Search results for: state machine

Authors: A. Elakkary, A. Echchatbi, N. Elalami

Abstract:

In this paper, we are interested in attitude control of a satellite, which using wheels of reaction, by state feedback. First, we develop a method allowing us to put the control and its integral in the state-feedback form. Then, by using the theorem of Gronwall- Bellman, we put the sufficient conditions so that the nonlinear system modeling the satellite is stabilisable and observed by state feedback.

Keywords: Satellite, attitude control, state feedback, attitude stabilization.

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Authors: Tatjana Eitrich, Bruno Lang

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This work deals with aspects of support vector machine learning for large-scale data mining tasks. Based on a decomposition algorithm for support vector machine training that can be run in serial as well as shared memory parallel mode we introduce a transformation of the training data that allows for the usage of an expensive generalized kernel without additional costs. We present experiments for the Gaussian kernel, but usage of other kernel functions is possible, too. In order to further speed up the decomposition algorithm we analyze the critical problem of working set selection for large training data sets. In addition, we analyze the influence of the working set sizes onto the scalability of the parallel decomposition scheme. Our tests and conclusions led to several modifications of the algorithm and the improvement of overall support vector machine learning performance. Our method allows for using extensive parameter search methods to optimize classification accuracy.

Keywords: Support Vector Machine Training, Multi-ParameterKernels, Shared Memory Parallel Computing, Large Data

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Authors: Suet Leng Khoo, Tiun Ling Ta, Lay Wah Lee

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In this era of globalization, the role of the State in all aspects of development is widely debated. Some scholars contend the 'demise' and diminishing role of the State whilst others claim that the State is still “de facto developmental". Clearly, it is vital to ascertain which of these two contentions are reflective of the role of the State as nations ascend their development trajectories. Based on the findings of this paper, the perception that the Malaysian State plays an active and committed role towards distributing equitable educational opportunities and enhancing employability of Malaysian PWDs is actually a myth and not reality. Thus, in order to fulfill the promise of Vision 2020 to transform Malaysia into a caring and socially-inclusive society; this paper calls for a more interventionist and committed role by the Malaysian State to translate the universal rights of education and employment opportunities for PWDs from mere policy rhetoric into inclusive realities.

Keywords: People with Disabilities, Malaysia, role of State, equal employment opportunities

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Authors: Nursyarizal Mohd Nor, Ramiah Jegatheesan, Perumal Nallagownden

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Newton-Raphson State Estimation method using bus admittance matrix remains as an efficient and most popular method to estimate the state variables. Elements of Jacobian matrix are computed from standard expressions which lack physical significance. In this paper, elements of the state estimation Jacobian matrix are obtained considering the power flow measurements in the network elements. These elements are processed one-by-one and the Jacobian matrix H is updated suitably in a simple manner. The constructed Jacobian matrix H is integrated with Weight Least Square method to estimate the state variables. The suggested procedure is successfully tested on IEEE standard systems.

Keywords: State Estimation (SE), Weight Least Square (WLS), Newton-Raphson State Estimation (NRSE), Jacobian matrix H.

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Authors: Mohammad Jafarifar

Abstract:

Multiphase Induction Machine (IM) is normally controlled using rotor field oriented vector control. Under phase(s) loss, the machine currents can be optimally controlled to satisfy certain optimization criteria. In this paper we discuss the performance of double manifold sliding mode observer (DM-SMO) in Sensorless control of multiphase induction machine under unsymmetrical condition (one phase loss). This observer is developed using the IM model in the stationary reference frame. DM-SMO is constructed by adding extra feedback term to conventional single mode sliding mode observer (SM-SMO) which proposed in many literature. This leads to a fully convergent observer that also yields an accurate estimate of the speed and stator currents. It will be shown by the simulation results that the estimated speed and currents by the method are very well and error between real and estimated quantities is negligible. Also parameter sensitivity analysis shows that this method is rather robust against parameter variation.

Keywords: Multiphase induction machine, field oriented control, sliding mode, unsymmetrical condition, manifold.

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Authors: Djamila Benhaddouche, Abdelkader Benyettou

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In this paper, we used data mining to extract biomedical knowledge. In general, complex biomedical data collected in studies of populations are treated by statistical methods, although they are robust, they are not sufficient in themselves to harness the potential wealth of data. For that you used in step two learning algorithms: the Decision Trees and Support Vector Machine (SVM). These supervised classification methods are used to make the diagnosis of thyroid disease. In this context, we propose to promote the study and use of symbolic data mining techniques.

Keywords: A classifier, Algorithms decision tree, knowledge extraction, Support Vector Machine.

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Authors: Wen-Yuh Jywe, Bor-Jeng Lin, Jing-Chung Shen, Jeng-Dao Lee, Hsueh-Liang Huang, Ming-Chen Cho

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The dynamical contouring error is a critical element for the accuracy of machine tools. The contouring error is defined as the difference between the processing actual path and commanded path, which is implemented by following the command curves from feeding driving system in machine tools. The contouring error is resulted from various factors, such as the external loads, friction, inertia moment, feed rate, speed control, servo control, and etc. Thus, the study proposes a 2D compensating system for the contouring accuracy of machine tools. Optical method is adopted by using stable frequency laser diode and the high precision position sensor detector (PSD) to performno-contact measurement. Results show the related accuracy of position sensor detector (PSD) of 2D contouring accuracy compensating system was ±1.5 μm for a calculated range of ±3 mm, and improvement accuracy is over 80% at high-speed feed rate.

Keywords: Position sensor detector, laser diode, contouring accuracy, machine tool.

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Authors: Kasra Majbouri Yazdi, Adel Majbouri Yazdi, Saeid Khodayi, Jingyu Hou, Wanlei Zhou, Saeed Saedy

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Fake news and false information are big challenges of all types of media, especially social media. There is a lot of false information, fake likes, views and duplicated accounts as big social networks such as Facebook and Twitter admitted. Most information appearing on social media is doubtful and in some cases misleading. They need to be detected as soon as possible to avoid a negative impact on society. The dimensions of the fake news datasets are growing rapidly, so to obtain a better result of detecting false information with less computation time and complexity, the dimensions need to be reduced. One of the best techniques of reducing data size is using feature selection method. The aim of this technique is to choose a feature subset from the original set to improve the classification performance. In this paper, a feature selection method is proposed with the integration of K-means clustering and Support Vector Machine (SVM) approaches which work in four steps. First, the similarities between all features are calculated. Then, features are divided into several clusters. Next, the final feature set is selected from all clusters, and finally, fake news is classified based on the final feature subset using the SVM method. The proposed method was evaluated by comparing its performance with other state-of-the-art methods on several specific benchmark datasets and the outcome showed a better classification of false information for our work. The detection performance was improved in two aspects. On the one hand, the detection runtime process decreased, and on the other hand, the classification accuracy increased because of the elimination of redundant features and the reduction of datasets dimensions.

Keywords: Fake news detection, feature selection, support vector machine, K-means clustering, machine learning, social media.

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Authors: W. Chattinnawat

Abstract:

Consider a mass production of HDD arms where hundreds of CNC machines are used to manufacturer the HDD arms. According to an overwhelming number of machines and models of arm, construction of separate control chart for monitoring each HDD arm model by each machine is not feasible. This research proposed a strategy to optimize the SPC management on shop floor. The procedure started from identifying the clusters of the machine with similar manufacturing performance using clustering technique. The three way control chart ( I - MR - R ) is then applied to each clustered group of machine. This proposed research has advantageous to the manufacturer in terms of not only better performance of the SPC but also the quality management paradigm.

Keywords: Three way control chart. I - MR - R , between/within variation, HDD arm.

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Authors: Wataru Nakamura, Tomoaki Hashimoto, Liang-Kuang Chen

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This paper provides a state estimation method for automatic control systems of nonlinear vehicle dynamics. A nonlinear tire model is employed to represent the realistic behavior of a vehicle. In general, all the state variables of control systems are not precisedly known, because those variables are observed through output sensors and limited parts of them might be only measurable. Hence, automatic control systems must incorporate some type of state estimation. It is needed to establish a state estimation method for nonlinear vehicle dynamics with restricted measurable state variables. For this purpose, unscented Kalman filter method is applied in this study for estimating the state variables of nonlinear vehicle dynamics. The objective of this paper is to propose a state estimation method using unscented Kalman filter for nonlinear vehicle dynamics. The effectiveness of the proposed method is verified by numerical simulations.

Keywords: State estimation, control systems, observer systems, unscented Kalman filter, nonlinear vehicle dynamics.

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Authors: A. T. Boum, L. Bitjoka, N. N. Léandre, S. Bennet

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This paper presents the estimation of the key parameters of a double fed induction machine (DFIM) by the use of the moving horizon estimator (MHE) for control and monitoring purpose. A study was conducted on the behavior of this observer in the presence of some faults which can occur during the operation of the machine. In the first case a stator phase has been suppressed. In the second case the rotor resistance has been multiplied by a factor. The results show a good estimation of different parameters such as rotor flux, rotor speed, stator current with a very small estimation error. The robustness of the observer was also tested in the practical case of DFIM by using another model different from the real one at a constant close. The very small estimation error makes the MHE a good software sensor candidate for monitoring purpose for the DFIM. 

Keywords: Doubly fed induction machine, moving horizon estimator parameters’ estimation.

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Authors: A. K. Sharma, N. P. Patidar, G. Agnihotri, D. K. Palwalia

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This paper presents the performance state analysis of Self-Excited Induction Generator (SEIG) using Artificial Bee Colony (ABC) optimization technique. The total admittance of the induction machine is minimized to calculate the frequency and magnetizing reactance corresponding to any rotor speed, load impedance and excitation capacitance. The performance of SEIG is calculated using the optimized parameter found. The results obtained by ABC algorithm are compared with results from numerical method. The results obtained coincide with the numerical method results. This technique proves to be efficient in solving nonlinear constrained optimization problems and analyzing the performance of SEIG.

Keywords: Artificial bee colony, Steady state analysis, Selfexcited induction generator, Nonlinear constrained optimization.

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Authors: Phruksaphanrat B.

Abstract:

This research presents a fuzzy multi-objective model for a machine selection problem in a flexible manufacturing system of a tire company. Two main objectives are minimization of an average machine error and minimization of the total setup time. Conventionally, the working team uses trial and error in selecting a pressing machine for each task due to the complexity and constraints of the problem. So, both objectives may not satisfy. Moreover, trial and error takes a lot of time to get the final decision. Therefore, in this research preemptive fuzzy goal programming model is developed for solving this multi-objective problem. The proposed model can obtain the appropriate results that the Decision Making (DM) is satisfied for both objectives. Besides, alternative choice can be easily generated by varying the satisfaction level. Additionally, decision time can be reduced by using the model, which includes all constraints of the system to generate the solutions. A numerical example is also illustrated to show the effectiveness of the proposed model.

Keywords: Machine Selection, Preemptive Fuzzy Goal Programming, Mixed Integer Programming, Application of Tire Industry.

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Authors: Erdal Eroğlu, Özhan Çetinkaya

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The purpose of this paper is to show how state plays a regulatory role in the relations of distribution by analyzing tax and expenditure in Turkey. This paper has two main arguments. First, state intervenes in economic and social life via budget policies and steers the relations of distribution within the scope of the reproduction of the capital accumulation and legitimacy. Secondly, a great amount of public expenditure benefits capital owners while state gains its tax income mainly from low and middle income groups.

Keywords: Distribution, public expenditure, state budget, taxes.

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Authors: Xiaobo Xi, Ruihong Zhang

Abstract:

At present, the orchard ditching and fertilizing technology has a series of problems, such as easy tree roots damage, high energy consumption and uneven fertilizing. In this paper, a gas explosion subsoiling and fertilizer injection machine was designed, which used high pressure gas to shock soil body and then injected fertilizer. The drill pipe mechanism with pneumatic chipping hammer excitation and hydraulic assistance was designed to drill the soil. The operation of gas and liquid fertilizer supply was controlled by PLC system. The 3D model of the whole machine was established by using SolidWorks software. The machine prototype was produced, and field experiments were carried out. The results showed that soil fractures were created and diffused by gas explosion, and the subsoiling effect radius reached 40 cm under the condition of 0.8 MPa gas pressure and 30 cm drilling depth. What’s more, the work efficiency is 0.048 hm²/h at least. This machine could meet the agronomic requirements of orchard, garden and city greening fertilization, and the tree roots were not easily damaged and the fertilizer evenly distributed, which was conducive to nutrient absorption of root growth.

Keywords: Gas explosion subsoiling, fertigation, pneumatic chipping hammer exciting, soil compaction.

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Authors: Chothmal, Basant Agarwal

Abstract:

Sentiment analysis means to classify a given review document into positive or negative polar document. Sentiment analysis research has been increased tremendously in recent times due to its large number of applications in the industry and academia. Sentiment analysis models can be used to determine the opinion of the user towards any entity or product. E-commerce companies can use sentiment analysis model to improve their products on the basis of users’ opinion. In this paper, we propose a new One-class Support Vector Machine (One-class SVM) based sentiment analysis model for movie review documents. In the proposed approach, we initially extract features from one class of documents, and further test the given documents with the one-class SVM model if a given new test document lies in the model or it is an outlier. Experimental results show the effectiveness of the proposed sentiment analysis model.

Keywords: Feature selection methods, Machine learning, NB, One-class SVM, Sentiment Analysis, Support Vector Machine.

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Authors: P. Wlodarczak, J. Soar, M. Ally

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The dramatic rise in the use of Social Media (SM) platforms such as Facebook and Twitter provide access to an unprecedented amount of user data. Users may post reviews on products and services they bought, write about their interests, share ideas or give their opinions and views on political issues. There is a growing interest in the analysis of SM data from organisations for detecting new trends, obtaining user opinions on their products and services or finding out about their online reputations. A recent research trend in SM analysis is making predictions based on sentiment analysis of SM. Often indicators of historic SM data are represented as time series and correlated with a variety of real world phenomena like the outcome of elections, the development of financial indicators, box office revenue and disease outbreaks. This paper examines the current state of research in the area of SM mining and predictive analysis and gives an overview of the analysis methods using opinion mining and machine learning techniques.

Keywords: Social Media, text mining, knowledge discovery, predictive analysis, machine learning.

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Authors: Mohammad S. Laghari, Qurban A. Memon, Gulzar A. Khuwaja

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The paper describes a knowledge based system for analysis of microscopic wear particles. Wear particles contained in lubricating oil carry important information concerning machine condition, in particular the state of wear. Experts (Tribologists) in the field extract this information to monitor the operation of the machine and ensure safety, efficiency, quality, productivity, and economy of operation. This procedure is not always objective and it can also be expensive. The aim is to classify these particles according to their morphological attributes of size, shape, edge detail, thickness ratio, color, and texture, and by using this classification thereby predict wear failure modes in engines and other machinery. The attribute knowledge links human expertise to the devised Knowledge Based Wear Particle Analysis System (KBWPAS). The system provides an automated and systematic approach to wear particle identification which is linked directly to wear processes and modes that occur in machinery. This brings consistency in wear judgment prediction which leads to standardization and also less dependence on Tribologists.

Keywords: Computer vision, knowledge based systems, morphology, wear particles.

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Authors: Eric Costa, Sara Bragança, Rui Sousa, Anabela Alves

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This paper presents an application of the Single-Minute Exchange of Die (SMED) methodology to a turret punching machine in an elevators company, in Portugal. The work was developed during five months, in the ambit of a master thesis in Industrial Engineering and Management. The Lean Production tool SMED was applied to reduce setup times in order to improve the production flexibility of the machine. The main results obtained were a reduction of 64% in setup time (from 15.1 to 5.4min), 50% in work-in-process amount (from 12.8 to 6.4 days) and 99% in the distance traveled by the operator during the internal period (from 136.7 to 1.7m). These improvements correspond to gains of about €7,315.38 per year.

Keywords: Lean production, setup process, SMED.

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Authors: B. S. Raghavendra, D. Narayana Dutt

Abstract:

Many recent electrophysiological studies have revealed the importance of investigating meditation state in order to achieve an increased understanding of autonomous control of cardiovascular functions. In this paper, we characterize heart rate variability (HRV) time series acquired during meditation using nonlinear dynamical parameters. We have computed minimum embedding dimension (MED), correlation dimension (CD), largest Lyapunov exponent (LLE), and nonlinearity scores (NLS) from HRV time series of eight Chi and four Kundalini meditation practitioners. The pre-meditation state has been used as a baseline (control) state to compare the estimated parameters. The chaotic nature of HRV during both pre-meditation and meditation is confirmed by MED. The meditation state showed a significant decrease in the value of CD and increase in the value of LLE of HRV, in comparison with premeditation state, indicating a less complex and less predictable nature of HRV. In addition, it was shown that the HRV of meditation state is having highest NLS than pre-meditation state. The study indicated highly nonlinear dynamic nature of cardiac states as revealed by HRV during meditation state, rather considering it as a quiescent state.

Keywords: Correlation dimension, Embedding dimension, Heartrate variability, Largest Lyapunov exponent, Meditation, Nonlinearity score.

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Authors: S. Joshi, D. Pant

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Absorption and fluorescence spectra of quinine sulphate (QSD) have been recorded at room temperature in wide range of solvents of different polarities. The ground-state dipole moment of QSD was obtained from quantum mechanical calculations and the excited state dipole moment of QSD was estimated from Bakhshiev-s and Kawski-Chamma-Viallet-s equations by means of solvatochromic shift method. Higher value of dipole moment is observed for excited state as compared to the corresponding ground state value and this is attributed to the more polar excited state of QSD.

Keywords: Dipole moment, Quinine sulphate dication, Solvatochromic shift

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Authors: Marco Soares dos Santos, Jorge Augusto Ferreira, Camila Nicola Boeri, Fernando Neto da Silva

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The present paper proposes high performance nonlinear force controllers for a servopneumatic real-time fatigue test machine. A CompactRIO® controller was used, being fully programmed using LabVIEW language. Fuzzy logic control algorithms were evaluated to tune the integral and derivative components in the development of hybrid controllers, namely a FLC P and a hybrid FLC PID real-time-based controllers. Their behaviours were described by using state diagrams. The main contribution is to ensure a smooth transition between control states, avoiding discrete transitions in controller outputs. Steady-state errors lower than 1.5 N were reached, without retuning the controllers. Good results were also obtained for sinusoidal tracking tasks from 1/¤Ç to 8/¤Ç Hz.

Keywords: Hybrid Fuzzy Selecting, Control, Range Controllers, Servopneumatic Fatigue System.

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Authors: Saleem Z. Ramadan, Gürsel A. Süer

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In this paper a genetic algorithm (GA) with dual-fitness function is proposed and applied to solve the deterministic identical machine scheduling problem. The mating fitness function value was used to determine the mating for chromosomes, while the selection fitness function value was used to determine their survivals. The performance of this algorithm was tested on deterministic identical machine scheduling using simulated data. The results obtained from the proposed GA were compared with classical GA and integer programming (IP). Results showed that dual-fitness function GA outperformed the classical single-fitness function GA with statistical significance for large problems and was competitive to IP, particularly when large size problems were used.

Keywords: Machine scheduling, Genetic algorithms, Due dates, Number of tardy jobs, Number of early jobs, Integer programming, Dual Fitness functions.

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Authors: Himanshu V. Gajjar, Anish H. Gandhi, Tanvir A Jafri, Harit K. Raval

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Bendability is constrained by maximum top roller load imparting capacity of the machine. Maximum load is encountered during the edge pre-bending stage of roller bending. Capacity of 3-roller plate bending machine is specified by maximum thickness and minimum shell diameter combinations that can be pre-bend for given plate material of maximum width. Commercially available plate width or width of the plate that can be accommodated on machine decides the maximum rolling width. Original equipment manufacturers (OEM) provide the machine capacity chart based on reference material considering perfectly plastic material model. Reported work shows the bendability analysis of heavy duty 3-roller plate bending machine. The input variables for the industry are plate thickness, shell diameter and material property parameters, as it is fixed by the design. Analytical models of equivalent thickness, equivalent width and maximum width based on power law material model were derived to study the bendability. Equation of maximum width provides bendability for designed configuration i.e. material property, shell diameter and thickness combinations within the machine limitations. Equivalent thicknesses based on perfectly plastic and power law material model were compared for four different materials grades of C-Mn steel in order to predict the bend-ability. Effect of top roller offset on the bendability at maximum top roller load imparting capacity is reported.

Keywords: 3-Roller bending, Bendability, Equivalent thickness, Equivalent width, Maximum width.

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Authors: Sannikumar Patel, Brian Nolan, Markus Hofmann, Philip Owende, Kunjan Patel

Abstract:

Sentiment analysis and opinion mining have become emerging topics of research in recent years but most of the work is focused on data in the English language. A comprehensive research and analysis are essential which considers multiple languages, machine translation techniques, and different classifiers. This paper presents, a comparative analysis of different approaches for multilingual sentiment analysis. These approaches are divided into two parts: one using classification of text without language translation and second using the translation of testing data to a target language, such as English, before classification. The presented research and results are useful for understanding whether machine translation should be used for multilingual sentiment analysis or building language specific sentiment classification systems is a better approach. The effects of language translation techniques, features, and accuracy of various classifiers for multilingual sentiment analysis is also discussed in this study.

Keywords: Cross-language analysis, machine learning, machine translation, sentiment analysis.

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Authors: Hamidreza Akbari

Abstract:

In this paper, for detection of inclined eccentricity in an induction motor, time–frequency analysis of the stator startup current is carried out. For this purpose, the discrete wavelet transform is used. Data are obtained from simulations, using winding function approach. The results show the validity of the approach for detecting the fault and discriminating with respect to other faults.

Keywords: Induction machine, Fault, DWT.

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Authors: [email protected]

Abstract:

Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data need a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM), ensemble learning with hyper parameters optimization, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.

Keywords: Machine learning, Deep learning, cancer prediction, breast cancer, LSTM, Score-Level Fusion.

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Authors: Mohammad N. Alanazi, David A. Gustafson

Abstract:

In this article, we introduce a new approach for analyzing UML designs to detect the inconsistencies between multiple state diagrams and sequence diagrams. The Super State Analysis (SSA) identifies the inconsistencies in super states, single step transitions, and sequences. Because SSA considers multiple UML state diagrams, it discovers inconsistencies that cannot be discovered when considering only a single UML state diagram. We have introduced a transition set that captures relationship information that is not specifiable in UML diagrams. The SSA model uses the transition set to link transitions of multiple state diagrams together. The analysis generates three different sets automatically. These sets are compared to the provided sets to detect the inconsistencies. SSA identifies five types of inconsistencies: impossible super states, unreachable super states, illegal transitions, missing transitions, and illegal sequences.

Keywords: Modeling Languages, Object-Oriented Analysis, Sequence Diagrams, Software Models, State Diagrams, UML.

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Authors: A. A. Hossam-Eldin, E. N. Abdallah, M. S. El-Nozahy

Abstract:

Power system state estimation is the process of calculating a reliable estimate of the power system state vector composed of bus voltages' angles and magnitudes from telemetered measurements on the system. This estimate of the state vector provides the description of the system necessary for the operation and security monitoring. Many methods are described in the literature for solving the state estimation problem, the most important of which are the classical weighted least squares method and the nondeterministic genetic based method; however both showed drawbacks. In this paper a modified version of the genetic algorithm power system state estimation is introduced, Sensitivity of the proposed algorithm to genetic operators is discussed, the algorithm is applied to case studies and finally it is compared with the classical weighted least squares method formulation.

Keywords: Genetic algorithms, ill-conditioning, state estimation, weighted least squares.

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Authors: Tolulope A. Olukunle

Abstract:

Plastic waste management has emerged as one of the greatest challenges facing developing countries. This paper describes the design of various components of a plastic shredder. This machine is widely used in industries and recycling plants. The introduction of plastic shredder machine will promote reduction of post-consumer plastic waste accumulation and serves as a system for wealth creation and empowerment through conversion of waste into economically viable products. In this design research, a 10 kW electric motor with a rotational speed of 500 rpm was chosen to drive the shredder. A pulley size of 400 mm is mounted on the electric motor at a distance of 1000 mm away from the shredder pulley. The shredder rotational speed is 300 rpm.

Keywords: Design, machine, plastic waste, recycling.

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