International Journal of Intelligent Engineering Informatics (10 papers in press)
An Iterative Solution Approach for Steady State Analysis of Self-Excited Induction Generator
by Hayri Arabaci
Abstract: The steady state analysis of self-excited induction generator is widely used for stand-alone renewable energy system. The impedance values of equivalent circuit vary depending on frequency. The frequency changes according to load, excitation capacitance and rotor speed. If the values of load and capacitance are known, only the frequency and slip are the two main unknowns left for analysis of the single-phase equivalent circuit. The simulation is generally performed for a desired slip value to obtain the frequency. However, with the proposed approach in this study, the values of rotor slip, and frequency of terminal voltage are both directly determined. The proposed algorithm is verified by the experiments of three phase 1.1 kW squirrel cage induction machine.
Keywords: iterative solution approach; renewable energy; self-excited induction generator; SCIG; stand-alone systems; steady state analysis.
New weighted clustering ensemble based on external index and subspace attributes partitions for large features datasets.
by Nadjia Khatir, Nait-Bahloul Safia
Abstract: Real world datasets are commonly large and involve a lot of features.
This is due because of the variety of domains where are obtained from or for
the impact of diverse features extractors techniques. Relatively few works on
selecting and weighting relevant features for the propose of clustering data are
involved in the literature. To cope with this issue, in this paper a new weighting
partitions based features selection framework is proposed in conjunction with
clustering ensemble for large features datasets.
The clustering framework proposed is designed at two different levels: at the
first level, from the original dataset, partitions of features are generated based
on graph partitioning methods, where the nodes represent the features vectors
and the links (edges) are the correlation coefficients between these features,
then, as each subset of features gives different representation of the same data,
a baseline clustering algorithms on each subset are applied. At the second level,
a new clustering ensemble scheme is proposed to combine the whole partitions
by proposing a new weight based on external index to ass the quality weight of
each clustering partition before incorporating them into the final partition of the
Six real world datasets from both images and biological domains are chosen to
be evaluated and an average accuracy between 75:18% and 98:04% is achieved.
Results show that the new proposed technique have been successfully outclassed
state-of-the-art methods in term of both effectiveness and efficiency.
Keywords: Clustering; Consensus; weighted partitions; multi-features data; Data fusion.
Heat Transfer Dynamics Modeling by Means of Clustering and Swarm Methods
by Oualid LAMRAOUI, Yassine BOUDOUAOUI, Hacene HABBI
Abstract: This paper deals with the modeling problem of heat transfer dynamics in thermal exchanger process by using fuzzy prediction approaches. Clustering and swarm based optimization methods are used to derive heat transfer dynamical models to predict temperature variations of hot and cold fluids in the exchanger. The clustering method relies on a one-shot potential calculating strategy to extract the fuzzy sets distribution from the data space. However, the swarm optimization method employs a subject function to optimize the premise and conclusion parameters of the fuzzy structure. Experimental data extracted from a pilot exchanger process is used to learn the fuzzy models, and their performances are compared on both training and testing measurement data.
Keywords: fuzzy clustering; fuzzy modeling; artificial bee colony optimization; metaheuristic optimization; heat exchanger.
A Comparison of Health Informatics Education in the United States
by D. Cenk Erdil
Abstract: The need for health informatics professionals has been increasing recently. One common task in health informatics, is to collect data. There is also a significant need to orchestrate collection of data through informatics infrastructure, manage computing resources, store data, and operate network-enabled medical devices. In addition, in many medical fields, the overall need for supporting many complex medical devices is also increasing. Existing health informatics undergraduate programs in the United States do not adequately equip students with skills to address these challenges, mostly due to limited STEM-focused courses. Thus, a skills gap arises between graduating health informatics professionals, and typical job requirements in many health informatics fields, which has traditionally been addressed by employing graduates with computer science and engineering degrees. Moreover, graduate programs in many health informatics fields consistently offer less credits in computer science and information technology, when compared to other informatics fields. For example, a basic analysis of public health graduate programs shows that the ratio of STEM credits in public health informatics to those in other health informatics fields is 1 to 2. This article provides a basic analysis of both under- graduate and graduate programs in health informatics in the United States. At the graduate level, it highlights the differences across common informatics programs in medical sciences. At the undergraduate level, it proposes more STEM-focused undergraduate degree options to complement existing undergraduate programs to have an immediate effect, which could provide students proper training to help narrow this technology-specific skills gap.
Keywords: Health informatics; information technology; computer science and engineering; big data; engineering education; STEM education.
A New Hybrid Gravitational Particle Swarm Optimization-ACO with Local Search Mechanism, PSOGSA-ACO-Ls for TSP.
by Nizar Rokbani
Abstract: Abstract: The travelling salesman problem, TSP, is a hard combinatorial optimization problem and a popular benchmarking problem at the same time. It is a common test bench frequently used to evaluate various heuristic and hybrid methods for combinatorial optimization. The TSP has also a number of practical real-world and industrial applications, such as routing in Internet of Things, IoT, networks, path planning in robotics and many others. Hybrid methods have shown good ability to find good solutions of these hard problems. Among them, those based on the combination of particle swarm optimization, PSO, and ant colony optimization, ACO, have demonstrated good results and performance. In this paper, a new hybrid algorithm for the TSP is proposed; it combines gravitational PSO, PSOGSA, and ACO, and is called ant supervised by gravitational particle swarm optimization with a local search, PSOGSA-ACO-LS. PSOGSA is used to optimize ACO settings while a local search mechanism, 2-Opt is employed by ACO to ameliorate its local solutions. The proposed method is evaluated using a set a test benches from the TSPLib database including: eil51, berlin52, st70, eil76, rat99, eil101, kroA100, and kroA200. Experimental results show that ACO-GPSO-LS is able to solve the set of TSP instances listed below including the large TSP Data sets. kroA100, eli101 and kroA200. The experiments also demonstrate that the proposed approach converges for all TSP selected test benches and gives better results than recent techniques for berlin52 and st70.
Keywords: Gravitational Search; PSO; ACO; TSP; Local Search; Hybridization.
Real-Time Image Encryption and Decryption Methods based on the KarhunenLoeve Transform
by Hasan Rashaideh, Ahmad Shaheen, Nijad Al-Najdawi
Abstract: The need for a reliable, fast, and accurate encryption algorithm that ensures an identical decrypted image is required in many organizations and industries where marginal concessions are not acceptable. The choice of encrypting images in the frequency domain is more suitable as the image components will be de-correlated, which provides a suitable platform to classify those values according to their significance. In this work and based on the Karhunen-Loeve transform and the Advanced Encryption Standard algorithm a symmetric lossless encryption and decryption algorithms are proposed, where various frequencies are processed in order to provide reliable and secure form. The encryption algorithm involves diminishing the horizontal, vertical and diagonal frequencies that represents the visual details of the given image, this is done using a series of operations designed specifically for this purpose. Simultaneously the frequency values are used to build a decryption key matrix that is to be further encrypted using the Advanced Encryption Standard algorithm. On the other hand, the decryption algorithm overturns the encryption steps returning the image to its original shape with no changes on its corresponding values. The proposed encryption scheme is applicable for applications that require protected delivery of high-quality data. In order to evaluate the efficiency of the proposed algorithms, a comprehensive analysis and evaluation has been conducted based on a set of standard benchmark test images. In terms of quality, the proposed decryption algorithm outperforms the rest of the algorithms in this domain by providing lossless decrypted images that are identical to the originals. Moreover, well-known statistical tests and security measures have been performed in order to validate the security of the proposed encryption algorithm, the results outperform the state-of-the-art algorithms in this domain.
Keywords: Cryptography; Lossless Symmetric Encryption and Decryption.
Real Time Fuzzy Logic Controlled Fire Detection System for Home Applications
by Mehmet Cunkas, Vacip DENIZ
Abstract: In this study, a low-cost alternative method for hardware implementation in fire detection has been developed for home or office applications by using fuzzy logic. The system consists of smoke, flame, heat and humidity sensors which are used to detect the fire. When a possible fire is detected, the data from the sensors are processed with fuzzy logic and the result is sent as a message to the mobile phone through the GSM module indicating the fire location in minimum time period. In addition, the sensor values can be monitored by the MQTT Dash application on the mobile phone. A fire test cabinet has been designed to test the developed method. Real time tests were done in the fire test cabinet and it was observed that the fire status was accurately detected in a short time. This study, which is important for researchers and practitioners, offers a different approach to fire detection, especially a new framework for home and office applications, and also helps fire-fighters to respond quickly to fire.
Keywords: Fire detection; fuzzy logic; GSM communication; home applications; sensor monitoring.
Forecasting Time Series Data Using Moving-Window Swarm Intelligence-Optimized Machine Learning Regression
by Ngoc-Tri Ngo, Thi Thu Ha Truong
Abstract: An accurate forecast of future time series data can support decision-makers to obtain economic benefits. This study proposes a hybrid time series forecast model namely a moving-window firefly algorithm (FA)-based least squares support vector regression (MFA-LSSVR). The LSSVR captures patterns of historical data and predicts future values of time series data while the FA is used to optimize the LSSVR`s parameters to improve the predictive accuracy. The proposed model was trained and tested using two actual datasets of the daily energy demand data and the stock price data. Experimental results show that the proposed MFA-LSSVR model is effective in forecasting time series data and the comparison results revealed that the proposed model outperforms other models, i.e., the LSSVR and the ARIMA (autoregressive integrated moving average) in predicting energy demand and stock price. This studys findings, thus, provide decision-makers a potential approach in early forecasting future patterns of time series data.
Keywords: Machine learning regression; moving-window concept; swarm intelligence; time series forecast.
An Improved Sensorless Control of Induction Motor Using ADALINE: Theory and Experiment
by Imane Ghlib, Youcef Messlem, Ahmad Zakaria Mehdi Chedjara
Abstract: This paper presents a new observer for speed sensorless field oriented control of induction motor drive. First, the state space model of induction motor is estimated using the Luenberger observer. Then, the errors between the measured currents and estimated states are involved in the PI controller to derivative the rotor speed. Moreover, a major element of the adaptive observer is the PI controller. However, these adaptive mechanisms are unsatisfactory because they can be adversely affected by certain conditions. Such, the main disadvantages of this controller is that its parameters are established only by trial and error method and they are fixed in the entire operating system. Several attempts have been made to evaluate these observers using artificial intelligence (artificial neural networks, fuzzy logic .). Although, most of these studies have been only carried out in theory, which cannot be able to establish in the experiment, because of its complexity algorithms. In order to avoid these problems, ADALINE (ADAptive LInear NEuron) is proposed to replace the PI parameters. They are obtained from the data for training the NN (neural network) in each step by using a simple and reduced algorithm. The aim of this paper is to enhance the performance of the traditional controller under various conditions. Furthermore, to make this technique able to involve in real time, both constraints are respected for accurate estimation and reduced calculation. The principle idea is used two weights where the first one as the proportional mean-while, the second as an integral parameter. This method of speed estimation has been tested experimentally in different modes of operation. The results obtained show the ability of online adaptation of the controller parameter for MRAS to estimate real speed even at low speed and under load condition
Keywords: ADALINE; adaptive speed estimation; artificial neural network; induction motor; Luenberger; MRAS; sensorless control.
Detection of Threatening User Accounts on Twitter Social Media Database
by Asha Kumari, Balkishan
Abstract: In this technical era, online social media platforms such as Twitter, Facebook, WhatsApp, WeChat, QZone, etc. are instrumental to provide global human connectivity. These social platforms have provided access to the user to the extent that they can fearlessly post and generate a huge amount of data. Although this data is useful to generate useful information, the database contains many of the malicious and threatening users that post the suspicious and fake content on the social media for the personal or organizational advantage. This demands to generate a system that can detect suspicious content and their respective user accounts. In this paper, an Ant Colony Optimization based system for Threatening Account Detection (ACOTAD) is proposed. Ant Colony Optimization helps to evaluate the connections among different twitter users. The connections among the different Twitter users are determined by the pheromone quality among the edges of the path traveled by individual artificial ants. Better the quality of pheromone indicates the strong connection of one user with another. This research work considers the experimentation on Twitter based Social Honeypot database. The feature set related to Twitter users, their accounts, their publically posted tweets, and their connections are considered for evaluation. The results of the proposed system are determined in terms of precision, recall, f-measure, true positive rate, and false positive rate. The calculated results in terms of evaluation metrics indicate the superiority of proposed concept in comparison with state of art techniques.
Keywords: Online Social Media; Twitter; Suspicious Activity; Threatening Users; Ant Colony Optimization; Swarm Intelligence; Twitter Microblogs.