International Journal of Intelligent Systems and Applications @ijisa
Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1187
Статья научная
In this research, manage the Internal Combustion (IC) engine modeling and a multi-input-multi-output artificial intelligence baseline chattering free sliding mode methodology scheme is developed with guaranteed stability to simultaneously control fuel ratios to desired levels under various air flow disturbances by regulating the mass flow rates of engine PFI and DI injection systems. Modeling of an entire IC engine is a very important and complicated process because engines are nonlinear, multi inputs-multi outputs and time variant. One purpose of accurate modeling is to save development costs of real engines and minimizing the risks of damaging an engine when validating controller designs. Nevertheless, developing a small model, for specific controller design purposes, can be done and then validated on a larger, more complicated model. Analytical dynamic nonlinear modeling of internal combustion engine is carried out using elegant Euler-Lagrange method compromising accuracy and complexity. A baseline estimator with varying parameter gain is designed with guaranteed stability to allow implementation of the proposed state feedback sliding mode methodology into a MATLAB simulation environment, where the sliding mode strategy is implemented into a model engine control module (“software”). To estimate the dynamic model of IC engine fuzzy inference engine is applied to baseline sliding mode methodology. The fuzzy inference baseline sliding methodology performance was compared with a well-tuned baseline multi-loop PID controller through MATLAB simulations and showed improvements, where MATLAB simulations were conducted to validate the feasibility of utilizing the developed controller and state estimator for automotive engines. The proposed tracking method is designed to optimally track the desired FR by minimizing the error between the trapped in-cylinder mass and the product of the desired FR and fuel mass over a given time interval.
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Quality evaluation of component-based software: an empirical approach
Статья научная
In recent days, component-based software engineering has become popular in the software industry for its reuse property. A suitable component-based software model is crucial for the effective design of the component-based software engineering. Quality assessment, evaluation, and analysis of a component model are highly essential to maintain the efficient design in the development of such system. Quality measurement for the component model will be more accurate, if it can be measured by a set of valid and meaningful metrics. This paper has proposed an empirical approach to validate a set of quality metrics along with a set of quality attributes for the design model of component-based software. In the proposed approach, metrics interdependencies have described using a Chi-Square non-parametric test. This paper has considered six different case studies of a well-known library management system to establish the metrics interdependency along with several quality attributes of a component model. This helps to identify the practically useful set of metrics for the quality assessment of high cohesive and low coupling metrics of the component-based system. A massive dataset has been collected from the 34 students of the institute on these six case studies. The Pearson's correlation method has been applied on the collected data set to identify the several correlations between the set of metrics and the set of quality attributes in terms of operation time. This facilitates to assess different crucial quality attributes of component-based system (CBS) design like complexity, analyzability, expressiveness etc.
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Статья научная
Epilepsy is considered one of the primary neurological disorders, and its treatment requires abundant technological assistance. General Anaesthesia induces distinct patterns in brain activity, with the most common being a gradual increase in low-frequency signals as the level of Anaesthesia deepens. In this instance, a method of validating epileptic seizures and Anaesthesia through the utilization of electroencephalogram (EEG) data, acquired non-invasively, is introduced. Epileptic seizures and detection of the presence of Anaesthesia approaches make use of discrete Laplace Transformation (LT), Discrete Cosine Transformation (DCT), and Fast Fourier Transform (FFT). Here, it is discussed how power spectral analysis (PSA) helps study EEG characteristics in detecting epileptic behavior and the presence of Anaesthesia. A dataset of EEG (Epileptic and Anaesthesia), which is available publicly [1,2], has been used in the propounded technique using FIR filters and LT, DCT, and FFT are used to store and process 16 channel data. Power Spectrum Density (PSD) and its average were contrasted against a specific spectrum and frequency range of a typical EEG signal to obtain the results. This work uses a technique to determine whether the patient being studied is epileptic and awake or anesthetized.
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Query Optimization in Arabic Plagiarism Detection: An Empirical Study
Статья научная
This article describes an ongoing research which intends to develop a plagiarism detection system for Arabic documents. We developed different heuristics to generate effective queries for document retrieval from the Web. The performance of those heuristics was empirically evaluated against a sizeable corpus in terms of precision, recall and f-measure. We found that a systematic combination of different heuristics greatly improves the performance of the document retrieval system.
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Статья научная
In the present paper, we have been analyzed the high temperature variations testing in order to be used to determine light emitting diode lifetime, even though laser diode failure mechanisms are more sensitive to increases in current density. As a measured parameter of degradation, the current density is of great significance when searching for failure modes in a laser diode. Raising the current density however, is not really indicative of lifetime since it is more likely a situation to be avoided than one that simulates normal lifetime degradation. The reliability of semiconductor sources is very dependent on the degradation modes. This paper has investigated some of the degradation modes and capabilities of typical LEDs currently used in many communication and sensing systems over wide range of the affecting parameters. LED’s are typically used in multimode transmission systems where data rates no larger than 50 Mbit/sec are required. They have larger spectral widths and can add to the problem of dispersion in communications systems. Laser diodes are used in systems that require coherent and often single mode light such as high data rate communications and sensing applications.
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Rate of Convergence of the Sine Imprecise Functions
Статья научная
We convert polynomial function of degree nth into imprecise form to obtain an important point called conversion point. For some particular region, we collect the finite number of data points to obtain the most economical function called imprecise function. Conversion point of the functions is shown with the help of MUPAD graph. Further we study the area of the imprecise function occurred by the multiplication of sine function to know how much variation of the imprecise functions are obtained for the respective intervals. For different imprecise polynomial we study level of the rate of convergence.
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Статья научная
The objective of this paper is to propose three modified versions of the Gravitational Search Algorithm for continuous optimization problems. Although the Gravitational Search Algorithm is a recently introduced promising memory-less heuristic but its performance is not so satisfactory in multimodal problems particularly during the later iterations. With a view to improve the exploration and exploitation capabilities of GSA, it is hybridized with well-known real coded genetic algorithm operators. The first version is the hybridization of GSA with Laplace Crossover which was initially designed for real coded genetic algorithms. The second version is the hybridization of GSA with Power Mutation which also was initially designed for real coded genetic algorithms. The third version hybridizes the GSA with both the Laplace Crossover and the Power mutation. The performance of the original GSA and the three proposed variants is investigated over a set of 23 benchmark problems considered in the original paper of GSA. Next, all the four variants are implemented on 30 rotated and shifted benchmark problems of CEC 2014. The extensive numerical, graphical and statistical analysis of the results show that the third version incorporating the Laplace Crossover and Power mutation is a definite improvement over the other variants.
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Real Time Hand Detection & Tracking for Dynamic Gesture Recognition
Статья научная
In the recent year gesture recognition has become the most intuitive and effective communication technique for human interaction with machines. In this paper we are going to work on hand gesture recognition and interpret the meaning of it from video sequences. Our work takes place in following three phases: 1. Hand Detection & Tracking 2. Feature extraction 3. Gesture recognition. We have started proposed work with first step as applying hand tracking and hand detection algorithm to track hand motion and to extract position of the hand. Trajectory based features are being drawn out from hand and used for recognition process and hidden markov model is being design for each gesture for gesture recognition. Hidden Markov Model is basically a powerful statistical tool to model generative sequences. Our method is being tested on our own data set of 16 gestures and the average recognition rate we have got is 91%. With proposed methodology gives the better recognition results compare with the traditional approaches such as PCA, ANN, SVM, DTW and many more.
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Real Time Multiple Hand Gesture Recognition System for Human Computer Interaction
Статья научная
With the increasing use of computing devices in day to day life, the need of user friendly interfaces has lead towards the evolution of different types of interfaces for human computer interaction. Real time vision based hand gesture recognition affords users the ability to interact with computers in more natural and intuitive ways. Direct use of hands as an input device is an attractive method which can communicate much more information by itself in comparison to mice, joysticks etc allowing a greater number of recognition system that can be used in a variety of human computer interaction applications. The gesture recognition system consist of three main modules like hand segmentation, hand tracking and gesture recognition from hand features. The designed system further integrated with different applications like image browser, virtual game etc. possibilities for human computer interaction. Computer Vision based systems has the potential to provide more natural, non-contact solutions. The present research work focuses on to design and develops a practical framework for real time hand gesture.
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Real-Time Compensation of Chatter Vibration in Machine Tools
Статья научная
The chatter vibration stands for a sudden relative vibration appeared between a material and a tool while processing with a machine. This vibration is a factor that seriously affects the quality of processed materials as well as being a factor which causes serious damages to the tool and the machine. This study is related to the detection and compensation of chatter vibration that can compensate chatter vibration faster and produce processed goods with more precision by autonomous compensation. The above-mentioned chatter vibration compensator includes the chatter vibration sensor and the chatter compensator that estimates the compensation value according to the sensor detecting the chatter vibration of machine tool and the chatter vibration detected from the sensor while having a feature of being organized by interlocking with the machine tool controller.
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Real-Time Fuel Quality Monitoring System for Smart Vehicles
Статья научная
A novel method of monitoring the quality i.e. the percentage of purity of diesel fuel in real-time environment in smart vehicles is presented here. The method incorporates temperature compensated density measurement using a dual load cell and temperature sensor. The mass of the small fixed volume of diesel sample and temperature of diesel are measured by the pre-calibrated load cell based amplifier set up and a smart miniature temperature sensor respectively. The amplified voltage of the load cell provides the measure of the density of the fuel. Further, the measured value of fuel density is temperature compensated and compared with standard reference values to indicate percentage of purity of the diesel sample. The method is automated with an intelligent virtual instrument that provides all the means of testing the fuel sample and displaying results with high level of accuracy ~ 99.8 %. The model offers a way of providing vital real time information to user and has great future prospects. Also, the data collected can be analyzed for developing complex mathematical models to suggest optimized driving parameters for vehicles.
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Real-time Deep Learning Based Mobile Application for Detecting Edible Fungi: Mushapp
Статья научная
Mushroom consumption and wild mushroom gathering are increasing in our country and in the world. Mushroom poisoning has an important place in food poisoning cases. Mushroom poisoning accounts for approximately 7% of poisoning cases in adults. Mushroom collection and consumption is common in many regions of our country. In this study, a deep learning based mobile application was developed to reduce the incidence of mushroom poisoning by taking a photo of a mushroom and determining the type and toxicity of the mushroom from the photo. This mobile application is called MushAPP. In the first phase of the study, 5150 mushroom images of 20 mushroom species were used to create the dataset. The dataset was then pre-processed and converted into a format that can be used by the deep learning algorithm. The mobile application side of the project was developed in Android Studio IDE environment. An artificial intelligence model was integrated into the designed mobile application. In the application, the type and toxicity status of the mushroom viewed from the mobile device camera are determined and presented to the user. The research findings were analyzed and it was determined that the accuracy rate of the application in detecting the mushroom species was 99.8%.
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Recent Trends, Applications, and Challenges of Brain-Computer Interfacing (BCI)
Статья научная
Brain-Computer Interfacing (BCI) enables a communication pathway that is used to directly control certain object or device with the human brain. It is possible to acquire data from the brain with the help of sensors which essentially monitor the physical processes that occur in the brain and with the help of software we can direct a device accordingly. BCI introduces its users a new system that communicates in a special way without the use of muscles. BCI also provides a useful platform for the people with physical disabilities to conveniently perform certain tasks in our society. It uses brain imaging technologies which help in increasing the quality of the communication between humans and machines. There has been significant research effort in the past decade to explore different aspects of this promising field of technology. Previously, BCIs had limited functionality but due to recent advancement in technology it has attained the maturity of its own right by adding new trends and extra features to it. In this paper, research work of development and integration of both hardware and software in BCI and the advancements of BCI are surveyed considering all the possibilities of direct communication of computers with a brain using emerging technologies. Different approaches used up till now with an overview of the methodology are presented to make the reader understand its meaning and functionality in a compact way. This research paper will guide a beginner to end up with a thorough knowledge of what's, how's and why's of BCI. In addition, BCI applications, challenges, possible solutions and future directions have also been discussed.
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Статья научная
Traditionally, Control Chart Patterns (CCP) is widely used as a powerful method to measure, classify,analyze and interpret process data to improve the quality of products and service by detecting instabilities and justifying possible causes. In this study, we have developed an expert system that we called an expert system for control chart patterns recognition for recognition of the common types of control chart patterns (CCPs). The proposed system includes three main modules: the feature extraction module, the classifier module and the optimization module. In the feature extraction module, the multi-resolution wavelets (MRW) are proposed as the effective features for representation of CCPs. In the classifier module, the adaptive neuro-fuzzy inference system (ANFIS) is investigated. In ANFIS training, the vector of radius has a very important role for its recognition accuracy. Therefore, in the optimization module, imperialist competitive algorithm(ICA) is proposed for finding optimum vector of radius. Simulation results show that the proposed system has high recognition accuracy.
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Reduced Multiplicative Tolerance Ranking and Applications
Статья научная
In this paper a reduced multiplicative tolerance - a measure of sensitivity analysis in multi-objective linear programming (MOLP) is presented. By using this new measure a method for ranking the set of efficient extreme solutions is proposed. The idea is to rank these solutions by values of the reduced tolerance. This approach can be applied to many MOLP problems, where sensitivity analysis is important for a decision maker. In the paper, applications of the presented methodology are shown in the market model and the transportation problem.
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Reducing Support Vector Machine Classification Error by Implementing Kalman Filter
Статья научная
The aim of this is to demonstrate the capability of Kalman Filter to reduce Support Vector Machine classification errors in classifying pipeline corrosion depth. In pipeline defect classification, it is important to increase the accuracy of the SVM classification so that one can avoid misclassification which can lead to greater problems in monitoring pipeline defect and prediction of pipeline leakage. In this paper, it is found that noisy data can greatly affect the performance of SVM. Hence, Kalman Filter + SVM hybrid technique has been proposed as a solution to reduce SVM classification errors. The datasets has been added with Additive White Gaussian Noise in several stages to study the effect of noise on SVM classification accuracy. Three techniques have been studied in this experiment, namely SVM, hybrid of Discrete Wavelet Transform + SVM and hybrid of Kalman Filter + SVM. Experiment results have been compared to find the most promising techniques among them. MATLAB simulations show Kalman Filter and Support Vector Machine combination in a single system produced higher accuracy compared to the other two techniques.
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Reduction of Misclassifications in Wildfire Detection: A Weighted Ensemble Deep Learning Approach
Статья научная
Governments worldwide are increasingly prioritizing early wildfire detection to safeguard lives, property, and the environment. Although CNN-based models have demonstrated exceptional performance in various computer vision applications, the evolving nature of wildfire images poses significant challenges for a single CNN-based model in wildfire detection. In this study, we addressed this issue by integrating and weighting the differential learning capabilities of three individual transfer learning models: InceptionV3, ResNet50, and VGG16. Experimental results show that the ensemble deep learning models significantly outperformed all single classifiers across all performance metrics. Both the ensemble and weighted ensemble deep learning models achieved 99.7% accuracy, 99.5% precision, 100% recall, 99.8% F1-score, 0.5%false positive rate, 0.0% false negative rate and 0.3% error rate. Additionally, these models reduced the error rate by 98%, 91%, and 40% compared to the error rates of ResNet50, InceptionV3, and VGG16 respectively. A false negative rate of 0% indicates that our proposed ensemble deep learning models identified and predicted all the wildfire instances present in the test set correctly without a single misclassification. This positions our proposed ensemble deep learning models as superior choices for reducing misclassifications in wildfire detection.
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Redundancy Level Optimization in Modular Software System Models using ABC
Статья научная
The performance of optimization algorithms is problem dependent and as per no free lunch theorem, there exists no such algorithm which can be efficiently applied to every type of problem(s). However, we can modify the algorithm/ technique in a manner such that it is able to deal with a maximum type of problems. In this study we have modified the structure of basic Artificial Bee Colony (ABC), a recently proposed metaheuristic algorithm based on the concept of swarm intelligence to optimize the models of software reliability. The modified variant of ABC is termed as balanced ABC (B-ABC). The simulated results show the efficiency and capability of the variant to solve such type of the problems.
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Reliable Mobile Ad-Hoc Network Routing Using Firefly Algorithm
Статья научная
Routing in Mobile Ad-hoc NETwork (MANET) is a contemporary graph problem that is solved using various shortest path search techniques. The routing algorithms employed in modern routers use deterministic algorithms that extract an exact non-dominated set of solutions from the search space. The search efficiency of these algorithms is found to have an exponential time complexity in the worst case. Moreover this problem is a multi-objective optimization problem in nature for MANET and it is required to consider changing topology layout. This study attempts to employ a formulation incorporating objectives viz., delay, hop-distance, load, cost and reliability that has significant impact on network performance. Simulation with different random topologies has been carried out to illustrate the implementation of an exhaustive search algorithm and it is observed that the algorithm could handle small-scale networks limited to 15 nodes. A random search meta-heuristic that adopts the nature of firefly swarm has been proposed for larger networks to yield an approximated non-dominated path set. Firefly Algorithm is found to perform better than the exact algorithm in terms of scalability and computational time.
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Representation of Fuzzy Matrices Based on Reference Function
Статья научная
Fuzzy matrices in the present form do not meet the most important requirement of matrix representatiom in the form of reference function without which no logical result can be expected. In this article, we intend to represent fuzzy matrices in which there would be the use of reference function. Our main purpose is to deal specially with complement of fuzzy matrices and some of its properties when our new definition of complementation of matrices is considered. For doing these the new definition of complementation of fuzzy sets based on reference function plays a very crucial role. Further, a new definition of trace of a fuzzy matrix is introduced in this article which is in accordance with newly defined fuzzy matrices with the help of reference function and thereby efforts have been made to establish some of the properties of trace of fuzzy matrices.
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