVM outperforms other classification algorithms in terms of both accuracy and robustness alongside considerable reduction in execution time. The innovativeness of developed system is it can serve as a framework for many vehicle classification systems.

Keywords: vehicle classification, bag-of-words technique, SVM classifier, LDA classifier, KNN classifier, decision tree classifier, SIFT algorithm

Procedia PDF Downloads 347

Authors: Joo Min Kim, Dongyoun Shin

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The project Future Station (FS) seek for a deeper understanding of metro station. The main idea of the project is enhancing the underground environment by combining new architectural design with IoT technology. This research shows the understanding of the metro environment giving references regarding traditional design approaches and IoT combined space design. Based on the analysis, this research presents design alternatives in two metro stations those are chosen for a testbed. It also presents how the FS platform giving a response to travelers and deliver the benefit to metro operators. In conclusion, the project describes methods to build future metro service and platform that understand traveler’s intentions and giving appropriate services back for enhancing travel experience. It basically used contemporary technology such as smart sensing grid, big data analysis, smart building, and machine learning technology.

Keywords: future station, digital lifestyle experience, sustainable metro, smart metro, smart city

Procedia PDF Downloads 291

Authors: Rajveer, Abhinav Saxena, Sanjeev Das

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The study is focused on the strength of forged aluminum alloy (AA) 6082 (T6). Aluminum alloy 6082 belongs to Al-Mg-Si family which has a wide range of automotive applications. A decrease in the strength of AA 6082 alloy was observed after T6 treatment. The as-received (extruded), forged, and forged + heat treated samples were examined to understand the reason. These examinations were accomplished by optical (OM) and scanning electron microscope (SEM) and X-ray diffraction (XRD) studies. It was observed that the defects had an insignificant effect on the alloy strength. The alloy samples were subjected to age hardening treatment and the time to achieve peak hardening was acquired. Standard tensile specimens were prepared from as-received (extruded), forged, forged + solutionized and forged + solutionized + age hardened. Tensile tests were conducted by Instron universal testing machine. It was observed that there was a significant drop in tensile strength in the case of solutionized sample. The detailed study of the fracture samples showed that the solutionizing after forging was not the best way to increase the strength of Al 6082 alloy.

Keywords: aluminum alloy 6082, strength, forging, age hardening

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Authors: Ching Yern Chee

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Various loading of nano silica is added into polyurethane (PU) and then coated on porcelain substrate. The wear and friction properties of the porcelain substrates coated with polyurethane/nano silica nano composite coatings were investigated using the reciprocating wear testing machine. The friction and wear test of polyurethane/nano silica coated porcelain substrate was studied at different sliding speed and applied load. It was found that the optimum composition of nano silica is 3 wt% which gives the lowest friction coefficient and wear rate in all applied load ranges and sliding speeds. For 3 wt% nano silica filled PU coated porcelain substrate, the increment of sliding speed caused higher wear rates but lower frictions coefficient. Besides, the friction coefficient of nano silica filled PU coated porcelain substrate decreased but the wear rate increased with the applied load.

Keywords: porcelain, nanocomposite coating, morphology, friction, wear behavior

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Authors: Angela Luft, Sebastian Bremen, Nicolae Balc

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Additive manufacturing processes have entered large parts of the industry and their range of application have progressed and grown significantly in the course of time. A major advantage of additive manufacturing is the innate flexibility of the machines. This corelates with the ongoing demand of creating highly flexible production environments. However, the potential of additive manufacturing technologies to enhance the flexibility of production systems has not yet been truly considered and quantified in a systematic way. In order to determine the potential of additive manufacturing technologies with regards to the strategic flexibility design in production systems, an integrated evaluation model has been developed, that allows for the simultaneous consideration of both conventional as well as additive production resources. With the described model, an operational scope of action can be identified and quantified in terms of mix and volume flexibility, process complexity, and machine capacity that goes beyond the current cost-oriented approaches and offers a much broader and more holistic view on the potential of additive manufacturing. A respective evaluation model is presented this paper.

Keywords: additive manufacturing, capacity planning, production systems, strategic production planning, flexibility enhancement

Procedia PDF Downloads 144

Authors: Yun-Xuan Tang, Pei-Yuan Liu, Kun-Mu Lu, Min-Tsung Tseng, Liang-Kuang Chen, Yuh-Feng Tsai, Ching-Wen Lee, Jay Wu

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Breast cancer is predominant of malignant tumors in females. The increase in the glandular density increases the risk of breast cancer. BI-RADS is a frequently used density indicator in mammography; however, it significantly overestimates the glandularity. Therefore, it is very important to accurately and quantitatively assess the glandularity by mammography. In this study, 20%, 30% and 50% glandularity phantoms were exposed using a mammography machine at 28, 30 and 31 kVp, and 30, 55, 80 and 105 mAs, respectively. The regions of interest (ROIs) were drawn to assess the gray level. The relationship between the glandularity and gray level under various compression thicknesses, kVp, and mAs was established by the multivariable linear regression. A phantom verification was performed with automatic exposure control (AEC). The regression equation was obtained with an R-square value of 0.928. The average gray levels of the verification phantom were 8708, 8660 and 8434 for 0.952, 0.963 and 0.985 g/cm3, respectively. The percent differences of glandularity to the regression equation were 3.24%, 2.75% and 13.7%. We concluded that the proposed method could be clinically applied in mammography to improve the glandularity estimation and further increase the importance of breast cancer screening.

Keywords: mammography, glandularity, gray value, BI-RADS

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Authors: Philip Symonds, Jon Taylor, Zaid Chalabi, Michael Davies

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With the world climate projected to warm and major cities in developing countries becoming increasingly populated and polluted, governments are tasked with the problem of overheating and air quality in residential buildings. This paper presents the development of an adaptable model of these risks. Simulations are performed using the EnergyPlus building physics software. An accurate metamodel is formed by randomly sampling building input parameters and training on the outputs of EnergyPlus simulations. Metamodels are used to vastly reduce the amount of computation time required when performing optimisation and sensitivity analyses. Neural Networks (NNs) are compared to a Radial Basis Function (RBF) algorithm when forming a metamodel. These techniques were implemented using the PyBrain and scikit-learn python libraries, respectively. NNs are shown to perform around 15% better than RBFs when estimating overheating and air pollution metrics modelled by EnergyPlus.

Keywords: neural networks, radial basis functions, metamodelling, python machine learning libraries

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Authors: Marcin Michalak

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Due to the increasing efforts on saving our natural environment a change in the structure of energy resources can be observed - an increasing fraction of a renewable energy sources. In many countries traditional underground coal mining loses its significance but there are still countries, like Poland or Germany, in which the coal based technologies have the greatest fraction in a total energy production. This necessitates to make an effort to limit the costs and negative effects of underground coal mining. The longwall complex is as essential part of the underground coal mining. The safety and the effectiveness of the work is strongly dependent of the diagnostic state of powered roof supports. The building of a useful and reliable diagnostic system requires a lot of data. As the acquisition of a data of any possible operating conditions it is important to have a possibility to generate a demanded artificial working characteristics. In this paper a new approach of modelling a leg pressure in the single unit of powered roof support. The model is a result of the analysis of a typical working cycles.

Keywords: machine modelling, underground mining, coal mining, structure

Procedia PDF Downloads 357

Authors: Anderson Rocha, Pedro Miguel de Figueiredo Dinis Oliveira Gaspar

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Machine vision has been widely used in recent years in agriculture, as a tool to promote the automation of processes and increase the levels of productivity. The aim of this work is the development of a path recognition algorithm based on image processing to guide a terrestrial robot in-between tree rows. The proposed algorithm was developed using the software MATLAB, and it uses several image processing operations, such as threshold detection, morphological erosion, histogram equalization and the Hough transform, to find edge lines along tree rows on an image and to create a path to be followed by a mobile robot. To develop the algorithm, a set of images of different types of orchards was used, which made possible the construction of a method capable of identifying paths between trees of different heights and aspects. The algorithm was evaluated using several images with different characteristics of quality and the results showed that the proposed method can successfully detect a path in different types of environments.

Keywords: agricultural mobile robot, image processing, path recognition, hough transform

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Authors: Rodrigo Arnaldo Scarpel

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Trend following is a widely accepted investment strategy that adopts a rule-based trading mechanism that rather than striving to predict market direction or on information gathering to decide when to buy and when to sell a stock. Thus, in trend following one must respond to market’s movements that has recently happen and what is currently happening, rather than on what will happen. Optimally, in trend following strategy, is to catch a bull market at its early stage, ride the trend, and liquidate the position at the first evidence of the subsequent bear market. For applying the trend following strategy one needs to find the trend and identify trade signals. In order to avoid false signals, i.e., identify fluctuations of short, mid and long terms and to separate noise from real changes in the trend, most academic works rely on moving averages and other technical analysis indicators, such as the moving average convergence divergence (MACD) and the relative strength index (RSI) to uncover intelligible stock trading rules following trend following strategy philosophy. Recently, some works has applied machine learning techniques for trade rules discovery. In those works, the process of rule construction is based on evolutionary learning which aims to adapt the rules to the current environment and searches for the global optimum rules in the search space. In this work, instead of focusing on the usage of machine learning techniques for creating trading rules, a time series trend classification employing a semi-supervised approach was used to early identify both the beginning and the end of upward and downward trends. Such classification model can be employed to identify trade signals and the decision-making procedure is that if an up-trend (down-trend) is identified, a buy (sell) signal is generated. Semi-supervised learning is used for model training when only part of the data is labeled and Semi-supervised classification aims to train a classifier from both the labeled and unlabeled data, such that it is better than the supervised classifier trained only on the labeled data. For illustrating the proposed approach, it was employed daily trade information, including the open, high, low and closing values and volume from January 1, 2000 to December 31, 2022, of the São Paulo Exchange Composite index (IBOVESPA). Through this time period it was visually identified consistent changes in price, upwards or downwards, for assigning labels and leaving the rest of the days (when there is not a consistent change in price) unlabeled. For training the classification model, a pseudo-label semi-supervised learning strategy was used employing different technical analysis indicators. In this learning strategy, the core is to use unlabeled data to generate a pseudo-label for supervised training. For evaluating the achieved results, it was considered the annualized return and excess return, the Sortino and the Sharpe indicators. Through the evaluated time period, the obtained results were very consistent and can be considered promising for generating the intended trading signals.

Keywords: evolutionary learning, semi-supervised classification, time series data, trading signals generation

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Authors: Jan Schmitt, Sophie Fischer

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After the Corona pandemic, climate change is a further, long-lasting challenge the society must deal with. An ongoing climate change need to be prevented. Nevertheless, the adoption tothe already changed climate conditionshas to be focused in many sectors. Recently, the decisive role of the economic sector with high value added can be seen in the Corona crisis. Hence, manufacturing industry as such a sector, needs to be prepared for climate change and adaption. Several examples from the manufacturing industry show the importance of a strategic effort in this field: The outsourcing of a major parts of the value chain to suppliers in other countries and optimizing procurement logistics in a time-, storage- and cost-efficient manner within a network of global value creation, can lead vulnerable impacts due to climate-related disruptions. E.g. the total damage costs after the 2011 flood disaster in Thailand, including costs for delivery failures, were estimated at 45 billion US dollars worldwide. German car manufacturers were also affected by supply bottlenecks andhave close its plant in Thailand for a short time. Another OEM must reduce the production output. In this contribution, a game-based learning approach is presented, which should enable manufacturing companies to derive their own strategies for climate adaption out of a mix of different actions. Based on data from a regional study of small, medium and large manufacturing companies in Mainfranken, a strongly industrialized region of northern Bavaria (Germany) the game-based learning approach is designed. Out of this, the actual state of efforts due to climate adaption is evaluated. First, the results are used to collect single actions for manufacturing companies and second, further actions can be identified. Then, a variety of climate adaption activities can be clustered according to the scope of activity of the company. The combination of different actions e.g. the renewal of the building envelope with regard to thermal insulation, its benefits and drawbacks leads to a specific strategy for climate adaption for each company. Within the game-based approach, the players take on different roles in a fictionalcompany and discuss the order and the characteristics of each action taken into their climate adaption strategy. Different indicators such as economic, ecologic and stakeholder satisfaction compare the success of the respective measures in a competitive format with other virtual companies deriving their own strategy. A "play through" climate change scenarios with targeted adaptation actions illustrate the impact of different actions and their combination onthefictional company.

Keywords: business strategy, climate change, climate adaption, game-based learning

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Authors: Cledinaldo A. Dias, Isabela C. Santos, Marcus V. S. Siqueira

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In the contemporary globalization, the competitiveness generated in the search of new markets aiming at the growth of productivity and, consequently, of profits, implies the redefinition of productive processes and new forms of work organization. As a result of this structuring, unemployment, labor force turnover and the increase in outsourcing and informal work occur. Considering the different relationships and working conditions of outsourced employees, this study aims to identify the most present stressors among outsourced service providers from a Federal Institution of Higher Education in Brazil. To reach this objective, a descriptive exploratory study with a quantitative approach was carried out. The qualitative approach was chosen to provide an in-depth analysis of the occupational conditions of outsourced workers since this method seeks to focus on the social as a world of investigated meanings and the language or speech of each subject as the object of this approach. The survey was conducted in the city of Montes Claros - Minas Gerais (Brazil) and involved eighty workers from companies hired by the institution, including armed security guards, porters, cleaners, drivers, gardeners, and administrative assistants. The choice of professionals obeyed non-probabilistic criteria for convenience or accessibility. Data collection was performed by means of a structured questionnaire composed of sixty questions, in a Likert-type frequency interval scale format, in order to identify potential organizational stressors. The results obtained evidence that the stress factors pointed out by the workers are, in most cases, a determining factor due to the low productive performance at work. Amongst the factors associated with stress, the ones that stood out most were those related to organizational communication failures, the incentive to competition, lack of expectations of professional growth, insecurity and job instability. Based on the results, the need for greater concern and organizational responsibility with the well-being and mental health of the outsourced worker and the recognition of their physical and psychological limitations, and care that goes beyond the functional capacity for the work. Specifically for the preservation of mental health, physical and quality of life, it is concluded that it is necessary for the professional to be inserted in the external world that favors it internally since this set is complemented so that the individual remains in balance and obtain satisfaction in your work.

Keywords: occupational health, outsourced, organizational studies, stressors

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Authors: Sujay Kotwale, Ramasubba Reddy M.

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Electrocardiogram (ECG) is a non-invasive technique used to study and analyze various heart diseases. Cardiac arrhythmia is a serious heart disease which leads to death of the patients, when left untreated. An early-time detection of cardiac arrhythmia would help the doctors to do proper treatment of the heart. In the past, various algorithms and machine learning (ML) models were used to early-time detection of cardiac arrhythmia, but few of them have achieved better results. In order to improve the performance, this paper implements principal component analysis (PCA) along with XGBoost model. The PCA was implemented to the raw ECG signals which suppress redundancy information and extracted significant features. The obtained significant ECG features were fed into XGBoost model and the performance of the model was evaluated. In order to valid the proposed technique, raw ECG signals obtained from standard MIT-BIH database were employed for the analysis. The result shows that the performance of proposed method is superior to the several state-of-the-arts techniques.

Keywords: cardiac arrhythmia, electrocardiogram, principal component analysis, XGBoost

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Authors: Gueidi Afef, Gharsellaoui Hamza, Ben Ahmed Samir

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This paper deals with the secret of the continual progression data that new data management solutions have been emerged: The NoSQL databases. They crossed several areas like personalization, profile management, big data in real-time, content management, catalog, view of customers, mobile applications, internet of things, digital communication and fraud detection. Nowadays, these database management systems are increasing. These systems store data very well and with the trend of big data, a new challenge’s store demands new structures and methods for managing enterprise data. The new intelligent machine in the e-learning sector, thrives on more data, so smart machines can learn more and faster. The robotics are our use case to focus on our test. The implementation of NoSQL for Robotics wrestle all the data they acquire into usable form because with the ordinary type of robotics; we are facing very big limits to manage and find the exact information in real-time. Our original proposed approach was demonstrated by experimental studies and running example used as a use case.

Keywords: NoSQL databases, database management systems, robotics, big data

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Authors: Derek Brandon G. Yu, Clyde Vincent O. Pilapil, Christine F. Peña

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College students of Cebu province have been indoors since March 2020, and a challenge encountered is the sudden shift from face to face to online learning and with the lack of empirical data on online learning on Higher Education Institutions (HEIs) in the Philippines. Sentiments on face to face and online learning will be collected from University of San Carlos (USC), School of Arts and Sciences (SAS) students regarding Mathematics in the Modern World (MMW), a General Education (GE) course. Natural Language Processing with machine learning algorithms will be used to classify the sentiments of the students. Results of the research study are the themes identified through topic modelling and the overall sentiments of the students in USC SAS

Keywords: natural language processing, online learning, sentiment analysis, topic modelling

Procedia PDF Downloads 231

Authors: Mohamed Fadzlee Sulaiman, Mohd Zabri Adil Talib, Aswami Fadillah Mohd Ariffin

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Each individual organization has their own mechanism to build up cyber defense capability in protecting their information infrastructures from data breaches and cyber espionage. But, we can not deny the possibility of failing to detect and stop cyber attacks especially for those targeting credential information and intellectual property (IP). In this paper, we would like to share the modern approach of effective digital forensic methodology in order to identify the artifacts in tracing the trails of evidence while mitigating the infection from the target machine/s. This proposed approach will suit the digital forensic investigation to be conducted while resuming the business critical operation after mitigating the infection and minimizing the risk from the identified attack to transpire. Therefore, traditional digital forensics methodology has to be improvised to be proactive which not only focusing to discover the root caused and the threat actor but to develop the relevant mitigation plan in order to prevent from the same attack.

Keywords: digital forensic, detection, eradication, targeted attack, malware

Procedia PDF Downloads 265

Authors: Tamadu Perry Egedegu

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Rapid growth in the use of electronic data has affected both the employee and work place. Our experience shows that jobs that have multiple risk factors have a greater likelihood of causing Work Related Musculoskeletal Disorder (WRMSDs), depending on the duration, frequency and/or magnitude of exposure to each. The study investigated musculoskeletal disorder among office workers. Thus, it is important that ergonomic risk factors be considered in light of their combined effect in causing or contributing to WRMSDs. Fast technological growth in the use of electronic system; have affected both workers and the work environment. Awkward posture and long hours in front of these visual display terminals can result in work-related musculoskeletal disorders (WRMSD). The study shall contribute to the awareness creation on the causes and consequences of WRMSDs due to lack of ergonomics training. The study was conducted using an observational cross-sectional design. A sample of 109 respondents was drawn from the target population through purposive sampling method. The sources of data were both primary and secondary. Primary data were collected through questionnaires and secondary data were sourced from journals, textbooks, and internet materials. Questionnaires were the main instrument for data collection and were designed in a YES or NO format according to the study objectives. Content validity approval was used to ensure that the variables were adequately covered. The reliability of the instrument was done through test-retest method, yielding a reliability index at 0.84. The data collected from the field were analyzed with a descriptive statistics of chart, percentage and mean. The study found that the most affected body regions were the upper back, followed by the lower back, neck, wrist, shoulder and eyes, while the least affected body parts were the knee calf and the ankle. Furthermore, the prevalence of work-related 'musculoskeletal' malfunctioning was linked with long working hours (6 - 8 hrs.) per day, lack of back support on their seats, glare on the monitor, inadequate regular break, repetitive motion of the upper limbs, and wrist when using the computer. Finally, based on these findings some recommendations were made to reduce the prevalent of WRMSDs among office workers.

Keywords: work related musculoskeletal disorder, Nigeria, office computer users, ergonomic risk factor

Procedia PDF Downloads 229

Authors: Sandra So, Mohd Roslan Ismail, Safurah Jaafar

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With the rapid proliferation of data in healthcare has provided an opportune platform creation of Artificial Intelligence (AI). AI has brought a paradigm shift for healthcare professionals, promising improvement in delivery and quality. This study aims to determine the perception of healthcare personnel on perceived ease of use, perceived usefulness, and subjective norm toward attitude for artificial intelligence acceptance. A cross-sectional single institutional study of employees’ perception of adopting AI in the hospital was conducted. The survey was conducted using a questionnaire adapted from Technology Acceptance Model and a four-point Likert scale was used. There were 96 or 75.5% of the total population responded. This study has shown the significant relationship and the importance of ease of use, perceived usefulness, and subjective norm to the acceptance of AI. In the study results, it concluded that the determining factor to the strong acceptance of AI in their practices is mostly those respondents with the most interaction with the patients and clinical management.

Keywords: artificial intelligence, machine learning, perceived ease of use, perceived usefulness, subjective norm

Procedia PDF Downloads 218

Authors: Pemika Ketsuwan, Pitt Supaphol, Manit Nithitanakul

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A typical used of ethylene methyl acrylate (EMA) gasket is in the manufacture of optical lens, and often, they are deteriorated rapidly due to high temperature during the process. The objective of this project is to improve the thermal stability of the EMA copolymer gasket by preparing EMA with cellulose and silica composites. Hydroxy propyl methyl cellulose (HPMC) and Carboxy methyl cellulose (CMC) were used in preparing of EMA/cellulose composites and fumed silica (SiO2) was used in preparing EMA/silica composites with different amounts of filler (3, 5, 7, 10, 15 wt.%), using a twin screw extruder at 160 °C and the test specimens were prepared by the injection molding machine. The morphology and dispersion of fillers in the EMA matrix were investigated by field emission scanning electron microscopy (FESEM). The thermal stability of the composite was determined by thermal gravimetric analysis (TGA), and differential scanning calorimeter (DSC). Mechanical properties were evaluated by tensile testing. The developed composites were found to enhance thermal and mechanical properties when compared to that of the EMA copolymer alone.

Keywords: ethylene methyl acrylate, HPMC, Silica, Thermal stability

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Authors: Christoph Linse, Thomas Martinetz

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Recent findings have shown that Neural Networks generalize also in over-parametrized regimes with zero training error. This is surprising, since it is completely against traditional machine learning wisdom. In our empirical study we fortify these findings in the domain of fine-grained image classification. We show that very large Convolutional Neural Networks with millions of weights do learn with only a handful of training samples and without image augmentation, explicit regularization or pretraining. We train the architectures ResNet018, ResNet101 and VGG19 on subsets of the difficult benchmark datasets Caltech101, CUB_200_2011, FGVCAircraft, Flowers102 and StanfordCars with 100 classes and more, perform a comprehensive comparative study and draw implications for the practical application of CNNs. Finally, we show that VGG19 with 140 million weights learns to distinguish airplanes and motorbikes with up to 95% accuracy using only 20 training samples per class.

Keywords: convolutional neural networks, fine-grained image classification, generalization, image recognition, over-parameterized, small data sets

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Authors: Anita Yaghobi, Homayoun Ebrahimian

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One of the important tools that can help orthopedic doctors for diagnose diseases is imaging scan. Imaging techniques can help physicians in see different parts of the body, including the bones, muscles, tendons, nerves, and cartilage. During CT scan, a patient must be in the same position from the start to the end of radiation treatment. Patient movements are usually monitored by the technologists through the closed circuit television (CCTV) during scan. If the patient makes a small movement, it is difficult to be noticed by them. In the present work, a simple patient movement monitoring device is fabricated to monitor the patient movement. It uses an electronic sensing device. It continuously monitors the patient’s position while the CT scan is in process. The device has been retrospectively tested on 51 patients whose movement and distance were measured. The results show that 25 patients moved 1 cm to 2.5 cm from their initial position during the CT scan. Hence, the device can potentially be used to control and monitor patient movement during CT scan and Radiography. In addition, an audible alarm situated at the control panel of the control room is provided with this device to alert the technologists. It is an inexpensive, compact device which can be used in any CT scan machine.

Keywords: CT scan, radiology, X Ray, unwanted movement

Procedia PDF Downloads 452

Authors: Kamyar Tolouei, Ehsan Moosavi

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In open-pit mines, the long-term production scheduling optimization problem (LTPSOP) is a complicated problem that contains constraints, large datasets, and uncertainties. Uncertainty in the output is caused by several geological, economic, or technical factors. Due to its dimensions and NP-hard nature, it is usually difficult to find an ideal solution to the LTPSOP. The optimal schedule generally restricts the ore, metal, and waste tonnages, average grades, and cash flows of each period. Past decades have witnessed important measurements of long-term production scheduling and optimal algorithms since researchers have become highly cognizant of the issue. In fact, it is not possible to consider LTPSOP as a well-solved problem. Traditional production scheduling methods in open-pit mines apply an estimated orebody model to produce optimal schedules. The smoothing result of some geostatistical estimation procedures causes most of the mine schedules and production predictions to be unrealistic and imperfect. With the expansion of simulation procedures, the risks from grade uncertainty in ore reserves can be evaluated and organized through a set of equally probable orebody realizations. In this paper, to synthesize grade uncertainty into the strategic mine schedule, a stochastic integer programming framework is presented to LTPSOP. The objective function of the model is to maximize the net present value and minimize the risk of deviation from the production targets considering grade uncertainty simultaneously while satisfying all technical constraints and operational requirements. Instead of applying one estimated orebody model as input to optimize the production schedule, a set of equally probable orebody realizations are applied to synthesize grade uncertainty in the strategic mine schedule and to produce a more profitable and risk-based production schedule. A mixture of metaheuristic procedures and mathematical methods paves the way to achieve an appropriate solution. This paper introduced a hybrid model between the augmented Lagrangian relaxation (ALR) method and the metaheuristic algorithm, the Harris Hawks optimization (HHO), to solve the LTPSOP under grade uncertainty conditions. In this study, the HHO is experienced to update Lagrange coefficients. Besides, a machine learning method called Random Forest is applied to estimate gold grade in a mineral deposit. The Monte Carlo method is used as the simulation method with 20 realizations. The results specify that the progressive versions have been considerably developed in comparison with the traditional methods. The outcomes were also compared with the ALR-genetic algorithm and ALR-sub-gradient. To indicate the applicability of the model, a case study on an open-pit gold mining operation is implemented. The framework displays the capability to minimize risk and improvement in the expected net present value and financial profitability for LTPSOP. The framework could control geological risk more effectively than the traditional procedure considering grade uncertainty in the hybrid model framework.

Keywords: grade uncertainty, metaheuristic algorithms, open-pit mine, production scheduling optimization

Procedia PDF Downloads 93

Authors: D.Mendes, J. Henriques, P. Carvalho, T. Rocha, S. Paredes, R. Cabiddu, R. Trimer, R. Mendes, A. Borghi-Silva, L. Kaminsky, E. Ashley, R. Arena, J. Myers

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The continued exploration of clinically relevant predictive models continues to be an important pursuit. Cardiorespiratory fitness (CRF) portends clinical vital information and as such its accurate prediction is of high importance. Therefore, the aim of the current study was to develop a data-driven model, based on computational intelligence techniques and, in particular, clustering approaches, to predict CRF. Two prediction models were implemented and compared: 1) the traditional Wasserman/Hansen Equations; and 2) an interpretable clustering approach. Data used for this analysis were from the 'FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base'; in the present study a subset of 10690 apparently healthy individuals were utilized. The accuracy of the models was performed through the computation of sensitivity, specificity, and geometric mean values. The results show the superiority of the clustering approach in the accurate estimation of CRF (i.e., maximal oxygen consumption).

Keywords: cardiorespiratory fitness, data-driven models, knowledge extraction, machine learning

Procedia PDF Downloads 277

Authors: Perminder JitKaur, Santosh Satya, K. K. Pant, S. N. Naik

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Bamboo is extensively used in construction industry. Low durability of bamboo due to fungus infestation and termites attack under storage puts certain constrains for it usage as modern structural material. Looking at many chemical formulations for bamboo treatment leading to severe harmful environment effects, research on eco-friendly preservatives for bamboo treatment has been initiated world-over. In the present studies, eco-friendly preservative for bamboo treatment has been developed. To validate its application for structural purposes, investigation of effect of treatment on compressive strength has been investigated. Neem oil(25%) integrated with copper naphthenate (0.3%) on dilution with kerosene oil impregnated into bamboo culm at 2 bar pressure, has shown weight loss of only 3.15% in soil block analysis method. The results of compressive strength analysis using The results from compressive strength analysis using HEICO Automatic Compression Testing Machine, reveal that preservative treatment has not altered the structural properties of bamboo culms. Compressive strength of control (11.72 N/mm2) and above treated samples (11.71 N/mm2) was found to be comparable.

Keywords: D. strictus, bamboo, neem oil, presure treatment, compressive strength

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Authors: Xiuying Wang, Changliang Li, Bo Xu

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A fixed-length feature vector is required for many machine learning algorithms in NLP field. Word embeddings have been very successful at learning lexical information. However, they cannot capture the compositional meaning of sentences, which prevents them from a deeper understanding of language. In this paper, we introduce a novel hierarchical tree long short-term memory (HTLSTM) model that learns vector representations for sentences of arbitrary syntactic type and length. We propose to split one sentence into three hierarchies: short phrase, long phrase and full sentence level. The HTLSTM model gives our algorithm the potential to fully consider the hierarchical information and long-term dependencies of language. We design the experiments on both English and Chinese corpus to evaluate our model on sentiment analysis task. And the results show that our model outperforms several existing state of the art approaches significantly.

Keywords: deep learning, hierarchical tree long short-term memory, sentence representation, sentiment analysis

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Authors: Ahmad Bilal

Abstract:

Digital and convergent media can be helpful in terms of acquiring film production skills and knowledge, and it has also reduced the cost of production. Thus, allowing filmmakers greater opportunities and access to the medium of film. Both these dimensions of new and convergent media have been challenging the established cinema of Pakistan, as traditionally, it has been controlled by the authorities through censorship policies. The use of the smartphone as a movie camera, editing machine, and a transmitter can further challenge the control in a postcolonial society. To explore the impact of new and convergent media on the art of filmmaking, a film 'Sohni Dharti: An untrue story' is produced. It is shot both on a smartphone and a Digital Single Lens Reflex Camera (DSLR), with almost zero budgets. It is distributed through Vimeo from Pakistan. This process reveals how the technologies that are available today, and the increased knowledge of film production that they bring, allow a more inclusive experience of the film production and distribution. At the same time, however, it also discloses the limitations that accompany new technologies within the context of a postcolonial society. This paper will investigate the role of technology to bring filmmaking at a level of pencil and paper.

Keywords: convergent media, filmmaking, smartphone, Pakistan

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Authors: Aysegul Sarac

Abstract:

The aim of this study is to analyze the impacts of integrating agile development principles and practices, in particular to reduce project lead time in a multi-project environment. We analyze Arçelik Washing Machine R&D Center in which multiple projects are conducted by shared resources. In the first part of the study, we illustrate the current waterfall model system by using a value stream map. We define all activities starting from the first idea of the project to the customer and measure process time and lead time of projects. In the second part of the study we estimate potential improvements and select a set of these improvements to integrate agile principles. We aim to develop a future state map and analyze the impacts of integrating lean principles on project lead time. The main contribution of this study is that we analyze and integrate agile product development principles in a real multi-project system.

Keywords: agile project management, multi project system, project lead time, product development

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Authors: Dhanuj M. Gandikota

Abstract:

Sensor-derived three-dimensional (3D) point clouds of trees are invaluable in remote sensing analysis for the accurate measurement of key structural metrics, bio-inventory values, spatial planning/visualization, and ecological modeling. Machine learning (ML) holds the potential in addressing the restrictive tradeoffs in cost, spatial coverage, resolution, and information gain that exist in current point cloud sensing methods. Terrestrial laser scanning (TLS) remains the highest fidelity source of both canopy and below-canopy structural features, but usage is limited in both coverage and cost, requiring manual deployment to map out large, forested areas. While aerial laser scanning (ALS) remains a reliable avenue of LIDAR active remote sensing, ALS is also cost-restrictive in deployment methods. Space-borne photogrammetry from high-resolution satellite constellations is an avenue of passive remote sensing with promising viability in research for the accurate construction of vegetation 3-D point clouds. It provides both the lowest comparative cost and the largest spatial coverage across remote sensing methods. However, both space-borne photogrammetry and ALS demonstrate technical limitations in the capture of valuable below-canopy point cloud data. Looking to minimize these tradeoffs, we explored a class of powerful ML algorithms called Deep Learning (DL) that show promise in recent research on 3-D point cloud reconstruction and interpolation. Our research details the efficacy of applying these DL techniques to reconstruct accurate below-canopy point clouds from space-borne and aerial remote sensing through learned patterns of tree species fractal symmetry properties and the supplementation of locally sourced bio-inventory metrics. From our dataset, consisting of tree point clouds obtained from TLS, we deconstructed the point clouds of each tree into those that would be obtained through ALS and satellite photogrammetry of varying resolutions. We fed this ALS/satellite point cloud dataset, along with the simulated local bio-inventory metrics, into the DL point cloud reconstruction architectures to generate the full 3-D tree point clouds (the truth values are denoted by the full TLS tree point clouds containing the below-canopy information). Point cloud reconstruction accuracy was validated both through the measurement of error from the original TLS point clouds as well as the error of extraction of key structural metrics, such as crown base height, diameter above root crown, and leaf/wood volume. The results of this research additionally demonstrate the supplemental performance gain of using minimum locally sourced bio-inventory metric information as an input in ML systems to reach specified accuracy thresholds of tree point cloud reconstruction. This research provides insight into methods for the rapid, cost-effective, and accurate construction of below-canopy tree 3-D point clouds, as well as the supported potential of ML and DL to learn complex, unmodeled patterns of fractal tree growth symmetry.

Keywords: deep learning, machine learning, satellite, photogrammetry, aerial laser scanning, terrestrial laser scanning, point cloud, fractal symmetry

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Authors: Misrak Assefa

Abstract:

The Amharic language is the second most widely spoken Semitic language in the world. There are several new overloaded on the web. Searching some useful documents from the web on a specific topic, which is written in the Amharic language, is a challenging task. Hence, document categorization is required for managing and filtering important information. In the classification of Amharic text news, there is still a gap in the domain of information that needs to be launch. This study attempts to design an automatic Amharic news classification using a supervised learning mechanism on four un-touch classes. To achieve this research, 4,182 news articles were used. Naive Bayes (NB) and Decision tree (j48) algorithms were used to classify the given Amharic dataset. In this paper, k-fold cross-validation is used to estimate the accuracy of the classifier. As a result, it shows those algorithms can be applicable in Amharic news categorization. The best average accuracy result is achieved by j48 decision tree and naïve Bayes is 95.2345 %, and 94.6245 % respectively using three categories. This research indicated that a typical decision tree algorithm is more applicable to Amharic news categorization.

Keywords: text categorization, supervised machine learning, naive Bayes, decision tree

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Authors: Tianyu Cao, KIRA (Ruizhi Zhao)

Abstract:

The purpose of our research is to study the possibility of people with physical disabilities manipulating mechanical prostheses through brain-computer interface (BCI) technology. The brain-machine interface (BCI) of the neural prosthesis records signals from neurons and uses mathematical modeling to decode them, converting desired movements into body movements. In order to improve the patient's neural control, the prosthesis is given a natural feeling. It records data from sensitive areas from the body to the prosthetic limb and encodes signals in the form of electrical stimulation to the brain. In our research, the brain-computer interface (BCI) is a bridge connecting patients’ cognition and the real world, allowing information to interact with each other. The efficient work between the two is achieved through external devices. The flow of information is controlled by BCI’s ability to record neuronal signals and decode signals, which are converted into device control. In this way, we could encode information and then send it to the brain through electrical stimulation, which has significant medical application.

Keywords: biomedical engineering, brain-computer interface, prosthesis, neural control

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