Search results for: support vector machines
7770 Factory Virtual Environment Development for Augmented and Virtual Reality
Authors: Michal Gregor, Jiri Polcar, Petr Horejsi, Michal Simon
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Machine visualization is an area of interest with fast and progressive development. We present a method of machine visualization which will be applicable in real industrial conditions according to current needs and demands. Real factory data were obtained in a newly built research plant. Methods described in this paper were validated on a case study. Input data were processed and the virtual environment was created. The environment contains information about dimensions, structure, disposition, and function. Hardware was enhanced by modular machines, prototypes, and accessories. We added new functionalities and machines into the virtual environment. The user is able to interact with objects such as testing and cutting machines, he/she can operate and move them. Proposed design consists of an environment with two degrees of freedom of movement. Users are in touch with items in the virtual world which are embedded into the real surroundings. This paper describes the development of the virtual environment. We compared and tested various options of factory layout virtualization and visualization. We analyzed possibilities of using a 3D scanner in the layout obtaining process and we also analyzed various virtual reality hardware visualization methods such as Stereoscopic (CAVE) projection, Head Mounted Display (HMD), and augmented reality (AR) projection provided by see-through glasses.Keywords: augmented reality, spatial scanner, virtual environment, virtual reality
Procedia PDF Downloads 3947769 An ANOVA-based Sequential Forward Channel Selection Framework for Brain-Computer Interface Application based on EEG Signals Driven by Motor Imagery
Authors: Forouzan Salehi Fergeni
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Converting the movement intents of a person into commands for action employing brain signals like electroencephalogram signals is a brain-computer interface (BCI) system. When left or right-hand motions are imagined, different patterns of brain activity appear, which can be employed as BCI signals for control. To make better the brain-computer interface (BCI) structures, effective and accurate techniques for increasing the classifying precision of motor imagery (MI) based on electroencephalography (EEG) are greatly needed. Subject dependency and non-stationary are two features of EEG signals. So, EEG signals must be effectively processed before being used in BCI applications. In the present study, after applying an 8 to 30 band-pass filter, a car spatial filter is rendered for the purpose of denoising, and then, a method of analysis of variance is used to select more appropriate and informative channels from a category of a large number of different channels. After ordering channels based on their efficiencies, a sequential forward channel selection is employed to choose just a few reliable ones. Features from two domains of time and wavelet are extracted and shortlisted with the help of a statistical technique, namely the t-test. Finally, the selected features are classified with different machine learning and neural network classifiers being k-nearest neighbor, Probabilistic neural network, support-vector-machine, Extreme learning machine, decision tree, Multi-layer perceptron, and linear discriminant analysis with the purpose of comparing their performance in this application. Utilizing a ten-fold cross-validation approach, tests are performed on a motor imagery dataset found in the BCI competition III. Outcomes demonstrated that the SVM classifier got the greatest classification precision of 97% when compared to the other available approaches. The entire investigative findings confirm that the suggested framework is reliable and computationally effective for the construction of BCI systems and surpasses the existing methods.Keywords: brain-computer interface, channel selection, motor imagery, support-vector-machine
Procedia PDF Downloads 367768 Friction and Wear, Including Mechanisms, Modeling,Characterization, Measurement and Testing (Bangladesh Case)
Authors: Gor Muradyan
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The paper is about friction and wear, including mechanisms, modeling, characterization, measurement and testing case in Bangladesh. Bangladesh is a country under development, A lot of people live here, approximately 145 million. The territory of this country is very small. Therefore buildings are very close to each other. As the pipe lines are very old, and people get almost dirty water, there are a lot of ongoing projects under ADB. In those projects the contractors using HDD machines (Horizontal Directional Drilling ) and grundoburst. These machines are working underground. As ground in Bangladesh is very sludge, machine can't work relevant because of big friction in the soil. When drilling works are finished machine is pulling the pipe underground. Very often the pulling of the pipes becomes very complicated because of the friction. Therefore long section of the pipe laying can’t be done because of a big friction. In that case, additional problems rise, as well as additional work must be done. As we mentioned above it is not possible to do big section of the pipe laying because of big friction in the soil, Because of this it is coming out that contractors must do more joints, more pressure test. It is always connected with additional expenditure and losing time. This machine can pull in 75 mm to 500 mm pipes connected with the soil condition. Length is possible till 500m related how much friction it will had on the puller. As less as much it can pull. Another machine grundoburst is not working at this soil condition at all. The machine is working with air compressor. This machine are using for the smaller diameter pipes, 20 mm to 63 mm. Most of the cases these machines are being used for the installing of the house connection pipes, for making service connection. To make a friction less contractors using bigger pulling had then the pipe. It is taking down the friction, But the problem of this machine is that it can't work at sludge. Because of mentioned reasons the friction has a big mining during this kind of works. There are a lot of ways to reduce the friction. In this paper we'll introduce the ways that we have researched during our practice in Bangladesh.Keywords: Bangladesh, friction and wear, HDD machines, reducing friction
Procedia PDF Downloads 3087767 Optimal Resource Configuration and Allocation Planning Problem for Bottleneck Machines and Auxiliary Tools
Authors: Yin-Yann Chen, Tzu-Ling Chen
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This study presents the case of an actual Taiwanese semiconductor assembly and testing manufacturer. Three major bottleneck manufacturing processes, namely, die bond, wire bond, and molding, are analyzed to determine how to use finite resources to achieve the optimal capacity allocation. A medium-term capacity allocation planning model is developed by considering the optimal total profit to satisfy the promised volume demanded by customers and to obtain the best migration decision among production lines for machines and tools. Finally, sensitivity analysis based on the actual case is provided to explore the effect of various parameter levels.Keywords: capacity planning, capacity allocation, machine migration, resource configuration
Procedia PDF Downloads 4507766 Early Diagnosis of Myocardial Ischemia Based on Support Vector Machine and Gaussian Mixture Model by Using Features of ECG Recordings
Authors: Merve Begum Terzi, Orhan Arikan, Adnan Abaci, Mustafa Candemir
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Acute myocardial infarction is a major cause of death in the world. Therefore, its fast and reliable diagnosis is a major clinical need. ECG is the most important diagnostic methodology which is used to make decisions about the management of the cardiovascular diseases. In patients with acute myocardial ischemia, temporary chest pains together with changes in ST segment and T wave of ECG occur shortly before the start of myocardial infarction. In this study, a technique which detects changes in ST/T sections of ECG is developed for the early diagnosis of acute myocardial ischemia. For this purpose, a database of real ECG recordings that contains a set of records from 75 patients presenting symptoms of chest pain who underwent elective percutaneous coronary intervention (PCI) is constituted. 12-lead ECG’s of the patients were recorded before and during the PCI procedure. Two ECG epochs, which are the pre-inflation ECG which is acquired before any catheter insertion and the occlusion ECG which is acquired during balloon inflation, are analyzed for each patient. By using pre-inflation and occlusion recordings, ECG features that are critical in the detection of acute myocardial ischemia are identified and the most discriminative features for the detection of acute myocardial ischemia are extracted. A classification technique based on support vector machine (SVM) approach operating with linear and radial basis function (RBF) kernels to detect ischemic events by using ST-T derived joint features from non-ischemic and ischemic states of the patients is developed. The dataset is randomly divided into training and testing sets and the training set is used to optimize SVM hyperparameters by using grid-search method and 10fold cross-validation. SVMs are designed specifically for each patient by tuning the kernel parameters in order to obtain the optimal classification performance results. As a result of implementing the developed classification technique to real ECG recordings, it is shown that the proposed technique provides highly reliable detections of the anomalies in ECG signals. Furthermore, to develop a detection technique that can be used in the absence of ECG recording obtained during healthy stage, the detection of acute myocardial ischemia based on ECG recordings of the patients obtained during ischemia is also investigated. For this purpose, a Gaussian mixture model (GMM) is used to represent the joint pdf of the most discriminating ECG features of myocardial ischemia. Then, a Neyman-Pearson type of approach is developed to provide detection of outliers that would correspond to acute myocardial ischemia. Neyman – Pearson decision strategy is used by computing the average log likelihood values of ECG segments and comparing them with a range of different threshold values. For different discrimination threshold values and number of ECG segments, probability of detection and probability of false alarm values are computed, and the corresponding ROC curves are obtained. The results indicate that increasing number of ECG segments provide higher performance for GMM based classification. Moreover, the comparison between the performances of SVM and GMM based classification showed that SVM provides higher classification performance results over ECG recordings of considerable number of patients.Keywords: ECG classification, Gaussian mixture model, Neyman–Pearson approach, support vector machine
Procedia PDF Downloads 1567765 A Method to Saturation Modeling of Synchronous Machines in d-q Axes
Authors: Mohamed Arbi Khlifi, Badr M. Alshammari
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This paper discusses the general methods to saturation in the steady-state, two axis (d & q) frame models of synchronous machines. In particular, the important role of the magnetic coupling between the d-q axes (cross-magnetizing phenomenon), is demonstrated. For that purpose, distinct methods of saturation modeling of dumper synchronous machine with cross-saturation are identified, and detailed models synthesis in d-q axes. A number of models are given in the final developed form. The procedure and the novel models are verified by a critical application to prove the validity of the method and the equivalence between all developed models is reported. Advantages of some of the models over the existing ones and their applicability are discussed.Keywords: cross-magnetizing, models synthesis, synchronous machine, saturated modeling, state-space vectors
Procedia PDF Downloads 4477764 Radar Fault Diagnosis Strategy Based on Deep Learning
Authors: Bin Feng, Zhulin Zong
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Radar systems are critical in the modern military, aviation, and maritime operations, and their proper functioning is essential for the success of these operations. However, due to the complexity and sensitivity of radar systems, they are susceptible to various faults that can significantly affect their performance. Traditional radar fault diagnosis strategies rely on expert knowledge and rule-based approaches, which are often limited in effectiveness and require a lot of time and resources. Deep learning has recently emerged as a promising approach for fault diagnosis due to its ability to learn features and patterns from large amounts of data automatically. In this paper, we propose a radar fault diagnosis strategy based on deep learning that can accurately identify and classify faults in radar systems. Our approach uses convolutional neural networks (CNN) to extract features from radar signals and fault classify the features. The proposed strategy is trained and validated on a dataset of measured radar signals with various types of faults. The results show that it achieves high accuracy in fault diagnosis. To further evaluate the effectiveness of the proposed strategy, we compare it with traditional rule-based approaches and other machine learning-based methods, including decision trees, support vector machines (SVMs), and random forests. The results demonstrate that our deep learning-based approach outperforms the traditional approaches in terms of accuracy and efficiency. Finally, we discuss the potential applications and limitations of the proposed strategy, as well as future research directions. Our study highlights the importance and potential of deep learning for radar fault diagnosis. It suggests that it can be a valuable tool for improving the performance and reliability of radar systems. In summary, this paper presents a radar fault diagnosis strategy based on deep learning that achieves high accuracy and efficiency in identifying and classifying faults in radar systems. The proposed strategy has significant potential for practical applications and can pave the way for further research.Keywords: radar system, fault diagnosis, deep learning, radar fault
Procedia PDF Downloads 817763 Advanced Electric Motor Design Using Hollow Conductors for Maximizing Power, Density and Degree of Efficiency
Authors: Michael Naderer, Manuel Hartong, Raad Al-Kinani
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The use of hollow conductors is known in electric generators of large MW scale. The application of motors of small scale between 50 and 200kW is new. The latest results in the practical application and set up of machines show that the power density can be raised significantly and the common problem of derating of the motors is prevented. Furthermore, new design dimensions can be realised as continuous current densities up to 75A/mm² are achievable. This paper shows the results of the application of hollow conductors for a motor design used for automotive traction machines comparing common coolings with hollow conductor cooling.Keywords: degree of efficiency, electric motor design, hollow conductors, power density
Procedia PDF Downloads 1907762 Prediction of Formation Pressure Using Artificial Intelligence Techniques
Authors: Abdulmalek Ahmed
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Formation pressure is the main function that affects drilling operation economically and efficiently. Knowing the pore pressure and the parameters that affect it will help to reduce the cost of drilling process. Many empirical models reported in the literature were used to calculate the formation pressure based on different parameters. Some of these models used only drilling parameters to estimate pore pressure. Other models predicted the formation pressure based on log data. All of these models required different trends such as normal or abnormal to predict the pore pressure. Few researchers applied artificial intelligence (AI) techniques to predict the formation pressure by only one method or a maximum of two methods of AI. The objective of this research is to predict the pore pressure based on both drilling parameters and log data namely; weight on bit, rotary speed, rate of penetration, mud weight, bulk density, porosity and delta sonic time. A real field data is used to predict the formation pressure using five different artificial intelligence (AI) methods such as; artificial neural networks (ANN), radial basis function (RBF), fuzzy logic (FL), support vector machine (SVM) and functional networks (FN). All AI tools were compared with different empirical models. AI methods estimated the formation pressure by a high accuracy (high correlation coefficient and low average absolute percentage error) and outperformed all previous. The advantage of the new technique is its simplicity, which represented from its estimation of pore pressure without the need of different trends as compared to other models which require a two different trend (normal or abnormal pressure). Moreover, by comparing the AI tools with each other, the results indicate that SVM has the advantage of pore pressure prediction by its fast processing speed and high performance (a high correlation coefficient of 0.997 and a low average absolute percentage error of 0.14%). In the end, a new empirical correlation for formation pressure was developed using ANN method that can estimate pore pressure with a high precision (correlation coefficient of 0.998 and average absolute percentage error of 0.17%).Keywords: Artificial Intelligence (AI), Formation pressure, Artificial Neural Networks (ANN), Fuzzy Logic (FL), Support Vector Machine (SVM), Functional Networks (FN), Radial Basis Function (RBF)
Procedia PDF Downloads 1477761 Adaptive Process Monitoring for Time-Varying Situations Using Statistical Learning Algorithms
Authors: Seulki Lee, Seoung Bum Kim
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Statistical process control (SPC) is a practical and effective method for quality control. The most important and widely used technique in SPC is a control chart. The main goal of a control chart is to detect any assignable changes that affect the quality output. Most conventional control charts, such as Hotelling’s T2 charts, are commonly based on the assumption that the quality characteristics follow a multivariate normal distribution. However, in modern complicated manufacturing systems, appropriate control chart techniques that can efficiently handle the nonnormal processes are required. To overcome the shortcomings of conventional control charts for nonnormal processes, several methods have been proposed to combine statistical learning algorithms and multivariate control charts. Statistical learning-based control charts, such as support vector data description (SVDD)-based charts, k-nearest neighbors-based charts, have proven their improved performance in nonnormal situations compared to that of the T2 chart. Beside the nonnormal property, time-varying operations are also quite common in real manufacturing fields because of various factors such as product and set-point changes, seasonal variations, catalyst degradation, and sensor drifting. However, traditional control charts cannot accommodate future condition changes of the process because they are formulated based on the data information recorded in the early stage of the process. In the present paper, we propose a SVDD algorithm-based control chart, which is capable of adaptively monitoring time-varying and nonnormal processes. We reformulated the SVDD algorithm into a time-adaptive SVDD algorithm by adding a weighting factor that reflects time-varying situations. Moreover, we defined the updating region for the efficient model-updating structure of the control chart. The proposed control chart simultaneously allows efficient model updates and timely detection of out-of-control signals. The effectiveness and applicability of the proposed chart were demonstrated through experiments with the simulated data and the real data from the metal frame process in mobile device manufacturing.Keywords: multivariate control chart, nonparametric method, support vector data description, time-varying process
Procedia PDF Downloads 2957760 Principal Component Analysis Combined Machine Learning Techniques on Pharmaceutical Samples by Laser Induced Breakdown Spectroscopy
Authors: Kemal Efe Eseller, Göktuğ Yazici
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Laser-induced breakdown spectroscopy (LIBS) is a rapid optical atomic emission spectroscopy which is used for material identification and analysis with the advantages of in-situ analysis, elimination of intensive sample preparation, and micro-destructive properties for the material to be tested. LIBS delivers short pulses of laser beams onto the material in order to create plasma by excitation of the material to a certain threshold. The plasma characteristics, which consist of wavelength value and intensity amplitude, depends on the material and the experiment’s environment. In the present work, medicine samples’ spectrum profiles were obtained via LIBS. Medicine samples’ datasets include two different concentrations for both paracetamol based medicines, namely Aferin and Parafon. The spectrum data of the samples were preprocessed via filling outliers based on quartiles, smoothing spectra to eliminate noise and normalizing both wavelength and intensity axis. Statistical information was obtained and principal component analysis (PCA) was incorporated to both the preprocessed and raw datasets. The machine learning models were set based on two different train-test splits, which were 70% training – 30% test and 80% training – 20% test. Cross-validation was preferred to protect the models against overfitting; thus the sample amount is small. The machine learning results of preprocessed and raw datasets were subjected to comparison for both splits. This is the first time that all supervised machine learning classification algorithms; consisting of Decision Trees, Discriminant, naïve Bayes, Support Vector Machines (SVM), k-NN(k-Nearest Neighbor) Ensemble Learning and Neural Network algorithms; were incorporated to LIBS data of paracetamol based pharmaceutical samples, and their different concentrations on preprocessed and raw dataset in order to observe the effect of preprocessing.Keywords: machine learning, laser-induced breakdown spectroscopy, medicines, principal component analysis, preprocessing
Procedia PDF Downloads 837759 Comparing SVM and Naïve Bayes Classifier for Automatic Microaneurysm Detections
Authors: A. Sopharak, B. Uyyanonvara, S. Barman
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Diabetic retinopathy is characterized by the development of retinal microaneurysms. The damage can be prevented if disease is treated in its early stages. In this paper, we are comparing Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers for automatic microaneurysm detection in images acquired through non-dilated pupils. The Nearest Neighbor classifier is used as a baseline for comparison. Detected microaneurysms are validated with expert ophthalmologists’ hand-drawn ground-truths. The sensitivity, specificity, precision and accuracy of each method are also compared.Keywords: diabetic retinopathy, microaneurysm, naive Bayes classifier, SVM classifier
Procedia PDF Downloads 3217758 Determining Current and Future Training Needs of Ontario Workers Supporting Persons with Developmental Disabilities
Authors: Erin C. Rodenburg, Jennifer McWhirter, Andrew Papadopoulos
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Support workers for adults with developmental disabilities promote the care and wellbeing of a historically underserved population. Poor employment training and low work satisfaction for these disability support workers are linked to low productivity, poor quality of care, turnover, and intention to leave employment. Therefore, to improve the lives of those within disability support homes, both client and caregiver, it is vital to determine where improvements to training and support for those providing direct care can be made. The current study aims to explore disability support worker’s perceptions of the training received in their employment at the residential homes, how it prepared them for their role, and where there is room for improvement with the aim of developing recommendations for an improved training experience. Responses were collected from 85 disability support workers across 40 Ontario group homes. Findings suggest most disability support workers within the 40 support homes feel adequately trained in their responsibilities of employment. For those who did not feel adequately trained, the main issues expressed were a lack of standardization in training, a need for more continuous training, and a move away from trial and error in performing tasks to support clients with developmental disabilities.Keywords: developmental disabilities, disability workers, support homes, training
Procedia PDF Downloads 1797757 Review of Different Machine Learning Algorithms
Authors: Syed Romat Ali Shah, Bilal Shoaib, Saleem Akhtar, Munib Ahmad, Shahan Sadiqui
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Classification is a data mining technique, which is recognizedon Machine Learning (ML) algorithm. It is used to classifythe individual articlein a knownofinformation into a set of predefinemodules or group. Web mining is also a portion of that sympathetic of data mining methods. The main purpose of this paper to analysis and compare the performance of Naïve Bayse Algorithm, Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN)and Support Vector Machine (SVM). This paper consists of different ML algorithm and their advantages and disadvantages and also define research issues.Keywords: Data Mining, Web Mining, classification, ML Algorithms
Procedia PDF Downloads 2907756 Stress and Social Support as Predictors of Quality of Life: A Case among Flood Victims in Malaysia
Authors: Najib Ahmad Marzuki, Che Su Mustaffa, Johana Johari, Nur Haffiza Rahaman
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The purpose of this paper is to examine the effects and relationship of stress and social support towards the quality of life among flood victims in Malaysia. A total of 764 respondents took part in the survey via random sampling. The depression, anxiety, and stress scales were utilized to measure stress while The Multidimensional Scale of Perceived Social Support was used to measure the quality of life. The findings of this study indicate that there were significant correlations between variables in the study. The findings show a significant negative relation between stress and quality of life, and significant positive correlations between support from family as well as support from friends with the quality of life. Stress and support from family were found to be significant predictors and influences the quality of life among flood victims.Keywords: stress, social support, quality of life, flood victims
Procedia PDF Downloads 5477755 Detection of Phoneme [S] Mispronounciation for Sigmatism Diagnosis in Adults
Authors: Michal Krecichwost, Zauzanna Miodonska, Pawel Badura
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The diagnosis of sigmatism is mostly based on the observation of articulatory organs. It is, however, not always possible to precisely observe the vocal apparatus, in particular in the oral cavity of the patient. Speech processing can allow to objectify the therapy and simplify the verification of its progress. In the described study the methodology for classification of incorrectly pronounced phoneme [s] is proposed. The recordings come from adults. They were registered with the speech recorder at the sampling rate of 44.1 kHz and the resolution of 16 bit. The database of pathological and normative speech has been collected for the study including reference assessments provided by the speech therapy experts. Ten adult subjects were asked to simulate a certain type of stigmatism under the speech therapy expert supervision. In the recordings, the analyzed phone [s] was surrounded by vowels, viz: ASA, ESE, ISI, SPA, USU, YSY. Thirteen MFCC (mel-frequency cepstral coefficients) and RMS (root mean square) values are calculated within each frame being a part of the analyzed phoneme. Additionally, 3 fricative formants along with corresponding amplitudes are determined for the entire segment. In order to aggregate the information within the segment, the average value of each MFCC coefficient is calculated. All features of other types are aggregated by means of their 75th percentile. The proposed method of features aggregation reduces the size of the feature vector used in the classification. Binary SVM (support vector machine) classifier is employed at the phoneme recognition stage. The first group consists of pathological phones, while the other of the normative ones. The proposed feature vector yields classification sensitivity and specificity measures above 90% level in case of individual logo phones. The employment of a fricative formants-based information improves the sole-MFCC classification results average of 5 percentage points. The study shows that the employment of specific parameters for the selected phones improves the efficiency of pathology detection referred to the traditional methods of speech signal parameterization.Keywords: computer-aided pronunciation evaluation, sibilants, sigmatism diagnosis, speech processing
Procedia PDF Downloads 2767754 Climatic and Environmental Variables Do Not Affect the Diversity of Possible Phytoplasmic Vector Insects Associated with Quercus humboltii Oak Trees in Bogota, Colombia
Authors: J. Lamilla-Monje, C. Solano-Puerto, L. Franco-Lara
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Trees play an essential role in cities due to their ability to provide multiple ecosystem goods and services. Bogota trees are threatened by factors such as pests, pathogens, contamination, among others. Among the pathogens, phytoplasmas are a potential risk for urban trees, generating symptoms that affect the ecosystem services that these trees provide in Bogota, an example of this is the affectation of Q. humboldtii by phytoplasmas, these bacteria are transmitted for insects of the order Hemiptera, this is why the objective of this work was to know if the climatic variables (humidity, precipitation, and temperature) and environmental variables (PM10 and PM2.5) could be related to the distribution of the Oak Quercus entomofauna and specifically with the phytoplasma vector insects in Bogota. For this study, the sampling points were distributed in areas of the city with contrasting variables in two types of locations: parks and streets. A total of 68 trees were sampled in which the associated insects were collected using two methodologies: jameo and agitation traps. The results show that insects of the order Hemiptera were the most abundant, including a total of 1682 individuals represented by 29 morphotypes, within this order individuals from eight families were collected (Aphidae, Aradidae, Berytidae, Cicadellidae, Issidae, Membracidae, Miridae, and Psyllidae), finding as possible vectors the families Cicadellidae, Membracidae, and Psyllidae with 959, 8 and 14 individuals respectively. Within the Cicadellidae family, 21 morphotypes were found, being reported as vectors in the literature: Amplicephalus, Exitianus atratus, Haldorus sp., Xestocephalus desertorum, Idiocerinae sp., Scaphytopius sp., the Membracidae family was represented by two morphotypes and the Psyllidae by one. Results that suggest that there is no correlation between climatic and environmental variables with the diversity of insects associated with oak. Knowing the vector insects of phytoplasmas in oak trees will complete the pathosystem and generate effective vector control.Keywords: vector insects, diversity, phytoplasmas, Cicadellidae
Procedia PDF Downloads 1487753 Experimental Implementation of Model Predictive Control for Permanent Magnet Synchronous Motor
Authors: Abdelsalam A. Ahmed
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Fast speed drives for Permanent Magnet Synchronous Motor (PMSM) is a crucial performance for the electric traction systems. In this paper, PMSM is drived with a Model-based Predictive Control (MPC) technique. Fast speed tracking is achieved through optimization of the DC source utilization using MPC. The technique is based on predicting the optimum voltage vector applied to the driver. Control technique is investigated by comparing to the cascaded PI control based on Space Vector Pulse Width Modulation (SVPWM). MPC and SVPWM-based FOC are implemented with the TMS320F2812 DSP and its power driver circuits. The designed MPC for a PMSM drive is experimentally validated on a laboratory test bench. The performances are compared with those obtained by a conventional PI-based system in order to highlight the improvements, especially regarding speed tracking response.Keywords: permanent magnet synchronous motor, model-based predictive control, DC source utilization, cascaded PI control, space vector pulse width modulation, TMS320F2812 DSP
Procedia PDF Downloads 6337752 Productivity and Structural Design of Manufacturing Systems
Authors: Ryspek Usubamatov, Tan San Chin, Sarken Kapaeva
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Productivity of the manufacturing systems depends on technological processes, a technical data of machines and a structure of systems. Technology is presented by the machining mode and data, a technical data presents reliability parameters and auxiliary time for discrete production processes. The term structure of manufacturing systems includes the number of serial and parallel production machines and links between them. Structures of manufacturing systems depend on the complexity of technological processes. Mathematical models of productivity rate for manufacturing systems are important attributes that enable to define best structure by criterion of a productivity rate. These models are important tool in evaluation of the economical efficiency for production systems.Keywords: productivity, structure, manufacturing systems, structural design
Procedia PDF Downloads 5757751 Utilizing Google Earth for Internet GIS
Authors: Alireza Derambakhsh
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The objective of this examination is to explore the capability of utilizing Google Earth for Internet GIS applications. The study particularly analyzes the utilization of vector and characteristic information and the capability of showing and preparing this information in new ways utilizing the Google Earth stage. It has progressively been perceived that future improvements in GIS will fixate on Internet GIS, and in three noteworthy territories: GIS information access, spatial data scattering and GIS displaying/preparing. Google Earth is one of the group of geobrowsers that offer a free and simple to utilize administration that empower information with a spatial part to be overlain on top of a 3-D model of the Earth. This examination makes a methodological structure to accomplish its objective that comprises of three noteworthy parts: A database level, an application level and a customer level. As verification of idea a web model has been produced, which incorporates a differing scope of datasets and lets clients direst inquiries and make perceptions of this custom information. The outcomes uncovered that both vector and property information can be successfully spoken to and imagined utilizing Google Earth. In addition, the usefulness to question custom information and envision results has been added to the Google Earth stage.Keywords: Google earth, internet GIS, vector, characteristic information
Procedia PDF Downloads 2997750 Data Hiding by Vector Quantization in Color Image
Authors: Yung Gi Wu
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With the growing of computer and network, digital data can be spread to anywhere in the world quickly. In addition, digital data can also be copied or tampered easily so that the security issue becomes an important topic in the protection of digital data. Digital watermark is a method to protect the ownership of digital data. Embedding the watermark will influence the quality certainly. In this paper, Vector Quantization (VQ) is used to embed the watermark into the image to fulfill the goal of data hiding. This kind of watermarking is invisible which means that the users will not conscious the existing of embedded watermark even though the embedded image has tiny difference compared to the original image. Meanwhile, VQ needs a lot of computation burden so that we adopt a fast VQ encoding scheme by partial distortion searching (PDS) and mean approximation scheme to speed up the data hiding process. The watermarks we hide to the image could be gray, bi-level and color images. Texts are also can be regarded as watermark to embed. In order to test the robustness of the system, we adopt Photoshop to fulfill sharpen, cropping and altering to check if the extracted watermark is still recognizable. Experimental results demonstrate that the proposed system can resist the above three kinds of tampering in general cases.Keywords: data hiding, vector quantization, watermark, color image
Procedia PDF Downloads 3587749 Signal Processing Techniques for Adaptive Beamforming with Robustness
Authors: Ju-Hong Lee, Ching-Wei Liao
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Adaptive beamforming using antenna array of sensors is useful in the process of adaptively detecting and preserving the presence of the desired signal while suppressing the interference and the background noise. For conventional adaptive array beamforming, we require a prior information of either the impinging direction or the waveform of the desired signal to adapt the weights. The adaptive weights of an antenna array beamformer under a steered-beam constraint are calculated by minimizing the output power of the beamformer subject to the constraint that forces the beamformer to make a constant response in the steering direction. Hence, the performance of the beamformer is very sensitive to the accuracy of the steering operation. In the literature, it is well known that the performance of an adaptive beamformer will be deteriorated by any steering angle error encountered in many practical applications, e.g., the wireless communication systems with massive antennas deployed at the base station and user equipment. Hence, developing effective signal processing techniques to deal with the problem due to steering angle error for array beamforming systems has become an important research work. In this paper, we present an effective signal processing technique for constructing an adaptive beamformer against the steering angle error. The proposed array beamformer adaptively estimates the actual direction of the desired signal by using the presumed steering vector and the received array data snapshots. Based on the presumed steering vector and a preset angle range for steering mismatch tolerance, we first create a matrix related to the direction vector of signal sources. Two projection matrices are generated from the matrix. The projection matrix associated with the desired signal information and the received array data are utilized to iteratively estimate the actual direction vector of the desired signal. The estimated direction vector of the desired signal is then used for appropriately finding the quiescent weight vector. The other projection matrix is set to be the signal blocking matrix required for performing adaptive beamforming. Accordingly, the proposed beamformer consists of adaptive quiescent weights and partially adaptive weights. Several computer simulation examples are provided for evaluating and comparing the proposed technique with the existing robust techniques.Keywords: adaptive beamforming, robustness, signal blocking, steering angle error
Procedia PDF Downloads 1187748 Process of Research, Development and Application of New Pelletizer
Authors: Ľubomír Šooš, Peter Križan, Juraj Beniak, Miloš Matúš
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The success of introducing a new product on the market is the new principle of production, or progressive design, improved efficiency or high quality of manufactured products. Proportionally with the growth of interest in press-biofuels - pellets or briquettes, is also growing interest in the new design better, more efficiently machines produce pellets, briquettes or granules completely new shapes. Our department has for years dedicated to the development of new highly productive designs pressing machines and new optimized press-biofuels. In this field, we have more than 40 national and international patents. The aim of paper is description of the introduction of a new principle pelleting mill and the description of his process of research, development, manufacturing and testing to deployment into production.Keywords: compacting process, pellets mill, design, new conception, press-biofuels, patent, waste
Procedia PDF Downloads 3757747 Comparing Deep Architectures for Selecting Optimal Machine Translation
Authors: Despoina Mouratidis, Katia Lida Kermanidis
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Machine translation (MT) is a very important task in Natural Language Processing (NLP). MT evaluation is crucial in MT development, as it constitutes the means to assess the success of an MT system, and also helps improve its performance. Several methods have been proposed for the evaluation of (MT) systems. Some of the most popular ones in automatic MT evaluation are score-based, such as the BLEU score, and others are based on lexical similarity or syntactic similarity between the MT outputs and the reference involving higher-level information like part of speech tagging (POS). This paper presents a language-independent machine learning framework for classifying pairwise translations. This framework uses vector representations of two machine-produced translations, one from a statistical machine translation model (SMT) and one from a neural machine translation model (NMT). The vector representations consist of automatically extracted word embeddings and string-like language-independent features. These vector representations used as an input to a multi-layer neural network (NN) that models the similarity between each MT output and the reference, as well as between the two MT outputs. To evaluate the proposed approach, a professional translation and a "ground-truth" annotation are used. The parallel corpora used are English-Greek (EN-GR) and English-Italian (EN-IT), in the educational domain and of informal genres (video lecture subtitles, course forum text, etc.) that are difficult to be reliably translated. They have tested three basic deep learning (DL) architectures to this schema: (i) fully-connected dense, (ii) Convolutional Neural Network (CNN), and (iii) Long Short-Term Memory (LSTM). Experiments show that all tested architectures achieved better results when compared against those of some of the well-known basic approaches, such as Random Forest (RF) and Support Vector Machine (SVM). Better accuracy results are obtained when LSTM layers are used in our schema. In terms of a balance between the results, better accuracy results are obtained when dense layers are used. The reason for this is that the model correctly classifies more sentences of the minority class (SMT). For a more integrated analysis of the accuracy results, a qualitative linguistic analysis is carried out. In this context, problems have been identified about some figures of speech, as the metaphors, or about certain linguistic phenomena, such as per etymology: paronyms. It is quite interesting to find out why all the classifiers led to worse accuracy results in Italian as compared to Greek, taking into account that the linguistic features employed are language independent.Keywords: machine learning, machine translation evaluation, neural network architecture, pairwise classification
Procedia PDF Downloads 1237746 The Impact of Information and Communication Technology on the Performance of Office Technology Managers
Authors: Sunusi Tijjani
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Information and communication technology is an indispensable tool in the performance of office technology managers. Today's offices are automated and equipped with modern office machines that enhances and improve the work of office managers. However, today's office technology managers can process, evaluate, manage and communicate all forms of information using technological devices. Information and Communication Technology is viewed as the process of processing, storing ad dissemination information while office technology managers are trained professional who can effectively operate modern office machines, perform administrative duties and attend meetings to take dawn minute of meetings. This paper examines the importance of information and communication technology toward enhancing the work of office managers. It also stresses the importance of information and communication technology toward proper and accurate record management.Keywords: communication, information, technology, managers
Procedia PDF Downloads 4747745 Two Points Crossover Genetic Algorithm for Loop Layout Design Problem
Authors: Xu LiYun, Briand Florent, Fan GuoLiang
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The loop-layout design problem (LLDP) aims at optimizing the sequence of positioning of the machines around the cyclic production line. Traffic congestion is the usual criteria to minimize in this type of problem, i.e. the number of additional cycles spent by each part in the network until the completion of its required routing sequence of machines. This paper aims at applying several improvements mechanisms such as a positioned-based crossover operator for the Genetic Algorithm (GA) called a Two Points Crossover (TPC) and an offspring selection process. The performance of the improved GA is measured using well-known examples from literature and compared to other evolutionary algorithms. Good results show that GA can still be competitive for this type of problem against more recent evolutionary algorithms.Keywords: crossover, genetic algorithm, layout design problem, loop-layout, manufacturing optimization
Procedia PDF Downloads 2707744 FLC with 3DSVM for 4LEG 4WIRE Shunt Active Power Filter
Authors: Abdelhalim Kessal, Ali Chebabhi
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In this paper, a controller based on fuzzy logic control (FLC) associated to Three Dimensional Space Vector Modulation (3DSVM) is applied for shunt active filter in αβo axes domain. The main goals are to improve power quality under disturbed loads, minimize source currents harmonics and reduce neutral current magnitude in the four-wire structure. FLC is used to obtain the reference current and control the DC-bus voltage at the inverter output. The switching signals of the four-leg inverter are generating through a Three Dimensional Space Vector Modulation (3DSVM). Selected simulation results have been shown to validate the proposed system.Keywords: flc, 3dsvm, sapf, harmonic, inverter
Procedia PDF Downloads 4907743 Feature Extraction of MFCC Based on Fisher-Ratio and Correlated Distance Criterion for Underwater Target Signal
Authors: Han Xue, Zhang Lanyue
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In order to seek more effective feature extraction technology, feature extraction method based on MFCC combined with vector hydrophone is exposed in the paper. The sound pressure signal and particle velocity signal of two kinds of ships are extracted by using MFCC and its evolution form, and the extracted features are fused by using fisher-ratio and correlated distance criterion. The features are then identified by BP neural network. The results showed that MFCC, First-Order Differential MFCC and Second-Order Differential MFCC features can be used as effective features for recognition of underwater targets, and the fusion feature can improve the recognition rate. Moreover, the results also showed that the recognition rate of the particle velocity signal is higher than that of the sound pressure signal, and it reflects the superiority of vector signal processing.Keywords: vector information, MFCC, differential MFCC, fusion feature, BP neural network
Procedia PDF Downloads 5207742 Learning to Recommend with Negative Ratings Based on Factorization Machine
Authors: Caihong Sun, Xizi Zhang
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Rating prediction is an important problem for recommender systems. The task is to predict the rating for an item that a user would give. Most of the existing algorithms for the task ignore the effect of negative ratings rated by users on items, but the negative ratings have a significant impact on users’ purchasing decisions in practice. In this paper, we present a rating prediction algorithm based on factorization machines that consider the effect of negative ratings inspired by Loss Aversion theory. The aim of this paper is to develop a concave and a convex negative disgust function to evaluate the negative ratings respectively. Experiments are conducted on MovieLens dataset. The experimental results demonstrate the effectiveness of the proposed methods by comparing with other four the state-of-the-art approaches. The negative ratings showed much importance in the accuracy of ratings predictions.Keywords: factorization machines, feature engineering, negative ratings, recommendation systems
Procedia PDF Downloads 2397741 The Lateral and Torsional Vibration Analysis of a Rotor-Bearing System Using Transfer Matrix Method
Authors: Mohammad Hadi Jalali, Mostafa Ghayour, Saeed Ziaei-Rad, Behrooz Shahriari
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The vibration problems that can be occurred in the operational conditions of rotating machines may cause damage to the machine or even failure of the machine completely. Therefore, dynamic analysis of rotors is vital in the design and development stages of the rotating machines. In this study, the uncoupled torsional and lateral vibration analysis of a rotor-bearing system is carried out using transfer matrix method. The Campbell diagram, critical speed and the mode shape corresponding to the critical speed are obtained in order to evaluate the dynamic behavior of the rotor.Keywords: transfer matrix method, rotor-bearing system, campbell diagram, critical speed
Procedia PDF Downloads 485