Showing papers in "International Journal of Intelligent Systems and Applications in 2013"
TL;DR: Vehicle tracking and locking system installed in the vehicle, to track the place and locking engine motor, and microcontroller issue the control signals to stop the engine motor.
Abstract: Currently almost of the public having an own vehicle, theft is happening on parking and sometimes driving insecurity places. The safe of vehicles is extremely essential for public vehicles. Vehicle tracking and locking system installed in the vehicle, to track the place and locking engine motor. The place of the vehicle identified using Global Positioning system (GPS) and Global system mobile communication (GSM). These systems constantly watch a moving Vehicle and report the status on demand. When the theft identified, the responsible person send SMS to the microcontroller, then microcontroller issue the control signals to stop the engine motor. Authorized person need to send the password to controller to restart the vehicle and open the door. This is more secured, reliable and low cost.
71 citations
TL;DR: Different mathematical models in terms of output position, speed, current, acceleration and torque, as well as corresponding simulink models, supporting MATLAB m.file and general function block models are to be introduced to simplify the process of DC motors sizing, selection, dynamic analysis and evaluation for different motion applications.
Abstract: The accurate control of motion is a fundamental concern in mechatronics applications, where placing an object in the exact desired location with the exact possible amount of force and torque at the correct exact time is essential for efficient system operation. An accurate modeling, simulation and dynamics analysis of actuators for mechatronics motion control applications is of big concern. The ultimate goal of this paper addresses different approaches used to derive mathematical models, building corresponding simulink models and dynamic analysis of the basic open loop electric DC motor system, used in mechatronics motion control applications, particularly, to design, construct and control of a mechatronics robot arm with single degree of freedom, and verification by MATLAB/Simulink. To simplify and accelerate the process of DC motors sizing, selection, dynamic analysis and evaluation for different motion applications, different mathematical models in terms of output position, speed, current, acceleration and torque, as well as corresponding simulink models, supporting MATLAB m.file and general function block models are to be introduced. The introduced models were verified using MATLAB/ Simulink. These models are intended for research purposes as well as for the application in educational process.
56 citations
TL;DR: A free mathematical tunable gain new sliding switching feedback linearization controller applied to robot manipulator is presented to have a stable and robust nonlinear controller and have a good result compared with conventional and pure fuzzy logic controllers.
Abstract: First three degree of six degree of freedom robotic manipulator is controlled by a new fuzzy sliding feedback linearization controller. The robot arm has six revolute joints allowing the corresponding links to move horizontally. When developing a controller using conventional control methodology (e.g., feedback linearization methodology), a design scheme has to be produced, usually based on a system’s dynamic model. The work outline in this research utilizes soft computing applied to new conventional controller to address these methodology issues. Feedback linearization controller (FLC) is influential nonlinear controllers to certain systems which this method is based on compute the required arm torque using nonlinear feedback control law. When all dynamic and physical parameters are known FLC works superbly; practically a large amount of systems have uncertainties and fuzzy feedback linearization controller (FFLC) reduce this kind of limitation. Fuzzy logic provides functional capability without the use of a system dynamic model and has the characteristics suitable for capturing the approximate, varying values found in a MATLAB based area. To increase the stability and robustness new mathematical switching sliding mode methodology is applied to FFLC. Based on this research model free mathematical tunable gain new sliding switching feedback linearization controller applied to robot manipulator is presented to have a stable and robust nonlinear controller and have a good result compared with conventional and pure fuzzy logic controllers.
55 citations
TL;DR: Comparing the different ANN models for time series analysis, it was found that FLANN gives better prediction results as compared to ARIMA model with less Absolute Average Percentage Error (AAPE) for the measured rainfall data.
Abstract: In this paper, ARIMA(1,1,1) model and Artificial Neural Network (ANN) models like Multi Layer Perceptron (MLP), Functional-link Artificial Neural Network (FLANN) and Legendre Polynomial Equation ( LPE) were used to predict the time series data. MLP, FLANN and LPE gave very accurate results for complex time series model. All the Artificial Neural Network model results matched closely with the ARIMA(1,1,1) model with minimum Absolute Average Percentage Error(AAPE). Comparing the different ANN models for time series analysis, it was found that FLANN gives better prediction results as compared to ARIMA model with less Absolute Average Percentage Error (AAPE) for the measured rainfall data.
51 citations
TL;DR: A novel system to classify three types of electrocardiogram beats, namely normal beats and two manifestations of heart arrhythmia, which includes three main modules: a feature extract ion module, a classifier module, and an optimization module.
Abstract: This paper proposes a novel system to classify three types of electrocardiogram beats, namely normal beats and two manifestations of heart arrhythmia. This system includes three main modules: a feature extract ion module, a classifier module, and an optimization module. In the feature ext raction module, a proper set combining the shape features and timing features is proposed as the efficient characteristic of the patterns. In the classifier module, a multi-class support vector machine (SVM)-based classifier is proposed. For the optimizat ion module, a particle swarm optimization algorithm is proposed to search for the best value of the SVM parameters and upstream by looking for the best subset of features that feed the classifier. Simulation results show that the proposed algorithm has very high recognition accuracy. This high efficiency is achieved with only little features, which have been selected using particle swarm optimizer.
44 citations
TL;DR: A new clustering algorithm called Efficient Data Clustering Algorithm (EDCA) is developed that uses a new definition of densities of data points which is based on the k-nearest neighbor method and the results show that the proposed algorithms improve the data clustering by Kmeans.
Abstract: This paper presents a new approach to overcome one of the most known disadvantages of the well-known Kmeans clustering algorithm. The problems of classical Kmeans are such as the problem of random initialization of prototypes and the requirement of predefined number of clusters in the dataset. Randomly initialized prototypes can often yield results to converge to local rather than global optimum. A better result of Kmeans may be obtained by running it many times to get satisfactory results. The proposed algorithms are based on a new novel definition of densities of data points which is based on the k-nearest neighbor method. By this definition we detect noise and outliers which affect Kmeans strongly, and obtained good initial prototypes from one run with automatic determination of K number of clusters. This algorithm is referred to as Efficient Initialization of Kmeans (EI- Kmeans). Still Kmeans algorithm used to cluster data with convex shapes, similar sizes, and densities. Thus we develop a new clustering algorithm called Efficient Data Clustering Algorithm (EDCA) that uses our new definition of densities of data points. The results show that the proposed algorithms improve the data clustering by Kmeans. EDCA is able to detect clusters with different non-convex shapes, different sizes and densities.
44 citations
TL;DR: The experimental results show that the proposed initialization method is more effective and converges to more accurate clustering results than those of the random initialization method and ability to identify the true clusters is the performance evaluation standard in this research.
Abstract: Partition-based clustering technique is one of several clustering techniques that attempt to directly decompose the dataset into a set of disjoint clusters. K- means algorithm dependence on partition-based clustering technique is popular and widely used and applied to a variety of domains. K-means clustering results are extremely sensitive to the initial centroid; this is one of the major drawbacks of k-means algorithm. Due to such sensitivity; several different initialization approaches were proposed for the K-means algorithm in the last decades. This paper proposes a selection method for initial cluster centroid in K-means clustering instead of the random selection method. Research provides a detailed performance assessment of the proposed initialization method over many datasets with different dimensions, numbers of observations, groups and clustering complexities. Ability to identify the true clusters is the performance evaluation standard in this research. The experimental results show that the proposed initialization method is more effective and converges to more accurate clustering results than those of the random initialization method.
39 citations
39 citations
TL;DR: A chaos-ANFIS approach is presented for analysis of EEG signals for epileptic seizure recognition, and the inter-ictal EEG based diagnostic approach achieves 97.4% accuracy with using 4-fold cross validation.
Abstract: A chaos-ANFIS approach is presented for analysis of EEG signals for epileptic seizure recognition. The non-linear dynamics of the original EEGs are quantified in the form of the hurst exponent (H) and largest lyapunov exponent (λ). The process of EEG analysis consists of two phases, namely the qualitative and quantitative analysis. The classification ability of the H and λ measures is tested using ANFIS classifier. This method is evaluated with using a benchmark EEG dataset, and qualitative and quantitative results are presented. Our inter-ictal EEG based diagnostic approach achieves 97.4% accuracy with using 4-fold cross validation. Diagnosis based on ictal data is also tested in ANFIS classifier, reaching 96.9% accuracy. Therefore, our method can be successfully applied to both inter-ictal and ictal data.
39 citations
TL;DR: This paper expands a Multi Input Multi Output (MIMO) fuzzy baseline variable structure control (VSC) which controller coefficient is off-line tuned by gradient descent algorithm which provides an optimal setting for other factors which crated by PID baseline method.
Abstract: This paper expands a Multi Input Multi Output (MIMO) fuzzy baseline variable structure control (VSC) which controller coefficient is off-line tuned by gradient descent algorithm. The main goal is to adjust the optimal value for fuel ratio (FR) in motor engine. The fuzzy inference system in proposed methodology is works based on Mamdani-Lyapunov fuzzy inference system (FIS). To reduce dependence on the gain updating factor coefficients of the fuzzy methodology, PID baseline method is introduced. This new method provides an optimal setting for other factors which crated by PID baseline method. The gradient descent methodology is off-line tune all coefficients of baseline fuzzy and variable structure function based on mathematical optimization methodology. The performance of proposed methodology is validated through comparison with fuzzy variable structure methodology (FVSC). Simulation results signify good performance of fuel ratio in presence of different torque load and external disturbance.
37 citations
TL;DR: Simulation results prove that the optimized performance obtained through CCA significantly reduces the chattering phenomena and results in better trajectory tracking compared to typical trial and error methods.
Abstract: One of the best nonlinear robust controllers which can be used in uncertain nonlinear systems is sliding mode controller (SMC), but pure SMC results in chattering in a noisy environment. This effect can be eliminated by optimizing the sliding surface slope. This paper investigates a novel methodology in designing a SMC by a new heuristic search, so called ―colonial competitive algorithm ―in order to tune the sliding surface slope and the switching gain of the discontinuous part in SMC structure. This process decreases the integral of absolute errors which results in tracking the desired inputs by the outputs in designing a controller for robot manipulator. Simulation results prove that the optimized performance obtained through CCA significantly reduces the chattering phenomena and results in better trajectory tracking compared to typical trial and error methods.
TL;DR: A modified particle swarm optimization (MPSO) mechanis m is proposed to deal with the equality and inequality constraints in the ELD problems through the application of Gaussian and Cauchy probability distributions and shows the efficiently and robustness when compared to other optimization algorithms reported in literature.
Abstract: This paper presents a new approach for solution of the economic load dispatch (ELD) problem with valve-point effect using a modified particle swarm optimization (MPSO) technique. The practical ELD problems have non-smooth cost function with equality and inequality constraints, which make the problem of finding the global optimum d ifficult when using any mathematical approaches. In this paper, a modified particle swarm optimization (MPSO) mechanis m is proposed to deal with the equality and inequality constraints in the ELD problems through the application of Gaussian and Cauchy probability distributions. The MPSO approach introduces new diversification and intensification strategy into the particles thus preventing PSO algorithm from premature convergence. To demonstrate the effectiveness of the proposed approach, the numerical studies have been performed for three different test systems, i.e. six, th irteen and forty generating unit systems, respectively. The results shows that performance of the proposed approach reveal the efficiently and robustness when compared results of other optimization algorithms reported in literature.
TL;DR: The comparison of the three fundamental image segmentation methods based on fuzzy logic namely Fuzzy C-Means (FCM), Intuitionistic FuzzY C- means (IFCM, and Type-II Fuzzed C-means (T2FCM) is presented in this paper.
Abstract: Medical image segmentation demands a segmentation algorithm which works against noise. The most popular algorithm used in image segmentation is Fuzzy C-Means clustering. It uses only intensity values for clustering which makes it highly sensitive to noise. The comparison of the three fundamental image segmentation methods based on fuzzy logic namely Fuzzy C-Means (FCM), Intuitionistic Fuzzy C-Means (IFCM), and Type-II Fuzzy C-Means (T2FCM) is presented in this paper. These algorithms are executed in two scenarios- both in the absence and in the presence of noise and on two kinds of images- Bacteria and CT scan brain image. In the bacteria image, clustering differentiates the bacteria from the background and in the brain CT scan image, clustering is used to identify the abnormality region. Performance is analyzed on the basis cluster validity functions, execution time and convergence rate. Misclassification error is also calculated for brain image analysis.
TL;DR: The asymptotic stability of fuzzy plus PID control methodology with first-order computed fuel ratio estimation in the parallel structure is proven and the finite time convergence with a super-twisting second-order sliding- mode is guaranteed.
Abstract: Both fuzzy logic and computed fuel ratio can compensate the steady-state error of proportional- derivative (PD) method. This paper presents parallel computed fuel ratio compensation for fuzzy plus PID control management with application to internal combustion (IC) engine. The asymptotic stability of fuzzy plus PID control methodology with first-order computed fuel ratio estimation in the parallel structure is proven. For the parallel structure, the finite time convergence with a super-twisting second-order sliding- mode is guaranteed.
TL;DR: The results demonstrate that the error-based linear SMC and CTC are model-based controllers which works well in certain and uncertain system and have acceptable performance in presence of uncertainty.
Abstract: Design a nonlinear controller for second order nonlinear uncertain dynamical systems (e.g., internal combustion engine) is one of the most important challenging works. This paper focuses on the comparative study between two important nonlinear controllers namely; computed torque controller (CTC) and sliding mode controller (SMC) and applied to internal combustion (IC) engine in presence of uncertainties. In order to provide high performance nonlinear methodology, sliding mode controller and computed torque controller are selected. Pure SMC and CTC can be used to control of partly known nonlinear dynamic parameters of IC engine. Pure sliding mode controller and computed torque controller have difficulty in handling unstructured model uncertainties. To solve this problem applied linear error-based tuning method to sliding mode controller and computed torque controller for adjusting the sliding surface gain ( ) and linear inner loop gain ( ). Since the sliding surface gain ( ) and linear inner loop gain ( ) are adjusted by linear error-based tuning method. In this research new and new are obtained by the previous and multiple gains updating factor . The results demonstrate that the error-based linear SMC and CTC are model-based controllers which works well in certain and uncertain system. These controllers have acceptable performance in presence of uncertainty.
TL;DR: This paper has prepared pseudo random numbers by Lorenz chaotic system for operators of Genetic Algorithm to avoid local convergence and shows that the proposed method is much more efficient in comparison with the traditional Genetic Al algorithm for solving optimization problems.
Abstract: Very recently evolutionary optimization algorithms use the Genetic Algorithm to improve the result of Optimization problems. Several processes of the Genetic Algorithm are based on 'Random', that is fundamental to evolutionary algorithms, but important defections in the Genetic Algorithm are local convergence and high tolerances in the results, they have happened for randomness reason. In this paper we have prepared pseudo random numbers by Lorenz chaotic system for operators of Genetic Algorithm to avoid local convergence. The experimental results show that the proposed method is much more efficient in comparison with the traditional Genetic Algorithm for solving optimization problems.
TL;DR: Instead of using weight for criteria, this paper considers entropy weights for sub-criteria in interval type-2 fuzzy TOPSIS method.
Abstract: Fuzzy Technique for Order Preference by Similarly to Ideal Solution (TOPSIS) is one of the most commonly used approaches in solving numerous multiple criteria decision making problems. It has been widely used in ranking of multiple alternatives with respect to multiple criteria with the superiority of fuzzy set type-1 and subjective weights. Recently, fuzzy TOPSIS has been merged with interval type-2 fuzzy sets and subjective weights for criteria as to handle the wide arrays of vagueness and uncertainty. However, the role of objective weights in this new interval type-2 fuzzy TOPSIS has given considerably less attention. This paper aims to propose a new objective weight for sub-criteria in interval type-2 fuzzy TOPSIS. Instead of using weight for criteria, this paper considers entropy weights for sub-criteria in interval type-2 fuzzy TOPSIS method. An example of supplier selection is used to illustrate the proposed method.
TL;DR: A novel heuristic optimization method to solve complex economic load dispatch problem using a hybrid method based on particle swarm optimization (PSO) and gravitational search algorithm (GSA) that combines the social thinking feature in PSO with the local search capability of GSA is presented.
Abstract: This paper presents a novel heuristic optimization method to solve complex economic load dispatch problem using a hybrid method based on particle swarm optimization (PSO) and gravitational search algorithm (GSA). This algorithm named as hybrid PSOGSA combines the social thinking feature in PSO with the local search capability of GSA. To analyze the performance of the PSOGSA algorithm it has been tested on four different standard test cases of different dimensions and complexity levels arising due to practical operating constraints. The obtained results are compared with recently reported methods. The comparison confirms the robustness and efficiency of the algorithm over other existing techniques.
TL;DR: The design, model and control of Mechatronics mobile robotic system is presented and it is intended for research purposes as well as for educational process.
Abstract: Mobile robot motion control is simplified to a DC motor motion control that may include gear system. The simplest and widespread approach to control the mobile robot motion is the differential drive style, it consists of two in-lines with each a DC motor. Both DC motors are independently powered so the desired movements will rely on how these two DC motors are commanded. Thedevelop design, model and control of Mechatronics mobile robotic system is presented in this paper. The developed robotic system is intended for research purposes as well as for educational process. The model of proposed mobile robot was created and verified using MATLAB- Simulink software.
TL;DR: The new techniques proposed and methodologies adopted in this paper supported by MATLAB/SIMULINK results represent a significant contribution to the field of design an optimized nonlinear computed torque controller for continuum robots.
Abstract: Refer to this research, a gradient descent optimization methodology for position fuzzy- model based computed torque controller (GDFCTC) is proposed for highly nonlinear continuum robot manipulator. The main problem of the pure computed torque controller (CTC) was equivalent problem in uncertain systems. The simulation results exhibit that the CTC works well in certain system. To eliminate the continuum robot manipulator system's dynamic; Mamdani fuzzy inference system is design and applied to CTC. This methodology is based on applied fuzzy logic in equivalent nonlinear dynamic part to estimate unknown parameters. This relatively controller is more plausible to implement in an actual real-time when compared to other techniques of nonlinear controller methodology of continuum arms. Based on the gradient descent optimization method, the PD-gain updating factor has been developed in certain and partly uncertain continuum robots. The new techniques proposed and methodologies adopted in this paper supported by MATLAB/SIMULINK results represent a significant contribution to the field of design an optimized nonlinear computed torque controller for continuum robots.
TL;DR: A underwater sensor network routing protocol named Energy Efficient Fitness based routing protocol for under water sensor networks (EEF) that promises the best use of total energy consumptions and higher network life time is proposed.
Abstract: Underwater sensor network is one of the potential research arenas that opens the window of pleasing a lot of researcher in studying the field. Network layer of the underwater sensor networks must be one of the most attractive fields to build up anew protocol. In this paper, we proposed a underwater sensor network routing protocol named Energy Efficient Fitness based routing protocol for under water sensor networks (EEF) that promises the best use of total energy consumptions. The proposed routing protocol takes into account residual energy, depth and distance from the forwarding node to the sink node to guide a packet from source to the destination node. The prominent advantages of the proposed protocol are to confirm higher network life time and less end to end delay. The proposed protocol does not use control packet that causes much energy consumption and end to end delay. Simulation has been performed to certify the better result of the proposed routing protocol.
TL;DR: Algorithms are developed for the Rayleigh and Rician fading channels, which computes the envelop and outage probability, which play very important role in the performance analysis and design of the digital communication systems over the multipath fading environment.
Abstract: An effort has been made to illustrate the performance comparison of the Rayleigh and Rician fading channel models by using MATLAB simulation in terms of source velocity and outage probability. We have developed algorithms for the Rayleigh and Rician fading channels, which computes the envelop and outage probability. The parameters such as source velocity and outage probability play very important role in the performance analysis and design of the digital communication systems over the multipath fading environment.
TL;DR: An Automatic Ration Materials Distribution Based on GSM and RFID technology instead of ration cards is proposed, which provides the materials automatically without help of humans.
Abstract: Now a day ration card is very important for every home and used for various field such as family members details, to get gas connection, it act as address proof for various purposes etc. All the people having a ration card to buy the various materials (sugar, rice, oil, kerosene, etc) from the ration shops. But in this system having two draw backs, first one is weight of the material may be inaccurate due to human mistakes and secondly, if not buy the materials at the end of the month, they will sale to others without any intimation to the government and customers. In this paper, proposed an Automatic Ration Materials Distribution Based on GSM (Global System for Mobile) and RFID (Radio Frequency Identification) technology instead of ration cards. To get the materials in ration shops need to show the RFID tag into the RFID reader, then controller check the customer codes and details of amounts in the card. After verification, these systems show the amount details. Then customer need to enter they required materials by using keyboard, after receiving materials controller send the information to government office and customer through GSM technology. In this system provides the materials automatically without help of humans.
TL;DR: A study using the cuckoo search algorithm to evaluate the effects of using computer-aided workstations on employee health and safety (HS) is conducted, and the NSCS algorithm is employed for the calculation of an HS risk index.
Abstract: A study using the cuckoo search algorithm to evaluate the effects of using computer-aided workstations on employee health and safety (HS) is conducted. We collected data for HS risk on employees at their workplaces, analyzed the data and proposed corrective measures applying our methodology. It includes a checklist with nine HS dimensions: work organization, displays, input devices, furniture, work space, environment, software, health hazards and satisfaction. By the checklist, data on HS risk factors are collected. For the calculation of an HS risk index a neural-swarm cuckoo search (NSCS) algorithm has been employed. Based on the HS risk index, IHS four groups of HS risk severity are determined: low, moderate, high and extreme HS risk. By this index HS problems are allocated and corrective measures can be applied. This approach is illustrated and validated by a case study. An important advantage of the approach is its easy use and HS index methodology speedily pointing out individual employee specific HS risk.
TL;DR: This paper examines single input single output (SISO) chattering free variable structure control (VSC) which controller coefficient is on-line tuned by fuzzy backstepping algorithm to control of continuum robot manipulator.
Abstract: This paper examines single input single output (SISO) chattering free variable structure control (VSC) which controller coefficient is on-line tuned by fuzzy backstepping algorithm to control of continuum robot manipulator. Variable structure methodology is selected as a framework to construct the control law and address the stability and robustness of the close loop system based on Lyapunove formulation. The main goal is to guarantee acceptable error result and adjust the trajectory following. The proposed approach effectively combines the design technique from variable structure controller is based on Lyapunov and modified Proportional plus Derivative (P+D) fuzzy estimator to estimate the nonlinearity of undefined system dynamic in backstepping controller. The input represents the function between variable structure function, error and the modified rate of error. The outputs represent joint torque, respectively. The fuzzy backstepping methodology is on-line tune the variable structure function based on adaptive methodology. The performance of the SISO VSC based on-line tuned by fuzzy backstepping algorithm (FBSAVSC) is validated through comparison with VSC. Simulation results signify good performance of trajectory in presence of uncertainty joint torque load.
TL;DR: This research addresses two basic issues related to the control of a continuum robots; a more accurate representation of the dynamic model of an existing prototype, and the design of a robust feedback controller that is used to position control of continuum robot in presence of uncertainties.
Abstract: The increasing demand for multi-degree-of- freedom (DOF) continuum robot in presence of highly nonlinear dynamic parameters in a number of industries has motivated a flurry of research in the development of soft computing nonlinear methodology This robot is capable of providing smooth and isotropic three- dimensional motion in each joint Compared to conventional robotic manipulators that offer the same motion capabilities, the innovative spherical motor possesses several advantages Not only can the spherical motor combine 3-DOF motion in a single joint, it has a large range of motion with no singularities in its workspace This research contributes to the on-going research effort by exploring alternate methods for controlling the continuum robot manipulator This research addresses two basic issues related to the control of a continuum robots; (1) a more accurate representation of the dynamic model of an existing prototype, and (2) the design of a robust feedback controller The robust backstepping controller proposed in this research is used to further demonstrate the appealing features exhibited by the continuum robot Robust feedback controller is used to position control of continuum robot in presence of uncertainties Using Lyapunov type stability arguments, a robust backstepping controller is designed to achieve this objective The controller developed in this research is designed in two steps Firstly, a robust stabilizing torque is designed for the nominal continuum robot dynamics derived using the constrained Lagrangian formulation Next, the fuzzy logic methodology applied to it to solution uncertainty problem The fuzzy model free problem is formulated to minimize the nonlinear formulation of continuum robot The eventual stability of the controller depends on the torque generating capabilities of the continuum robots
TL;DR: In this article, the spontaneous heating risks of some of the Indian coals covering few major coalfields were assessed using Crossing Point temperature (CPT) apparatus and it was found that the Mixture Surface Regression (MSR) model was more effective and gave very good residual values as compared to the polynomial and simple multiple regression models.
Abstract: Spontaneous combustion of coals leading to mine fires is a major problem in Indian coal mines that creates serious safety and mining risk. A number of experimental techniques based on petrological, thermal and oxygen avidity studies have been used for assessing the spontaneous heating liability of coals all over the world. Crossing point temperature (CPT) is one of the most common methods in India to assess the fire risk of coal so that appropriate strategies and effective action plans could be made in advance to prevent occurrence and spread of fire and hence minimize coal loss. In this paper, the spontaneous heating risks of some of the Indian coals covering few major coalfields were assessed using CPT apparatus. Statistical analysis was carried out between CPT and the proximate analysis parameters and it was found that the Mixture Surface Regression (MSR) model was more effective and gave very good residual values as compared to the polynomial and simple multiple regression models. The performance of Anderson-Darling testing was done between the prediction results of MSR model and measured value of CPT showed that the residual follows normal distribution hence justifies the suitability of model for the prediction of spontaneous heating liability of coal.
TL;DR: A new definition of cardinality is proposed which is rooted in the definition of complementation of fuzzy sets on the basis of reference function and it is found that these properties are somewhat analogus to those obtained with the help of the existing definition.
Abstract: In this article, we would like to revisit and comment on the widely used definition of cardinality of fuzzy sets. For this purpose we have given a brief description of the history of development of fuzzy cardinality. In the process, we can find that the existing definition fails to give a proper cardinality while dealing with complementation of fuzzy sets. So there arises the need of defining the cardinality in a different manner. Here a new definition of cardinality is proposed which is rooted in the definition of complementation of fuzzy sets on the basis of reference function. This definition of cardinality will inevitably play an important role in any problem area that involves complementation. Further, some important results are proven with the help of the proposed definition and it is found that these properties are somewhat analogus to those obtained with the help of the existing definition.
TL;DR: DPSO approach is used for the optimal placement and sizing of distributed generations and capacitors in distribution systems for simultaneous voltage profile improvement, loss and total harmonic distortion (THD) reduction.
Abstract: Presence of distributed generation (DG) in distribution systems has significant impacts on the operational characteristics of these systems, also using capacitor for reactive compensation and loss reduction is so common. Injected harmonic currents from non- linear loads into distribution system distort all of voltages and currents and must be considered when placing the capacitor banks so that the resonance will not occur. In this paper discrete particle swarm optimization (DPSO) approach is used for the optimal placement and sizing of distributed generations and capacitors in distribution systems for simultaneous voltage profile improvement, loss and total harmonic distortion (THD) reduction. There is a term in the objective function which prevents harmonic resonance between capacitor reactance and system reactance. Constraints include voltage limit, voltage THD, number/ size of capacitors and generators. For evaluating the proposed algorithm, the IEEE 33-bus test system is modified and employed.
TL;DR: In this paper the simulation and analysis of the PV panel and also high efficient boost converter design and simulation is performed and the input voltage to the boost converter is given as 15V and receives the output voltage of 55.64V.
Abstract: As per the present scenario lot of power shortages are there in all over the world especially country like India the grid transferring problem is also high. Almost the power from the fossil fuels are becoming so less some of the examples of the fossil fuels are (coal, lignite, oil, and gases).So most of them looking in forward for the power from green or renewable based energies like solar, wind, biomass, tidal etc. Which does not cause any pollution to the environment. In this paper the simulation and analysis of the PV panel and also high efficient boost converter design and simulation is also performed. Even though the solar based systems are renewable based energies when compared to other renewable energies like wind, biomass it does not connect to more number of grid connections. Lot of necessary steps want to be taken one of the main important factor that high efficient boost converter is needed, here in this paper the input voltage to the boost converter is given as 15V and receives the output voltage of 55.64V