Search results for: permanent magnet machine

Authors: Anli Du Preez, James Bekker

Abstract:

Data is currently one of the most critical and influential emerging technologies. However, the true potential of data is yet to be exploited since, currently, about 1% of generated data are ever actually analyzed for value creation. There is a data gap where data is not explored due to the lack of data analytics infrastructure and the required data analytics skills. This study developed a decision support framework for data analytics by following Jabareen’s framework development methodology. The study focused on machine learning algorithms, which is a subset of data analytics. The developed framework is designed to assist data analysts with little experience, in choosing the appropriate machine learning algorithm given the purpose of their application.

Keywords: Data analytics, Industrial engineering, Machine learning, Value creation

Procedia PDF Downloads 144

Authors: Peter Adekunle, Clinton Aigbavboa, Matthew Ikuabe, Opeoluwa Akinradewo

Abstract:

The future of machine learning (ML) in building may seem like a distant idea that will take decades to materialize, but it is actually far closer than previously believed. In reality, the built environment has been progressively increasing interest in machine learning. Although it could appear to be a very technical, impersonal approach, it can really make things more personable. Instead of eliminating humans out of the equation, machine learning allows people do their real work more efficiently. It is therefore vital to evaluate the factors influencing the implementation and challenges of implementing machine learning techniques in the South African built environment. The study's design was one of a survey. In South Africa, construction workers and professionals were given a total of one hundred fifty (150) questionnaires, of which one hundred and twenty-four (124) were returned and deemed eligible for study. Utilizing percentage, mean item scores, standard deviation, and Kruskal-Wallis, the collected data was analyzed. The results demonstrate that the top factors influencing the adoption of machine learning are knowledge level and a lack of understanding of its potential benefits. While lack of collaboration among stakeholders and lack of tools and services are the key hurdles to the deployment of machine learning within the South African built environment. The study came to the conclusion that ML adoption should be promoted in order to increase safety, productivity, and service quality within the built environment.

Keywords: machine learning, implementation, built environment, construction stakeholders

Procedia PDF Downloads 105

Authors: Amy M. Kwon

Abstract:

Support vector machine (SVM) is a well-known algorithm in machine learning due to its superior performance, and it also functions well in multiple-instance (MI) problems. Our study proposes a schematic algorithm to select instances based on Hausdorff distance, which can be adapted to SVMs as input vectors under the MI setting. Based on experiments on five benchmark datasets, our strategy for adapting representation outperformed in comparison with original approach. In addition, task execution times (TETs) were reduced by more than 80% based on MissSVM. Hence, it is noteworthy to consider this representation adaptation to SVMs under MI-setting.

Keywords: support vector machine, Margin, Hausdorff distance, representation selection, multiple-instance learning, machine learning

Procedia PDF Downloads 5

Authors: Guang Yang, Zhiwei Bai, Bo Sun

Abstract:

This paper analyzes the influence of failure mode and harmfulness of rotative machine according to FMECA (Failure Mode, Effects, and Criticality Analysis) method, and finds out the weak links that affect the reliability of this equipment. Also in this paper, fault tree analysis software is used for quantitative and qualitative analysis, pointing out the main factors of failure of this equipment. Based on the experimental results, this paper puts forward corresponding measures for prevention and improvement, and fundamentally improves the inherent reliability of this rotative machine, providing the basis for the formulation of technical conditions for the safe operation of industrial applications.

Keywords: rotative machine, reliability test, fault tree analysis, FMECA

Procedia PDF Downloads 134

Authors: Benjamen Nathaniel, Eludiora Safiriyu Ijiyemi, Egume Oneme Lucky

Abstract:

A review on machine translation systems shows that a lot of computational artefacts has been carried out to translate written or spoken texts from a source language to Yorùbá language through Machine Translation systems. However, there are no work on French to Yorùbá language machine translation system; hence, the study investigated the process involved in the translation of French-to-Yorùbá language equivalent with the view to adopting a rule- based MT approach to build a Machine Translation framework from simple sentences administered through questionnaire. Articles and relevant textbooks were reviewed with key speakers of both languages interviewed to find out the processes involved in the translation of French language and their equivalent in Yorùbálanguage simple sentences using home domain terminologies. Achieving this, a model was formulated using phrase grammar structure, re-write rule, parse tree, automata theory- based techniques, designed and implemented respectively with unified modeling language (UML) and python programming language. Analysing the result, it was observed when carrying out the result that, the Machine Translation system performed 18.45% above Experimental Subject Respondent and 2.7% below Linguistics Expert when analysed with word orthography, sentence syntax and semantic correctness of the sentences. And, when compared with Google Machine Translation system, it was noticed that the developed system performed better on lexicons of the target language.

Keywords: machine translation (MT), rule-based, French language, Yoru`ba´ language

Procedia PDF Downloads 31

Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

Abstract:

The widespread use of machine learning applications in production is significantly accelerated by improved computing power and increasing data availability. Predictive quality enables the assurance of product quality by using machine learning models as a basis for decisions on test results. The use of real Bosch production data based on geometric gauge blocks from machining, mating data from assembly and hydraulic measurement data from final testing of directional valves is a promising approach to classifying the quality characteristics of workpieces.

Keywords: machine learning, classification, predictive quality, hydraulics, supervised learning

Procedia PDF Downloads 171

Authors: Khaled M. EL-Naggar

Abstract:

Synchronizing and damping torque coefficients of a synchronous machine can give a quite clear picture for machine behavior during transients. These coefficients are used as a power system transient stability measurement. In this paper, a crow search optimization algorithm is presented and implemented to study the power system stability during transients. The algorithm makes use of the machine responses to perform the stability study in time domain. The problem is formulated as a dynamic estimation problem. An objective function that minimizes the error square in the estimated coefficients is designed. The method is tested using practical system with different study cases. Results are reported and a thorough discussion is presented. The study illustrates that the proposed method can estimate the stability coefficients for the critical stable cases where other methods may fail. The tests proved that the proposed tool is an accurate and reliable tool for estimating the machine coefficients for assessment of power system stability.

Keywords: optimization, estimation, synchronous, machine, crow search

Procedia PDF Downloads 111

Authors: Tung-Hui Hsu, Wen-Yuh Jywe

Abstract:

Five-axis computer numerical control (CNC) machine tools (three linear and two rotary axes) are ideally suited to the fabrication of complex work pieces, such as dies, turbo blades, and cams. The locations of the axis average line and centerline of the rotary axes strongly influence the performance of these machines; however, techniques to compensate for eccentric error in the rotary axes remain weak. This paper proposes optical (Non-Bar) techniques capable of calibrating five-axis CNC machine tools and compensating for eccentric error in the rotary axes. This approach employs the measurement path in ISO/CD 10791-6 to determine the eccentric error in two rotary axes, for which compensatory measures can be implemented. Experimental results demonstrate that the proposed techniques can improve the performance of various five-axis CNC machine tools by more than 90%. Finally, a result of the cutting test using a B-type five-axis CNC machine tool confirmed to the usefulness of this proposed compensation technique.

Keywords: calibration, compensation, rotary axis, five-axis computer numerical control (CNC) machine tools, eccentric error, optical calibration system, ISO/CD 10791-6

Procedia PDF Downloads 357

Authors: S. Ben Brahim, T. H. Vuong, J. David, R. Bouallegue, M. Pietrzak-David

Abstract:

Electrical machine monitoring is important to protect motor from unexpected problems. Today, using wireless communication for electrical machines is interesting for both real time monitoring and diagnostic purposes. In this paper, we propose a system based on wireless communication IEEE 802.11 to control electrical machine. IEEE 802.11 standard is recommended for this type of applications because it provides a faster connection, better range from the base station, and better security. Therefore, our contribution is to study a new technique to control and monitor the rotating electrical machines (motors, generators) using wireless communication. The reliability of radio channel inside rotating electrical machine is also discussed. Then, the communication protocol, software and hardware design used for the proposed system are presented in detail and the experimental results of our system are illustrated.

Keywords: control, DFIM machine, electromagnetic field, EMC, IEEE 802.11, monitoring, rotating electrical machines, wireless communication

Procedia PDF Downloads 667

Authors: Nadia Allouache

Abstract:

Solar radiation is by far the largest and the most world’s abundant, clean and permanent energy source. The amount of solar radiation intercepted by the Earth is much higher than annual global energy use. The energy available from the sun is greater than about 5200 times the global world’s need in 2006. In recent years, many promising technologies have been developed to harness the sun's energy. These technologies help in environmental protection, economizing energy, and sustainable development, which are the major issues of the world in the 21st century. One of these important technologies is the solar cooling systems that make use of either absorption or adsorption technologies. The solar adsorption cooling systems are good alternative since they operate with environmentally benign refrigerants that are natural, free from CFCs, and therefore they have a zero ozone depleting potential (ODP). A numerical analysis of thermal and solar performances of an adsorption solar refrigerating system using different adsorbent/adsorbate pairs such as activated carbon AC35 and activated carbon BPL/Ammoniac; is undertaken in this study. The modeling of the adsorption cooling machine requires the resolution of the equation describing the energy and mass transfer in the tubular adsorber that is the most important component of the machine. The Wilson and Dubinin- Astakhov models of the solid-adsorbat equilibrium are used to calculate the adsorbed quantity. The porous medium is contained in the annular space and the adsorber is heated by solar energy. Effect of key parameters on the adsorbed quantity and on the thermal and solar performances are analysed and discussed. The performances of the system that depends on the incident global irradiance during a whole day depends on the weather conditions: the condenser temperature and the evaporator temperature. The AC35/methanol pair is the best pair comparing to the BPL/Ammoniac in terms of system performances.

Keywords: activated carbon-methanol pair, activated carbon-ammoniac pair, adsorption, performance coefficients, numerical analysis, solar cooling system

Procedia PDF Downloads 46

Authors: Amel Ourici

Abstract:

An electrical machine failure that is not identified in an initial stage may become catastrophic and it may suffer severe damage. Thus, undetected machine faults may cascade in it failure, which in turn may cause production shutdowns. Such shutdowns are costly in terms of lost production time, maintenance costs, and wasted raw materials. Doubly fed induction generators are used mainly for wind energy conversion in MW power plants. This paper presents a detection of an inter turn stator fault in a doubly fed induction machine whose stator and rotor are supplied by two pulse width modulation (PWM) inverters. The method used in this article to detect this fault, is based on Park’s Vector Approach, using a neural network.

Keywords: doubly fed induction machine, PWM inverter, inter turn stator fault, Park’s vector approach, neural network

Procedia PDF Downloads 571

Authors: Pawel Twardowski, Maciej Tabaszewski, Jakub Czyżycki

Abstract:

The main goal of the work was to use machine learning to predict cutting-edge wear. The research was carried out while milling hardened steel with sintered carbide cutters at various cutting speeds. During the tests, cutting-edge wear was measured, and vibration acceleration signals were also measured. Appropriate measures were determined from the vibration signals and served as input data in the machine-learning process. Two approaches were used in this work. The first one involved a two-state classification of the cutting edge - suitable and unfit for further work. In the second approach, prediction of the cutting-edge state based on vibration signals was used. The obtained research results show that the appropriate use of machine learning algorithms gives excellent results related to monitoring cutting edge during the process.

Keywords: milling of hardened steel, tool wear, vibrations, machine learning

Procedia PDF Downloads 24

Authors: Yanhua Ma, Lu Zhai, Xinchen Wang, Hongyu Liang

Abstract:

In this paper, by understanding the development status of the human-machine interface in today's automobile cab, a subjective and objective evaluation system for evaluating the optimization of human-machine interface matching in automobile cab was established. The man-machine interface of the car cab was divided into a software interface and a hard interface. Objective evaluation method of software human factor analysis is used to evaluate the hard interface matching; The analytic hierarchy process is used to establish the evaluation index system for the software interface matching optimization, and the multi-level fuzzy comprehensive evaluation method is used to evaluate hard interface machine. This article takes Dongfeng Sokon (DFSK) C37 model automobile as an example. The evaluation method given in the paper is used to carry out relevant analysis and evaluation, and corresponding optimization suggestions are given, which have certain reference value for designers.

Keywords: analytic hierarchy process, fuzzy comprehension evaluation method, human-machine interface, matching optimization, software human factor analysis

Procedia PDF Downloads 121

Authors: Yizhen Zhao, Adam S. Z. Belloum, Goncalo Maia Da Costa, Zhiming Zhao

Abstract:

Machine learning has evolved from an area of academic research to a real-word applied field. This change comes with challenges, gaps and differences exist between common practices in academic environments and the ones in production environments. Following continuous integration, development and delivery practices in software engineering, similar trends have happened in machine learning (ML) systems, called MLOps. In this paper we propose a framework that helps to streamline and introduce best practices that facilitate the ML lifecycle in an industrial setting. This framework can be used as a template that can be customized to implement various machine learning experiment. The proposed framework is modular and can be recomposed to be adapted to various use cases (e.g. data versioning, remote training on cloud). The framework inherits practices from DevOps and introduces other practices that are unique to the machine learning system (e.g.data versioning). Our MLOps practices automate the entire machine learning lifecycle, bridge the gap between development and operation.

Keywords: cloud computing, continuous development, data versioning, DevOps, industrial setting, MLOps

Procedia PDF Downloads 237

Authors: Ali Asmari, Alex Symington, Htaik Than, Austin Caradonna, John Senft

Abstract:

This paper presents the collaborative effort between ULC Technologies and Con Edison in developing a groundbreaking robotic cable splicing machine. The focus is on the machine's design, which integrates advanced robotics and automation to enhance safety and efficiency in power network maintenance. The paper details the operational steps of the machine, including cable grounding, cutting, and removal of different insulation layers, and discusses its novel technological approach. The significant benefits over traditional methods, such as improved worker safety and reduced outage times, are highlighted based on the field data collected during the validation phase of the project. The paper also explores the future potential and scalability of this technology, emphasizing its role in transforming the landscape of power network maintenance.

Keywords: cable splicing machine, power network maintenance, electric distribution, electric transmission, medium voltage cable

Procedia PDF Downloads 39

Authors: E. K. A. Ogunshile

Abstract:

This paper is aimed at creating an Automatic Java X-Machine testing tool for software development. The nature of software development is changing; thus, the type of software testing tools required is also changing. Software is growing increasingly complex and, in part due to commercial impetus for faster software releases with new features and value, increasingly in danger of containing faults. These faults can incur huge cost for software development organisations and users; Cambridge Judge Business School’s research estimated the cost of software bugs to the global economy is $312 billion. Beyond the cost, faster software development methodologies and increasing expectations on developers to become testers is driving demand for faster, automated, and effective tools to prevent potential faults as early as possible in the software development lifecycle. Using X-Machine theory, this paper will explore a new tool to address software complexity, changing expectations on developers, faster development pressures and methodologies, with a view to reducing the huge cost of fixing software bugs.

Keywords: conformance testing, finite state machine, software testing, x-machine

Procedia PDF Downloads 247

Authors: N. Allouache, W. Elgahri, A. Gahfif, M. Belmedani

Abstract:

Solar radiation is by far the largest and the most world’s abundant, clean and permanent energy source. The amount of solar radiation intercepted by the Earth is much higher than annual global energy use. The energy available from the sun is greater than about 5200 times the global world’s need in 2006. In recent years, many promising technologies have been developed to harness the sun's energy. These technologies help in environmental protection, economizing energy, and sustainable development, which are the major issues of the world in the 21st century. One of these important technologies is the solar cooling systems that make use of either absorption or adsorption technologies. The solar adsorption cooling systems are a good alternative since they operate with environmentally benign refrigerants that are natural, free from CFCs, and therefore they have a zero ozone depleting potential (ODP). A numerical analysis of thermal and solar performances of an adsorption solar refrigerating system using different adsorbent/adsorbate pairs, such as activated carbon AC35 and activated carbon BPL/Ammoniac; is undertaken in this study. The modeling of the adsorption cooling machine requires the resolution of the equation describing the energy and mass transfer in the tubular adsorber, that is the most important component of the machine. The Wilson and Dubinin- Astakhov models of the solid-adsorbat equilibrium are used to calculate the adsorbed quantity. The porous medium is contained in the annular space, and the adsorber is heated by solar energy. Effect of key parameters on the adsorbed quantity and on the thermal and solar performances are analysed and discussed. The performances of the system that depends on the incident global irradiance during a whole day depends on the weather conditions: the condenser temperature and the evaporator temperature. The AC35/methanol pair is the best pair comparing to the BPL/Ammoniac in terms of system performances.

Keywords: activated carbon-methanol pair, activated carbon-ammoniac pair, adsorption, performance coefficients, numerical analysis, solar cooling system

Procedia PDF Downloads 54

Authors: Akinfiresoye W. A., Olarewaju O. O., Okunola, Okunola I. O.

Abstract:

An indigenous honey-extracting machine was designed, fabricated and evaluated at the workshop of the department of Agricultural Technology, Federal Polytechnic, Ile-Oluji, Nigeria using locally available materials. It has the extraction unit, the presser, the honey collector and the frame. The harvested honeycomb is placed inside the cylindrical extraction unit with perforated holes. The press plate was then placed on the comb while the hydraulic press of 3 tons was placed on it, supported by the frame. The hydraulic press, which is manually operated, forces the oil out of the extraction chamber through the perforated holes into the honey collector positioned at the lowest part of the extraction chamber. The honey-extracting machine has an average throughput of 2.59 kg/min and an efficiency of about 91%. The cost of producing the honey extracting machine is NGN 31, 700: 00, thirty-one thousand and seven hundred nairas only or $70 at NGN 452.8 to a dollar. This cost is affordable to beekeepers and would-be honey entrepreneurs. The honey-extracting machine is easy to operate and maintain without any complex technical know-how.

Keywords: honey, extractor, cost, efficiency

Procedia PDF Downloads 51

Authors: S. Jagadeesh Kumar

Abstract:

This expose presents a luculent of recent machine learning practices as employed in the modern and as pertinent to prospective automatic speech recognition schemes. The aspiration is to promote additional traverse ablution among the machine learning and automatic speech recognition factions that have transpired in the precedent. The manuscript is structured according to the chief machine learning archetypes that are furthermore trendy by now or have latency for building momentous hand-outs to automatic speech recognition expertise. The standards offered and convoluted in this article embraces adaptive and multi-task learning, active learning, Bayesian learning, discriminative learning, generative learning, supervised and unsupervised learning. These learning archetypes are aggravated and conferred in the perspective of automatic speech recognition tools and functions. This manuscript bequeaths and surveys topical advances of deep learning and learning with sparse depictions; further limelight is on their incessant significance in the evolution of automatic speech recognition.

Keywords: automatic speech recognition, deep learning methods, machine learning archetypes, Bayesian learning, supervised and unsupervised learning

Procedia PDF Downloads 418

Authors: Othman M. Hakami, Abdul Jabbar Al-Rajab

Abstract:

Occurrence of heavy metals in water resources increased in the recent years albeit at low concentrations. Lead (PbII) is among the most important inorganic pollutants in ground and surface water. However, removal of this toxic metal efficiently from water is of public and scientific concern. In this study, we developed a rapid and efficient removal method of lead from water using chitosan/magnetite nanoparticles. A simple and effective process has been used to prepare chitosan/magnetite nanoparticles (NPs) (CS/Mag NPs) with effect on saturation magnetization value; the particles were strongly responsive to an external magnetic field making separation from solution possible in less than 2 minutes using a permanent magnet and the total Fe in solution was below the detection limit of ICP-OES (<0.19 mg L-1). The hydrodynamic particle size distribution increased from an average diameter of ~60 nm for Fe3O4 NPs to ~75 nm after chitosan coating. The feasibility of the prepared NPs for the adsorption and desorption of Pb(II) from water were evaluated using Chitosan/Magnetite NPs which showed a high removal efficiency for Pb(II) uptake, with 90% of Pb(II) removed during the first 5 minutes and equilibrium in less than 10 minutes. Maximum adsorption capacities for Pb(II) occurred at pH 6.0 and under room temperature were as high as 85.5 mg g-1, according to Langmuir isotherm model. Desorption of adsorbed Pb on CS/Mag NPs was evaluated using deionized water at different pH values ranged from 1 to 7 which was an effective eluent and did not result the destruction of NPs, then, they could subsequently be reused without any loss of their activity in further adsorption tests. Overall, our results showed the high efficiency of chitosan/magnetite nanoparticles (NPs) in lead removal from water in controlled conditions, and further studies should be realized in real field conditions.

Keywords: chitosan, magnetite, water, treatment

Procedia PDF Downloads 379

Authors: Lemi Tolu

Abstract:

Background: Postpartum family planning (PPFP) focuses on the prevention of unintended and closely spaced pregnancies through the first 12 months following childbirth. Objective: This study assesses the barriers to uptake of long-term and permanent family planning methods among immediate post-partum mothers at Saint Paul’s Hospital Millennium Medical College in Addis Ababa, Ethiopia. Methodology: An institution-based cross-sectional study was conducted from January 1 to June 30, 2017. The six months of study were used as strata, and systematic sampling used to select participants in each month. Post-partum mothers were interviewed using pretested structured questionnaires. Data entry and analysis were done using SPSS version 17. Bivariate and multivariable logistic regressions were fitted to identify determinants of post-partum family planning uptake. An OR with 95% CIs was calculated, and p values set at 005 were used to determine the statistical significance of associations. Results: Four hundred and twenty-two post-partum women were interviewed. Two hundred sixty-eight (63%) women received counselling on family planning, and 241 (66.8 %) got information about contraception. One hundred and fifty-two (45%) of the women accepted long-term and permanent contraception on their immediate postpartum period before discharge. Contraceptive counselling (OR = 2.13, 95% CI 1.004-3.331), getting information from the health facility (OR = 15.15, 95% CI 1.848-19.242), and partner support (OR = 1.367, 95% CI 1.175-2.771) were significantly associated with long-term and permanent contraception uptake. Conclusion: Postpartum counselling on family planning and provision of contraception information improve immediate postpartum FP acceptance, and, hence postpartum programs need to strengthen such services.

Keywords: contraception, immediate postpartum, long-term family planning, post-partum family planning

Procedia PDF Downloads 194

Authors: M. Al-Farttoosi, M. Y. Rafiq, J. Summerscales, C. Williams

Abstract:

Structures can be subjected to impact loading from various sources like earthquake, tsunami, missiles and explosions. The impact loading can cause different degrees of damage to concrete structures. The demand for strengthening and rehabilitation of damaged structures is increasing. In recent years, Car0bon Fibre Reinforced Polymer (CFRP) matrix composites has gain more attention for strengthening and repairing these structures. To study the impact behaviour of the reinforced concrete (RC) beams strengthened or repaired using CFRP, a heavy impact test machine was designed and manufactured .The machine included a newly designed support system for beams together with various instrumentation. This paper describes the support design configuration of the impact test machine, instrumentation and dynamic analysis of the concrete beams. To evaluate the efficiency of the new impact test machine, experimental impact tests were conducted on simple supported reinforced concrete beam. Different methods were used to determine the impact force and impact response of the RC beams in terms of inertia force, maximum deflection, reaction force and fracture energy. The manufactured impact test machine was successfully used in testing RC beams under impact loading and used successfully to test the reinforced concrete beams strengthened or repaired using CFRP under impact loading.

Keywords: beam, concrete, impact, machine

Procedia PDF Downloads 397

Authors: Habtamu Ayenew Asegie

Abstract:

Wealth, as opposed to income or consumption, implies a more stable and permanent status. Due to natural and human-made difficulties, households' economies will be diminished, and their well-being will fall into trouble. Hence, governments and humanitarian agencies offer considerable resources for poverty and malnutrition reduction efforts. One key factor in the effectiveness of such efforts is the accuracy with which low-income or poor populations can be identified. As a result, this study aims to predict a household’s wealth status using ensemble Machine learning (ML) algorithms. In this study, design science research methodology (DSRM) is employed, and four ML algorithms, Random Forest (RF), Adaptive Boosting (AdaBoost), Light Gradient Boosted Machine (LightGBM), and Extreme Gradient Boosting (XGBoost), have been used to train models. The Ethiopian Demographic and Health Survey (EDHS) dataset is accessed for this purpose from the Central Statistical Agency (CSA)'s database. Various data pre-processing techniques were employed, and the model training has been conducted using the scikit learn Python library functions. Model evaluation is executed using various metrics like Accuracy, Precision, Recall, F1-score, area under curve-the receiver operating characteristics (AUC-ROC), and subjective evaluations of domain experts. An optimal subset of hyper-parameters for the algorithms was selected through the grid search function for the best prediction. The RF model has performed better than the rest of the algorithms by achieving an accuracy of 96.06% and is better suited as a solution model for our purpose. Following RF, LightGBM, XGBoost, and AdaBoost algorithms have an accuracy of 91.53%, 88.44%, and 58.55%, respectively. The findings suggest that some of the features like ‘Age of household head’, ‘Total children ever born’ in a family, ‘Main roof material’ of their house, ‘Region’ they lived in, whether a household uses ‘Electricity’ or not, and ‘Type of toilet facility’ of a household are determinant factors to be a focal point for economic policymakers. The determinant risk factors, extracted rules, and designed artifact achieved 82.28% of the domain expert’s evaluation. Overall, the study shows ML techniques are effective in predicting the wealth status of households.

Keywords: ensemble machine learning, households wealth status, predictive model, wealth status prediction

Procedia PDF Downloads 12

Authors: Melike Yaylacı, Tuğba Bilgin

Abstract:

Against the climate crisis, an increasing number of countries are working on green energy, carbon emission measurement, calculation and reduction. The work of industrial organizations with the highest carbon emissions on these issues is increasing. Aim of this paper is calculating carbon emissions of laser cutting machine with cradle-to-grave approach and discuss the potential affects of usage condisions, such as laser power, gas type, gas pressure, on carbon footprint. In particular, this study includes consumption of electricity used in production, laser cutting machine raw materials, and disposal of the machine. In the process of raw material supplying, machine procesing and shipping, all calculations were studied using the Tier1 approach. Laser cutting machines require a specified cutting parameter set for each different material in different thickneses, this parameters are a combination of laser power, gas type, cutting speed, gas pressure and focus point, The another purpose of this study is examine the potential affect of different cutting parameters for the same material in same thickness on carbon footprint.

Keywords: life cycle assessment, carbon emission, laser cutting machine, cutting parameters

Procedia PDF Downloads 69

Authors: In-Gun Kim, Hyun-Seok Hong, Dong-Woo Kang, Ju Lee

Abstract:

An Inverter type arc-welding machine is inclined to be designed for higher frequency in order to reduce the size and cost. The need of the core material reconsideration for high frequency pulse transformer is more important since core loss grows as the frequency rises. An arc welding machine’s pulse transformer is designed using an Area Product (Ap) method and is considered margin air gap core design in order to prevent the burning of the IGBT by the inrush current. Finally, the reduction of the core weight and the core size are compared according to different materials for 30kW inverter type arc welding machine.

Keywords: pulse transformers, welding, inrush current, air gaps

Procedia PDF Downloads 428

Authors: Uduak Umoh, Imo Eyoh, Emmauel Nyoho

Abstract:

This paper compares fuzzy-machine learning algorithms such as Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) for the predicting cases of fire outbreak. The paper uses the fire outbreak dataset with three features (Temperature, Smoke, and Flame). The data is pre-processed using Interval Type-2 Fuzzy Logic (IT2FL) algorithm. Min-Max Normalization and Principal Component Analysis (PCA) are used to predict feature labels in the dataset, normalize the dataset, and select relevant features respectively. The output of the pre-processing is a dataset with two principal components (PC1 and PC2). The pre-processed dataset is then used in the training of the aforementioned machine learning models. K-fold (with K=10) cross-validation method is used to evaluate the performance of the models using the matrices – ROC (Receiver Operating Curve), Specificity, and Sensitivity. The model is also tested with 20% of the dataset. The validation result shows KNN is the better model for fire outbreak detection with an ROC value of 0.99878, followed by SVM with an ROC value of 0.99753.

Keywords: Machine Learning Algorithms , Interval Type-2 Fuzzy Logic, Fire Outbreak, Support Vector Machine, K-Nearest Neighbour, Principal Component Analysis

Procedia PDF Downloads 152

Authors: Bhalke D. G., Bormane D. S., Kharate G. K.

Abstract:

This paper presents classification of musical instrument using machine learning techniques. The classification has been carried out using temporal, spectral, cepstral and wavelet features. Detail feature analysis is carried out using separate and combined features. Further, instrument model has been developed using K-Nearest Neighbor and Support Vector Machine (SVM). Benchmarked McGill university database has been used to test the performance of the system. Experimental result shows that SVM performs better as compared to KNN classifier.

Keywords: feature extraction, SVM, KNN, musical instruments

Procedia PDF Downloads 456

Authors: Jun Wang, Ge Zhang

Abstract:

Machine learning has been used in many areas today. It thrives at reviewing large volumes of data and identifying patterns and trends that might not be apparent to a human. Given the huge potential benefit and the amount of data available in the financial market, it is not surprising to see machine learning applied to various financial products. While future prices of financial securities are extremely difficult to forecast, we study them from a different angle. Instead of trying to forecast future prices, we apply machine learning algorithms to predict the direction of future price movement, in particular, whether a sector Exchange Traded Fund (ETF) would outperform or underperform the market in the next week or in the next month. We apply several machine learning algorithms for this prediction. The algorithms are Linear Discriminant Analysis (LDA), k-Nearest Neighbors (KNN), Decision Tree (DT), Gaussian Naive Bayes (GNB), and Neural Networks (NN). We show that these machine learning algorithms, most notably GNB and NN, have some predictive power in forecasting out-performance and under-performance out of sample. We also try to explore whether it is possible to utilize the predictions from these algorithms to outperform the buy-and-hold strategy of the S&P 500 index. The trading strategy to explore out-performance predictions does not perform very well, but the trading strategy to explore under-performance predictions can earn higher returns than simply holding the S&P 500 index out of sample.

Keywords: machine learning, ETF prediction, dynamic trading, asset allocation

Procedia PDF Downloads 63

Authors: Yukiko Sasaki Alam, Shahid Alam

Abstract:

Syntactic parsing is vital for semantic treatment by many applications related to natural language processing (NLP), because form and content coincide in many cases. However, it has not yet reached the levels of reliable performance. By manually examining and analyzing individual machine translation output errors that involve syntax as well as semantics, this study attempts to discover what is required for improving syntactic and semantic parsing.

Keywords: syntactic parsing, error analysis, machine translation, deep parsing

Procedia PDF Downloads 525

Authors: Isaac Ali Kwasu, Aje Tokan

Abstract:

The research was set to design a manually pedal operated water pump to aid water flow technology for irrigation activities for rural farmers. The development was carried out first by a prototype design to guide the fabrication. All items needed for the fabrication were used for the final product. The machine is operated manually by pedaling. This engages all the parts of the machine into active motion. Energy is generated and transfer finally to the pumping unit which is wired with plastic pipes. The pumping unit which is wired with PVC pipes, both linked to the water source and the reservoir respectively. The (rpm) revolution per minute of the machine is approximated at 3130 depending on the pedaling speed of the user. The machine does not have gear arrangement yet can give high (rpm) for effective performance. The pumping performance of the machine is 125 liters in one minute and can sustain small scale irrigation farming activities and to supplement water management system to sustain crop growth.

Keywords: pump, development, manual, flywheel, sprocket, pulley, machine, v belt, chain, hub, pipe, steel, mechanism, irrigation, prototype, fabrication

Procedia PDF Downloads 180