International Journal of Intelligent Systems and Applications @ijisa
Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1214
Parameter Tuning via Genetic Algorithm of Fuzzy Controller for Fire Tube Boiler
Статья научная
The optimal use of fuel energy and water in a fire tube boiler is important in achieving economical system operation, precise control system design required to achieve high speed of response with no overshot. Two artificial intelligence techniques, fuzzy control (FLC) and genetic-fuzzy control (GFLC) applied to control both of the water/steam temperature and water level control loops of boiler. The parameters of the FLC are optimized to locating the optimal solutions to meet the required performance objectives using a genetic algorithm. The parameters subject to optimization are the width of the membership functions and scaling factors. The performance of the fire tube boiler that fitted with GFLC has reliable dynamic performance as compared with the system fitted with FLC.
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Статья научная
This work falls within the framework of the Arabic natural language processing. We are interested in parsing Arabic texts. Existing parsers generate parse trees that give an idea about the structure of the sentence without considering the syntactic functions specific to the Arabic language. Thus, the results are still insufficient in terms of syntactic information. The system we have developed in this article takes into consideration all these syntactic functions. This system begins with a morphological analysis in the context. Then, it uses a CFG grammar to extract the phrases and ends by exploiting the formalism of unification grammar and traditional grammar to combine these phrases and generate the final sentence structure.
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Passenger body vibration control in active quarter car model using hybrid ANFIS PID controller
Статья научная
The objective of this paper is to improve the passenger ride comfort and safety in an active quarter car model. For this purpose, a quarter car model with passenger body and seat is considered to capture the dynamic behaviour of a real complete car system. To achieve the desired target, two different controllers such as Adaptive Neuro Fuzzy (ANFIS) controller and Hybrid ANFIS PID controller (HANFISPID) are designed. The controllers selection and design was aimed to achieve good passenger ride comfort and health, taking passenger body acceleration and displacement response under random road excitations. The performance of designed controllers are evaluated using simulation work in time and frequency domain. Simulation results show that the proposed HANFISPID control scheme can succesfully achieve the desired ride comfort and safety of passenger compared to passive and ANFIS controlled cases in an active quarter car model.
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Passivity analysis of neutral fuzzy system with linear fractional uncertainty
Статья научная
In this paper, the passivity analysis of Takagi-Sugeno (T-S) fuzzy neutral system with interval time-varying delay and linear fractional parametric uncertainty is investigated. Based on the Lyapunov-Krasovskii functional and the free weighting matrix method, delay-dependent sufficient conditions for solvability of the passive problem are obtained in terms of Linear matrix inequalities (LMIs). Finally, a simulation example is provided to demonstrate effectiveness and applicability of the theoretical results.
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Pattern Formation in Swarming Spacecrafts using Tersoff-Brenner Potential Field
Статья научная
We present a distributed control strategy that lets a swarm of spacecrafts autonomously form a lattice in orbit around a planet. The system, based on the artificial potential field approach, proposes a novel way to divide the artificial field into two main terms: a global artificial potential field mainly based on the famous C-W equations that gathers the spacecrafts around a predefined meeting point, and a local term exploited the well-known Tersoff-Brenner potential that allows a spacecraft to place itself in the correct position relative to its closest neighbors. Moreover, in order to obtain convergence from all initial distributions of the spacecrafts, a dissipation term depended on the velocity of agent is introduced. The new methodology is demonstrated in the problem of forming a hexagon lattice, the structure unit of graphite. It is shown that a pattern formation can operate around a planet. By slightly changing the scenario our method can be easily applied to shape other configurations, such as a regular tetrahedron (with central point), the structure unit, etc.
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Performance Analysis of Deep Learning Techniques for Multi-Focus Image Fusion
Статья научная
Multi-Focus Image Fusion (MFIF) plays an important role in the field of computer vision. It aims to merge multiple images that possess different focus depths, resulting in a single image with a focused appearance. Though deep learning based methods have demonstrated development in the MFIF field, they vary significantly with regard to fusion quality and robustness to different focus changes. This paper presents the performance analysis of three deep learning-based MFIF methods specifically ECNN (Ensemble based Convolutional Neural Network), DRPL (Deep Regression Pair Learning) and SESF-Fuse. These techniques have been selected due to their publicly availability of training and testing source code, facilitating a thorough and reproducible analysis along with their diverse architectural approaches to MFIF. For training, three datasets were used ILSVRC2012, COCO2017, and DIV2K. The performance of the techniques was evaluated on two publicly available MFIF datasets: Lytro and RealMFF datasets using four objective evaluation metrics viz. Mutual Information, Gradient based metric, Piella metric and Chen-Varshney metric. Extensive experiments were conducted both qualitatively and quantitatively to analyze the effectiveness of each technique in terms of preserving details, artifacts reduction, consistency at the boundary region, texture fidelity etc. which jointly determine the feasibility of these methods for real-world applications. Ultimately, the findings illuminate the strengths and limitations of these deep learning approaches, providing valuable insights for future research and development in methodologies for MFIF.
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Performance Analysis of Multi Functional Bot System Design Using Microcontroller
Статья научная
This paper includes performance analysis of a multipurpose microcontroller based system which has various modes to control distinct applications. The paper elucidates how a single chip microcontroller can process different signals and accomplish different tasks. The system discussed in this paper has seven modes. Each mode controls different applications. Radio frequency signals are used for controlling the system wirelessly and each mode is triggered by giving a suitable 4-bit logic by the transmitter. The different modes which the system works in consist of security tracking, temperature measurement, sound actuated control, voltmeter mode, door automation, pits avoider and obstacle avoider. The versatility of the system is tested using Xilinx software v10.1. It is found that the system is functioning properly under normal conditions and the variations of the different parameters for a particular mode have been plotted in MATLAB R2013a v8.1 to validate the accuracy of the system.
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Performance Analysis of Rayleigh and Rician Fading Channel Models using Matlab Simulation
Статья научная
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.
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Performance Analysis of Shallow and Deep Learning Classifiers Leveraging the CICIDS 2017 Dataset
Статья научная
In order to implement the advantages of machine learning in the cybersecurity ecosystem, various anomaly detection-based models are being developed owing to their ability to flag zero-day attacks over their signature-based counterparts. The development of these anomaly detection-based models depends heavily on the dataset being employed in terms of factors such as wide attack pool or diversity. The CICIDS 2017 stands out as a relevant dataset in this regard. This work involves an analytical comparison of the performances by selected shallow machine learning algorithms as well as a deep learning algorithm leveraging the CICIDS 2017 dataset. The dataset was imported, pre-processed and necessary feature selection and engineering carried out for the shallow learning and deep learning scenarios respectively. Outcomes from the study show that the deep learning model presented the highest performance of all with respect to accuracy score, having percentage value as high as 99.71% but took the longest time to process with 550 seconds. Furthermore, some shallow learning classifiers such as Decision Tree and Random Forest took less processing time (4.567 and 3.95 seconds respectively) but had slightly less accuracy scores than the deep learning model with the CICIDS 2017 dataset. Results from our study show that Deep Neural Network is a viable model for intrusion detection with the CICIDS 2017 dataset. Furthermore, the results of this study are to provide information that may influence choices while developing machine learning based intrusion detection systems with the CICIDS 2017 dataset.
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Performance Analysis of VBLAST Based MIMO OFDM System in Vehicular Channel
Статья научная
Vehicular communication is emerging as an important ingredient for successful implement of Intelligent Transportation Systems(ITS). But the development of suitable communications systems plays an important role in mobility condition. The desired system should support dynamic wireless exchange of data between nearby vehicles or road side infrastructure. In this regards multiple input and multiple output (MIMO) orthogonal frequency domain multiplexing (OFDM) system is possibly the best solution. This paper deals with the performance analysis of the MIMO OFDM system in Nakagami-m channel with Doppler shift and also partial hardware implementation of baseband signal processing.
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Статья научная
This paper presents a comparative performance study of various analog integrated circuits (namely CC-II, DVCC, CDBA and CDTA) used with ISFET for monitoring the quality of water. The use of these active components makes the implementation simple and attractive. The functionality of the circuits are tested using Tanner simulator version 15 for a 70nm CMOS process model also the transfer functions realization for each is done on MATLAB R2011a version, the Very high speed integrated circuit Hardware description language(VHDL) code for all scheme is simulated on Xilinx ISE 10.1 and various simulation results are obtained and its is found that DVCC is most stable and consume maximum power whereas CC-II is the least stable and consumes minimum power amongst all the four deployed analog IC’s. Detailed simulation results are included in the paper to give insight into the research work carried out.
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Статья научная
We have proposed a new approach to identify segments, which can be executed simultaneously, or coextending to achieve high computational speed with optimized utilization of available resources. Our suggested approach is divided into four modules. In first module we have represented a program segment using Abstract Syntax Tree (AST) along with an algorithm for constructing AST and in second module, this AST has been converted into Program Dependence Graph (PDG), the detailed approach has been described in section II, The process of construction of PDG is divided into two steps: First we construct a Control Dependence Graph (CDG, In second step reachability definition algorithm has been used to identify data dependencies between the various modules of a program by constructing Data Dependence Graph (DDG). In third module an algorithm is suggested to identify parallel modules, i.e., the modules that can be executed simultaneously in the section III and in fourth module performance analysis is discussed through our approach along with the computation of time complexity and its comparison with sequential approach is demonstrated in a pictorial form.
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Performance Comparison of Hybrid GA-PSO Based Tuned IMMs for Maneuver Target Tracking
Статья научная
Target tracking is very important field of research as it has wider applications in defense as well as civilian applications. Kalman filter is generally used for such applications. When the process and measurements are non linear extensions of Kalman filters like Extended Kalman Filter, Unscented Kalman Filters are widely used. UKF can give estimations up to second order characteristics of random process. The target is maneuvering and switching among different models like constant velocity (CV), constant acceleration (CA) or constant turn (CT), Interactive Multiple Models (IMM) are employed. Implementation of IMM filters for any application is difficult because of initialization of Kalman filter i,e, tuning of filter has to be performed before applying to real time situations. It demands prior estimations of Noise covariance matrices which are left for engineering intuitions. This paper presents the nonlinear state estimation using IMM and tuning of the filter is done using bio-inspired algorithms like PSO GA and Hybrid GA-PSO.
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Performance Comparison of Various Robust Data Clustering Algorithms
Статья научная
Robust clustering techniques are real life clustering techniques for noisy data. They work efficiently in the presence of noise. Fuzzy C-means (FCM) is the first clustering algorithm, based upon fuzzy sets, proposed by J C Bezdek but it does not give accurate results in the presence of noise. In this paper, FCM and various robust clustering algorithms namely: Possibilistic C-Means (PCM), Possibilistic Fuzzy C-means (PFCM), Credibilistic Fuzzy C-means (CFCM), Noise Clustering (NC) and Density Oriented Fuzzy C-Means (DOFCM) are studied and compared based upon robust characteristics of a clustering algorithm. For the performance analysis of these algorithms in noisy environment, they are applied on various noisy synthetic data sets, standard data sets like DUNN data-set, Bensaid data set. In comparison to FCM, PCM, PFCM, CFCM, and NC, DOFCM clustering method identified outliers very well and selected more desirable cluster centroids.
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Статья научная
High blood pressure (BP) monitoring Blood pressure (BP) is one of the common cardiovascular diseases and therefore the early high blood pressure (hypertension) detection, management, and prevention are mandatory. One promising method of continuous, non-invasive blood pressure estimation is photoplethysmography (PPG). In this study, a novel method was proposed to introduce the AlexNet framework into the time-frequency domain for classification of BP levels based on PPG signals. The study was conducted using the publicly available Figshare dataset which offers PPG signals, and the blood pressure labels against them. Data balancing techniques were used to alleviate class imbalances. Preprocessing and Feature Extraction of PPG Signals. The PPG signals were preprocessed with noise filtering and signals were then transformed from 1D-time to image to facilitate robust feature extraction. The proposed classification model, based on AlexNet showed the best result, with 98.89% accuracy, recall, and precision, and 99.44% specificity. This model outperformed alternative models (VGG16, DenseNet, ResNet50, GoogleNet) for classifying BP levels into the JNC 7 report standard categories normotension, prehypertension and hypertension. This study has two primary contributions. Initially, it demonstrates the efficacy of AlexNet model to extract meaningful features from PPG signals by its hierarchical convolutional and max-pooling layers thereby enabling accurate classification of BP levels. This study underscores the potential of deep learning and PPG signals for developing a highly accurate and truly non-invasive BP monitoring system. In the second aspect, the study offers a systematic assessment and comparison of the proposed over other well-known deep-learning networks, presenting the effectiveness of the AlexNet-based one. These results are of critical importance in the development of novel non-invasive BP monitoring modalities and optimization of cardiovascular health managements and personalized health cares.
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Статья научная
Advances in FPGA technology have dramatically increased the use of FPGAs for computer vision applications. Availability of on-chip processor (like PowerPC) made it possible to design embedded systems using FPGAs for video processing applications. The objective of this research is to evaluate the performance of different memory components available on FPGA boards for embedded/platform-based implementations of image/video processing applications. The clustering based change detection algorithm for Ubiquitous Multimedia Environment is selected for evaluating the effect of different memory components (DDR/BRAM) on performance of the system in terms of frame rate (frames per second).
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Статья научная
As the world evolves day by day with new technologies, there is a need to design a secure network in such a way that intruders and unauthorized persons should not have access to the network as well as information regarding the personnel in any firm. In this study, a new cryptographic technique for securing data transmission based on the LaplaceLaguerre polynomial (LLP) is developed and compared to an existing auto-associative neural network technique (AANNT).The performance of the LLPT and AANNT was tested with some selected files in a MATLAB environment and the results obtained provided comparative information (in respect of AANNT versus LLPT) as follows: encryption time (1.67 ms versus 3.9931s), decryption time (1.833 ms versus 2.1172s), throughput (26.2975 Kb/s versus 0.01098 Kb/s), memory consumption (3.349 KB versus 15.958 KB). From the compared results, it shows that AANNT offers a faster processing time, higher throughput, and takes up less memory space than the LLPT. However, cryptanalysis of the AANNT is possible if the network's weight and design are known; hence, the technique is unreliable for ensuring the data integrity and confidentiality of encrypted data. The proposed LLP cryptographic algorithm is designed to provide a higher security level by making the LLP algorithm computationally tedious to invert using the standard Laplace transform inversion method. When compared to the AANN-based cryptographic technique, cracking the algorithm to uncover the encryption key takes time. This shows the strength and robustness of the proposed LLP cryptographic algorithm against attacks, as well as its suitability for solving the problem of data privacy and security when compared to the AANN-based cryptographic algorithm.
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Performance Evaluation of Modified DBLA Using Dark Channel Prior & CLAHE
Статья научная
This paper has focused on the different image enhancement techniques. Image enhancement has found to be one of the most important vision applications because it has ability to enhance the visibility of images. It enhances the quality of poor pictures. Distinctive procedures have been proposed so far for improving the quality of the digital images. To enhance picture quality image enhancement can specifically improve and limit some data presented in the input picture. It is a kind of vision system which reductions picture commotion, kill antiquities, and keep up the informative parts. Its object is to open up certain picture characteristics for investigation, conclusion and further use. The main objective of this paper is to modify the DBLA using the dark channel prior and CLAHE to enhance the results further. The comparative analysis has shown the significant improvement over the CLAHE and the DBLA.
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Статья научная
Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.
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Performance Improvement of Plant Identification Model based on PSO Segmentation
Статья научная
Plant identification has been a challenging task for many researchers. Several researches proposed various techniques for plant identification based on leaves shape. However, image segmentation is an essential and critical part of analyzing the leaves images. This paper, proposed an efficient plant species identification model using the digital images of leaves. The proposed identification model adopts the particle swarm optimization for leaves images segmentation. Then, feature selection process using information gain and discritization process are applied to the segmented image's features. The proposed model was evaluated on the Flavia dataset. Experimental results on different kind of classifiers show an improvement in the identification accuracy up to 98.7%.
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