International Journal of Intelligent Systems and Applications @ijisa
Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1203
Traditional PI and FPIL based VFCU for an isolated induction generator (IIG)
Статья научная
With the increase in load, the AC terminal voltage and frequency at the consumer premises decreases. Hence, there is encountered a need of some simple and intelligent controller which can support both voltage and frequency under load conditions. This manuscript emphasis on implementation and testing of traditional PI and non- traditional fuzzy PI logic (FPIL) based voltage and frequency controller unit (VFCU) in MATLAB based SIMULINK environment for an IIG for step change in consumer load. The electronic Controller comprises of various elements: a 3-leg diode bridge rectifier, chopper as a switch, dc filtering capacitor, discrete PI controller block, fuzzy logic controller block, and a resistive type dump load. The transient and the steady state behavior of IIG-VFCU system is examined and tested under different operating conditions to illustrate the efficaciousness of intelligent controller as compared to traditional PI controller. The performance enhancement of IIG is attained relating to rise time, settling time, overshoot and THD values using the planned intelligent controller.
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Trajectory Tracking of Linear Inverted Pendulum Using Integral Sliding Mode Control
Статья научная
This paper considers the trajectory tracking control of linear inverted pendulum (IP) system. First the linearized model of IP is derived to facilitate the control design. To avoid non robust reaching phase, integral sliding mode control (ISMC) has been proposed but single variable case is tested. Linear IP is a multivariable system having angle of pendulum and position of cart are two variables to be controlled. In control design, the LQR control is designed as a nominal control to get the desired trajectory. Then discontinuous control using integral sliding mode(ISM) is introduced to get desired trajectory tracking in the presence of uncertainties. This control is robust to the model uncertainties and disturbances during entire motion of the states. The simulation results are presented to show the effectiveness of proposed control scheme. The results are compared with LQR control to show the integral sliding mode control is having better tracking performance in the presence of uncertainties.
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Transient Stability Assessment using Decision Trees and Fuzzy Logic Techniques
Статья научная
Many techniques are used for Transient Stability assessment (TSA) of synchronous generators encompassing traditional time domain state numerical integration, Lyapunov based methods, probabilistic approaches and Artificial Intelligence (AI) techniques like pattern recognition and artificial neural networks. This paper examines another two proposed artificial intelligence techniques to tackle the transient stability problem. The first technique is based on the Inductive Inference Reasoning (IIR) approach which belongs to a particular family of machine learning from examples. The second presents a simple fuzzy logic classifier system for TSA. Not only steady state but transient attributes are used for transient stability estimation so as to reflect machine dynamics and network changes due to faults. The two techniques are tested on a standard test power system. The performance evaluation demonstrated satisfactory results in early detection of machine instability. The advantage of the two techniques is that they are straightforward and simple for on-line implementation.
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Translation movement stability control of quad tiltrotor using LQR and LQG
Статья научная
Quadrotor as one type of UAV (Unmanned Aerial Vehicle) is a system that underactuated. It means that the system has a signal control amount is lower than the degrees of freedom or DOF (Degree Of Freedom). This condition causes the quadrotor have limited mobility. If quadrotor is made to have 6 DOF or more (overactuated system), the motion control system to optimise the flight will be different from before. We need to develop overactuated quadrotor control. Quadtiltrotor as the development of quadrotor has some control signal over its DOF. So we call it as an overactuated system. Based on the type of manoeuvre to do, the transition process when the quad tiltrotor performs a translational motion using the tilting rotor need special treatment. The tilt angle change is intended that the quad tiltrotor can perform translational motion while still maintaining its orientation angle near 0°. This orientation angle can change during the undesirable rotational movement as the effect of the transition process. If additional rotational movements cannot be damped, the quad tiltrotor can experience multi overshoot, steady-state error, or even fall. Because of this matter, we need to develop flight control system to handle it. The flight control system of quad tiltrotor can be designed using a model of the system. Models can be created using quad tiltrotor dynamics by the Newton-Euler approach. Then the model is simulated along with the control system using the method of control. Several control methods can be utilised in a quad tiltrotor flight systems. However, with the implementation of LQG control method and Integrator, optimal translational control of the quad tiltrotor can be achieved.
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Trust Based Resource Selection in Cloud Computing Using Hybrid Algorithm
Статья научная
Cloud computing is experiencing rapid advancement in academia and industry. This technology offers distributed, virtualized and elastic resources as utilities for end users and can support full recognition of “computing as a utility” in the future. Scheduling distributes resources among parties which simultaneously and asynchronously seek it. Scheduling algorithms are meant for scheduling and they reduce resource starvation ensuring fairness among those using resources. Most Task-scheduling cloud computing procedures consider task resource requirements for CPU and memory, and not bandwidth. This study suggests optimizing scheduling with BAT-Harmony search hybrid algorithm.
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Two Approaches Based on Genetic Algorithm to Generate Short Iris Codes
Статья научная
This paper has the following contributions in iris recognition compass: first, novel parameters selection for Gabor filters to extract the iris features. Second, due to iris textures randomness and assigning the Gabor parameters by pre-knowledgeable values, traditionally, a large Gabor filter bank has been used to prevent losing the discriminative information. It leads to perform extracting and matching the features heavily and on the other hand, the generated feature vectors are lengthened as required for extra storage space. We have proposed and compared two different approaches based on Genetic Algorithm to reduce the system complexity: optimizing the Gabor parameters and feature selection. Third, proposing a novel encoding strategy based on the texture variations to generate compact iris codes. The experimental results show that generated iris codes by optimizing the Gabor parameters approach is more distinctive and compact than ones based on feature selection approach.
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UWB Cooperatif Radar for Localization and Communication Dedicated to Guided Transport
Статья научная
Wireless technology for communication and localization in train applications are widely used. Ultra wide band appears as a very suitable technology for this kind of application, due to its large bandwidth, also to its good resistance to the interference and to multipath. In this paper, a new system dedicated to railway transport, based on UWB technology is presented. The originality of this study is combination of the two main functionalities, localization and communication providing a high data rate. The sensor, in order to detect the position of vehicles, uses a matched digital correlation receiver. To allow a multi user access and to combine the two functionalities, two original multiplexing techniques called SSS2 (Sequential Spreading Spectrum technique) and CPM (Code Position Modulation) are performed, in addition to other parameters like used waveform and orthogonal codes.
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Статья научная
This paper has proposed new materials based conventional arrayed waveguide grating (AWG) devices such as pure silica glass (SiO2), Lithium niobate (LiNbO3) , and gallium aluminum arsenide (Ga(1-x)Al(x)As) materials for multiplexing and demultiplexing applications in interval of 1.45 μm to 1.65 μm wavelength band, which including the short, conventional, long, and ultra long wavelength band. Moreover we have taken into account a comparison between these new materials within operating design parameters of conventional AWG devices such as diffraction order, length difference of adjacent waveguides, focal path length, free spectral range or region, maximum number of input/output wavelength channels, and maximum number of arrayed waveguides. As well as we have employed these materials based AWG to include Multi band applications under the effect of ambient temperature variations.
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Uncovering Brain Chaos with Hypergraph-Based Framework
Статья научная
The scientist has proven that the birth of neurons in a region of adult rat brain migrates from their birthplace to other parts of the brain. The same process also happens in adult humans. There was no efficient visualization tool to view the functions and structures of the human brain. In this paper, we focus to design a framework to understand more about Alzheimer’s disease and its process of neurons in the human brain. This framework named a hypergraph-based neuron reconstruction framework. It helped to map, the birth and death of neurons with the construction and reconstruction of the hypergraph. This framework also recognizes the structural changes during the life cycle of the neuron. Its performance was evaluated quantitatively with small-world networks and robust connectivity measures.
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Статья научная
Supply chain fraud, a persistent issue over the decades, has seen a significant rise in both prevalence and sophistication in recent years. In the current landscape of supply chain management, the increasing complexity of fraudulent activities demands the use of advanced analytical tools. Despite numerous studies in this domain, many have fallen short in exploring the full extent of recent developments. Thus, this paper introduces an innovative deep learning-based classification model specifically designed for fraud detection in supply chain analytics. To enhance the model's performance, hyperparameters are fine-tuned using Bayesian optimization techniques. To manage the challenges posed by high-dimensional data, Principal Component Analysis (PCA) is applied to streamline data dimensions. In order to address class imbalance, the SMOTE technique has been employed for oversampling the minority class of the dataset. The model's robustness is validated through evaluation on the well-established 'DataCo smart supply chain for big data analysis' dataset, yielding impressive results. The proposed approach achieves a 94.71% fraud detection rate and an overall accuracy of 99.42%. Comparative analysis with various other models highlights the significant improvements in fraud transaction detection achieved by this approach. While the model demonstrates high accuracy, it may not be directly transferable to more diverse or real-world datasets. As part of future work, the model can be tested on more varied datasets and refined to enhance generalizability, better aligning it with real-world scenarios. This will include addressing potential overfitting to the specific dataset used and ensuring further validation across different environments to confirm the model's robustness and generalizability.
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Urinary System Diseases Diagnosis Using Machine Learning Techniques
Статья научная
The urinary system is the organ system responsible for the production, storage and elimination of urine. This system includes kidneys, bladder, ureters and urethra. It represents the major system which filters the blood and any imbalance of this organ can increases the rate of being infected with diseases. The aim of this paper is to evaluate the performance of different variants of Support Vector Machines and k-Nearest Neighbor with different distances and try to achieve a satisfactory rate of diagnosis (infected or non-infected urinary system). We consider both diseases that affect the urinary system: inflammation of urinary bladder and nephritis of renal pelvis origin. Our experimentation will be conducted on the database “Acute Inflammations Data Set” obtained from UCI Machine Learning Repository. We use the following measures to evaluate the results: classification accuracy rate, classification time, sensitivity, specificity, positive and negative predictive values.
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Статья научная
For more relevant informational retrieval and matching of user request with metadata about biomedical informational recourses it is necessary to formulize the user knowledge about this subject domain. We propose to use the ontologies and associated with them thesauri of the appropriate subject domains for representation of biomedicine knowledge. The algorithms of formation and normalization of the multilinguistic thesauruses, and also methods of their comparison are given in this work.
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Using Artificial Immune Recognition Systems in Order to Detect Early Breast Cancer
Статья научная
In this work, a decision support system for early breast cancer detection is presented. In hard to diagnose cases, different examinations (i.e. mammography, ultrasonography and magnetic resonance imaging) provide contradictory findings and patient is guided to biopsy for definite results. The proposed method employs a Correlation Feature Selection procedure and an Artificial Immune Recognition System (AIRS) and is evaluated using real data collected from 53 subjects with contradictory diagnoses. Comparative results with commonly used artificial intelligence classifiers verify the suitability of the AIRS classifier. The application of such an approach can reduce the number of unnecessary biopsies.
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Using Description Logics to specify a Document Synthesis System
Статья научная
This paper deals with an automatic document’s synthesis system. Our approach is based on the prior formal description of the semantics of the main elements (document, reader and his request) in the synthesis system. In this approach, semantic capture is based on ontology definition that is specified formally using Description Logics (DL). The DL inference techniques associated to production rules are then used to compute a document synthesis. Moreover, DL inference techniques are used to reason about each component.
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Using Fuzzy Models and Time Series Analysis to Predict Water Quality
Статья научная
Water quality prediction is very important for both water resource scheduling and management. Simple linear regression analysis and artificial neural network models cannot accurately forecast water quality because of complicated linear and nonlinear relationships in the water quality dataset. An adaptive neuro-fuzzy inference system (ANFIS) that can integrate linear and nonlinear relationships has been proposed to address the problem. However, the ANFIS model can only work in scenarios where input and target parameters have strong correlations. In this paper, a fuzzy model integrated with a time series data analysis method is proposed to address the water quality prediction problem when the correlation between the input and target parameters is weak. The water quality datasets collected from the Las Vegas Wash between the years 2005 and 2010, and the Boulder Basin, Nevada-Arizona from the years 2011 to 2016 are used to test the proposed model. The prediction accuracy of the proposed model is measured by three different statistical indices: mean average percentage error, root mean square error, and coefficient of determination. The experimental results have proven that the ANFIS model combined with a time series analysis method achieves the best prediction accuracy for predicting electrical conductivity and total dissolved solids in the Las Vegas Wash, with the testing value of coefficient of determination reaching 0.999 and 0.997, respectively. The fuzzy time series analysis has the best performance for dissolved oxygen and electrical conductivity prediction in the Boulder Basin, and dissolved oxygen prediction in the Las Vegas Wash, with testing value of coefficients of determination equal to 0.990, 90975, and 0.960, respectively.
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Using Heuristic-based Search for Zinc Models
Статья научная
The Zinc modelling language provides a rich set of constraints, data structures and expressions to support high-level modelling. Zinc is the only modelling language that supports all solving techniques: constraint programming, mathematical methods, and local search. By providing search patterns, it allows users to implement their search methods in a declarative way. There are currently three search patterns implemented in Zinc: backtracking search, branch and bound search, and local search. In this paper we explain how Zinc efficiently implements user-defined local search algorithms.
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Статья научная
The current Learning Management Systems used in e-learning lack intelligent mechanisms which can be used by an instructor to group learners during an online group task based on the learners' collaboration competence level. In this paper, we discuss a novel approach for grouping students in an online learning group task based on individual learners' collaboration competence level. We demonstrate how it can be applied in a Learning Management System such as Moodle using forum data. To create the collaboration competence levels, two machine learning algorithms for clustering namely Skmeans and Expectation Maximization (EM) were applied to cluster data and generate clusters based on learner's collaboration competence. We develop an intelligent grouping algorithm which utilizes these machine learning generated clusters to form heterogeneous groups. These groups are automatically made available to the instructor who can proceed to assign them to group tasks. This approach has the advantage of dynamically changing the group membership based on learners' collaboration competence level.
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Using Rough Set Theory for Reasoning on Vague Ontologies
Статья научная
Web ontologies can contain vague concepts, which means the knowledge about them is imprecise and then query answering will not possible due to the open world assumption. A concept description can be very exact (crisp concept) or exact (fuzzy concept) if its knowledge is complete, otherwise it is inexact (vague concept) if its knowledge is incomplete. In this paper, we propose a method based on the rough set theory for reasoning on vague ontologies. With this method, the detection of vague concepts will insert into the original ontology new rough vague concepts where their description is defined on approximation spaces to be used by extended Tableau algorithm for automatic reasoning. A prototype of Tableau's extended algorithm is developed and tested on examples where encouraging results are given by this method to demonstrate that unlike other methods, it is possible to answer queries even in the presence of incomplete information.
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Using the Euler-Maruyama Method for Finding a Solution to Stochastic Financial Problems
Статья научная
The purpose of this paper is to survey stochastic differential equations and Euler-Maruyama method for approximating the solution to these equations in financial problems. It is not possible to get explicit solution and analytically answer for many of stochastic differential equations, but in the case of linear stochastic differential equations it may be possible to get an explicit answer. We can approximate the solution with standard numerical methods, such as Euler-Maruyama method, Milstein method and Runge-Kutta method. We will use Euler-Maruyama method for simulation of stochastic differential equations for financial problems, such as asset pricing model, square-root asset pricing model, payoff for a European call option and estimating value of European call option and Asian option to buy the asset at the future time. We will discuss how to find the approximated solutions to stochastic differential equations for financial problems with examples.
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Статья научная
In computational study and automatic recognition of opinions in free texts, certain words in sentences are used to decide its sentiments. While analysing each customer’s opinion per time in churn management will be effective for personalised recommendations. Oftentimes, the opinion is not sufficient for contextualised content mining. While personalised recommendations are time consuming, it also does not provide complete picture of an overall sentiment in the business community of customers. To help businesses identify widespread issues affecting a large segment of their customers towards engendering patterns and trends of different customer churn behaviour, here, we developed a clustered contextualised conversation as opinions set for integration with Roberta Model. The developed churn behavioural opinion clusters disambiguated short messages while charactering contents collectively based on context beyond keyword-based sentiment matching for effective mining. Based on the predicted opinion threshold, customer churn category for group-based personalised decision support was generated, with matching concepts. The baseline RoBERTa model on the contextually clustered opinions, trained with a batch size of 16, a learning rate of 2e-5, over 8 epochs, using a maximum sequence length of 128 and standard hyperparameters, achieved an accuracy of 92%, Precision of 88%, Recall of 86% and F1 score of 84% over a test set of 30%.
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