Showing papers in "Indonesian Journal of Electrical Engineering and Computer Science in 2016"

Wireless Indoor Localization Systems and Techniques: Survey and Comparative Study

Abstract: The popularity, great influence and huge importance made wireless indoor localization has a unique touch, as well its wide successful on positioning and tracking systems for both human and assists also contributing to take the lead from outdoor systems in the scope of the recent research works. In this work, we will attempt to provide a survey of the existing indoor positioning solutions and attempt to classify different its techniques and systems. Five typical location predication approaches (triangulation, fingerprinting, proximity, vision analysis and trilateration) are considered here in order to analysis and provide the reader a review of the recent advances in wireless indoor localization techniques and systems to have a good understanding of state of the art technologies and motivate new research efforts in this promising direction. For these reasons, existing wireless localization position systems and location estimation schemes are reviewed. We also made a comparison among the related techniques and systems along with conclusions and future trends to identify some possible areas of enhancements.

The User Satisfaction Perspectives of the Information System Projects

Abstract: It is inevitable that information system (IS) has been the essential role in the management and academic functions of the higher education institutions (HEIs) in developing countries, like Indonesia. In this country, despite the IS project developments have been performed since 1980s, but its performances tended still low. The efforts have been performed for responding this issue, but the studies have been remained in limited number. Accordingly, this study was carried out to know what factors that influence the satisfactions, in respect of its significant effects towards the project performances. The internal stakeholders were the population, which was sampled using the purpose random sampling. The 52 (87%) paper based data and the 61 (9%) online ones were then used in the data analysis stage with the partial least squares-structural equation modeling (PLS-SEM) method. The results revealed that, besides the system and service qualities directly affected the endogenous variable, the institutional contexts also indirectly influenced the variable. The study remained limitations, but its findings may theoretically and practically contributes towards the user satisfaction measurement, especially from the perspectives of the Islamic HEIs in Indonesia.

Tuning Methods of PID Controller for DC Motor Speed Control

Abstract: The traditional PID controllers are used for a long time to control the DC motor for many industrial processes, that because of the simplicity, flexibility, and satisfactory performance of this type of controller. This paper discusses the basic PID tuning method (Ziegler-Nichols) and its modification (Chien-Hrones-Reswick). Also, analysis the speed control DC motor response using the PID controller parameters that result from the tuning methods mentioned earlier. Moreover, explain the advantage and disadvantage of each formula of these methods. GUL/MATLAB windows used to implementing both methods to create more comfortable and friendly environment for better understanding of the PID controller tuning methods formula for engineering students and practicing engineers.

Power system design using firefly algorithm for dynamic stability enhancement

Abstract: Utilising additional devices in power systems have been developed by industry. Devices such as a Power System Stabilizer (PSS) and a Superconducting Magnetic Energy Storage (SMES) are commonly employed in industry. This work investigated the coordination of a PSS and SMES applied to a power system to enhance dynamic stability. To obtain optimal coordination, the parameters of the PSS and SMES are tuned using the Firefly Algorithm (FA). The simulation of the power system, PSS, and SMES has been performed using MATLAB and Simulink, and the FA run in Matlab. For testing the small signal stability, the eigenvalue of the system will be investigated, while for dynamic stability the system will be given an external disturbance. The rotor angle and frequency deviation of the power system are compared without a controller, with a PSS and SMES included, and with the PSS and SMES tuned by FA. The simulation results show that the proposed system can improve not only small signal stability (steady state stability) but also dynamic stability.

Modeling and Simulation of MPPT-SEPIC Combined Bidirectional Control Inverse KY Converter Using ANFIS in Microgrid System

Abstract: Photovoltaic system (PV) is widely used in various renewable energy application. The main problem of PV system is how to get the maximum output power which is integrated in microgrid system. Furthermore, the redundancy output power generated by on a distribution system should also be considered. This study utilizes the excess power for energy storage using bidirectional of KY inverse converter. Since the DC voltage which generated by PV and the energy storage will be converted into AC voltage using inverter toward load. This paper proposes ANFIS as search optimization method using SEPIC converter with a maximum efficiency of 99.95% to impact to power generation performance in microgrid system.

A Review on Modulation Strategies of Multi Level Inverter

Abstract: This review develop different switching methods for Multi Level Inverter (MLI). The switching methods proposed in this paper are to compare various methods and to predict exact switching method for different application based upon its quality of the outputs. The performance of the inverter is analyzed with the parameters like THD (Total Harmonic Injection), V RMS (fundamental), CF (Crest Factor), FF (Form Factor) and DF (Distortion Factor). From the various non PWM (Pulse Width Modulation) and PWM methods the analysis are method to identify the exact PWM strategies for specific applications.

Optimal Power Flow using the Moth Flam Optimizer: A case study of the Algerian power system

Abstract: In this paper, a new technique of optimization known as Moth-Flam Optimizer (MFO) has been proposed to solve the problem of the Optimal Power Flow (OPF) in the interconnected power system, taking into account the set of equality and inequality constraints. The proposed algorithm has been presented to the Algerian power system network for a variety of objectives. The obtained results are compared with recently published algorithms such as; as the Artificial Bee Colony (ABC), and other meta-heuristics. Simulation results clearly reveal the effectiveness and the robustness of the proposed algorithm for solving the OPF problem.

Optimization of Ship’s Route Scheduling Using Genetic Algorithm

Abstract: Route scheduling is a quite complicated process because it involves some determinant factors. Several methods have been used to help resolve the NP-hard problems. This research uses genetic algorithm to assist in optimizing ship scheduling, that where there are several ports to be visited by some ships. The goal is to divide the ship to go to a specific port so that each port is only visited by one ship to minimize the total distance of all ships. The computational experiment produces optimal parameters such as the number of popsize is 30, the number of generations is 100, crossover rate value is 0.3 and mutation rate values is 0.7. The final results is an optimal ship route by minimizing the distance of each ship.

A Review on LSB Substitution and PVD Based Image Steganography Techniques

Abstract: There has been a tremendous growth in Information and Communication technologies during the last decade. Internet has become the dominant media for data communication. But the secrecy of the data is to be taken care. Steganography is a technique for achieving secrecy for the data communicated in Internet. This paper presents a review of the steganography techniques based on least significant bit (LSB) substitution and pixel value differencing (PVD). The various techniques proposed in the literature are discussed and possible comparison is done along with their respective merits. The comparison parameters considered are, (i) hiding capacity, (ii) distortion measure, (iii) security, and (iv) computational complexity.

Big Data Platforms and Techniques

Abstract: Data is growing at unprecedented rate and has led to huge volume generated; the data sources include mobile, internet and sensors. This voluminous data is generated and updated at high velocity by batch and streaming platforms. This data is also varied along structured and unstructured types. This volume, velocity and variety of data led to the term big data. Big data has been premised to contain untapped knowledge, its exploration and exploitation is termed big data analytics. This literature reviewed platforms such as batch processing, real time processing and interactive analytics used in big data environments. Techniques used for big data are machine learning, Data Mining, Neural Network and Deep Learning. There are big data architecture offerings from Microsoft, IBM and National Institute of Standards and Technology. Big data potentials can transform economies and reduce running cost of institutions. Big data has challenges such as storage, computation, security and privacy

Backstepping Approach for Autonomous Mobile Robot Trajectory Tracking

Abstract: This paper proposes a backstepping controller design for trajectory tracking of unicycle-type mobile robots The main object of the control algorithms developed is to design a robust output tracking controller The design of the controller is based on the lyapunov theorem, kinematic tracking controller of an unicycle-like mobile robot is used to provides the desired values of the linear and angular velocities for the given trajectory A Lyapunov-based stability analysis is presented to guarantee the robot stability of the tracking errors Simulation and experimental results show the effectiveness of the proposed robust controller in term of accuracy and stability under different load conditions

Voltage Instability Analysis for Electrical Power System Using Voltage Stabilty Margin and Modal Analysis

Abstract: Voltage instability analysis in electric power system is one of the most important factors in order to maintain the equilibrium of the power system. A power system is said to be unstable if the system is not able to maintain the voltage at all buses in the system remain unchanged after the system is being subjected to a disturbance.The research work presented in this paper is about the analysis of voltage instability of electric power system by using voltage stability margin (VSM), load real power (P) margin, reactive power (Q) margin, reactive power-voltage (QV) and real power-voltage (PV) modal analysis. IEEE 30-bus system has been chosen as the power system. The load flow analysis are simulated by using Power World Simulator software version 16. Both QV and PV modal analysis were done by using MATLAB application software.

Automatic Voltage Generation Control for Two Area Power System Based on Particle Swarm Optimization

Abstract: This article presentes a PID controller for two area power system equipped with both automatic generation control and automatic voltage regulator loops. The research has been done to control two area power systems with PSO optimized self-tuning PID controller. The comparison between different controllers and the suggested PSO based controller illustrates that the proposed controller can generate the best dynamic response for a step load change. For this purpose, MATLAB-Simulink software is used. The obtained results are promising.

Bandwidth Enhancement of Rectangular Microstrip Patch Antenna using Defected Ground Structure

Abstract: This paper presents the bandwidth enhancement of a Proximity Coupled Feed Rectangular Microstrip Patch Antenna using a new Defected Ground Structure - an ‘inverted SHA’ shaped slot on the ground plane of the proximity coupled feed rectangular Microstrip patch antenna. The parameters such as Bandwidth, Return loss, VSWR and Radiation efficiency are improved in the proposed antenna than simple proximity coupled feed rectangular Microstrip patch antenna without Defected Ground Structure. A comparison is also shown for the proposed Microstrip patch antenna with the antenna structure without Defected Ground Structure. The proposed antenna resonates in S-band at frequency of 2.4 GHz with bandwidth of 180 MHz. A very good return loss of -47.9223 dB is obtained for the Microstrip patch antenna with an ’inverted SHA’ shaped Defected Ground Structure. Implementing an ‘inverted SHA’ shaped defect in the ground plane of the proximity coupled feed rectangular Microstrip patch antenna results in 5.3% improvement in bandwidth with 16.01% reduction in the overall area of the ground plane as compared to the Microstrip patch antenna without Defected Ground Structure. Full Text: PDF DOI: http://dx.doi.org/10.11591/ijeecs.v3.i2.pp428-434

Investigation of Solar Panel Performance Based on Different Wind Velocity Using ANSYS Software

Abstract: The low conversion energy efficiency of solar panel is affected by the several environmental issues. Solar radiation, ambient temperature, dust accumulation and wind velocity are the environmental problems. This main goal of this paper is to understanding the solar panel behavior under varying of wind velocity amounts. A three-dimension (3-D) model of solar panel is conducted in the present investigation. The solar panel model is simulated under given operating condition and different amounts of wind velocity. Four different of wind velocity value of 0 m/s, 0.43 m/s, 2.5 m/s and 6.95 m/s was selected to examine the solar panel performance. The simulation results are obtained with ANSYS simulation software. The temperature distribution of the solar panel model will be discussed in this current paper. The simulation result is showed highest wind velocity can be provided good cooling effect for the solar panel model in order to enable the solar panel can be operated to perform well at lower temperature.

Tuning of PID Controller for Load Frequency Control Problem via Harmony Search Algorithm

Abstract: In this paper, a new artificial intelligence technique, Harmony Search (HS), will be used for the optimization of a classical order PID for a two-area load frequency control (LFC) model using the participation factor concept. The HS has four main variants, these variants had been used for the optimization of classical order PID controllers in case of centralized control scheme, the results had been compared to select and recommend the best HS variant. Then, this best HS variant had been used for the tuning of PID controllers in case of decentralized scheme.

A Review on Machine Translation Approaches

Abstract: There is need to fill the gap between two entities which use different languages to communicate which can be done either via human translation or machine translation means. The world has over 7000 living natural languages, thus making it costly if human translation route is taken hence need for machine translation. There is need to know the available machine translation approaches and their requirement in order to decide which one suits for particular languages or not, hence the motivation for this survey. The survey provide overview and architectures of the three major techniques available namely: rule based translation, corpus based translation and hybrid based translation plus their subcategories available in each approach.

An Image Processing Method to Convert RGB Image into Binary

Abstract: It is important in image processing to extract objects from their background into binary image. Binary image is used as input to feature extraction process and have an important role in generating unique feature to distinguish several classes in pattern recognition. This paper propose an image processing algorithm to obtain a binary image from RGB. The results showed that the binary image of the proposed algorithm contained the desired object.

Wireless Body Area Networks Routing Protocols: A Review

Abstract: Wireless Body Area Networks (WBANs) are designed for monitoring the patient’s conditions and assist them in critical health situation. Tiny size wireless sensors sense the data related to patient health. Further, the sensed data sends to base station for further process. The base station sends the data to the nearest sink node or to the nearest medical center or hospital. In order to complete this entire process, wireless sensors need efficient routing without any error or delay. The routing protocols have been suffered with various challenges and issues which are related to the routing and energy issues. This paper provides a comprehensive survey of important existing routing protocols for WBANs. Paper also discusses the protocols strengths, limitations with their critical analysis.

Optimization of Discrete Cosine Transform-Based Image Watermarking by Genetics Algorithm

Abstract: data in an image file is needed by its owner to set his ownership in a logo as a watermark embedded in the image file. Hiding the logo in the image was done in several methods. One of the method is domain transform using 2D-DCT in which data is embedded in frequency domain of the image. First, the host RGB image is converted to certain color space. The available and chosen color spaces are RGB, YCbCr or NTSC. The layer in which the watermark is embedded also can be selected. The available choices are 1 st layer, 2 nd layer, 3 rd layer, 1 st & 2 nd layer, 2 nd & 3 rd layer, 1 st & 3 rd layer and all layers. After the selected layer of image in certain color space is transformed in block based to frequency domain by DCT, one bit watermark is embedded on the AC coefficient of each block such a way that the bit is represented by specific value called delta in a zigzag and vary length of pixel. The vary parameters optimized by Genetics Algorithm are selected color space, selected layer, block size, length of pixel to be embedded by one bit watermark, and delta. Bit “1” is represented by +delta, and bit “0” is represented by –delta in vary length of pixel after zigzag. The simulation result performs that GA is useful to search the value of parameter that produces controllable the combination between robustness, invisibility and capacity. Thus, GA improves the method by determining the exact value of parameter achieving BER, PSNR and payload.

Finding Kicking Range of Sepak Takraw Game: Fuzzy Logic and Dempster-Shafer Theory Approach

Abstract: Sepak takraw is played by two regus, each consisting of three players. One of the three players shall be at the back and he is called a Tekong. The other two players shall be in front, one on the left and the other on the right. Having volley kicked a throw from the net by a team mate, the ball must then travel over the net to begin play. During the service, as soon as the Tekong kicks the ball, all the players are allowed to move about freely in their respective courts. The novel approach is the integration within a Tsukamoto's Fuzzy reasoning and inferences for evidential reasoning based on Dempster-Shafer theory. Sepak takraw is a highly complex net-barrier kicking sport that involves dazzling displays of quick reflexes, acrobatic twists, turns and swerves of the agile human body movement. Because of the human’s involvement in the game, the Fuzzy Logic type reasoning are the most appropriate. The individual rule outputs of Tsukamoto's Fuzzy reasoning scheme are crisp numbers, and therefore, the functional relationship between the input vector and the system output can be relatively easily identified. The result reveals that if Tekong is kick far and front player is kick near then another regu's player is kick far, if Tekong is kick near and front player is kick far then another regu's player is kick near, moreover possibility of kicking range is another regu's player is kick far in kicking range.

Development of Quran Reciter Identification System Using MFCC and Neural Network

Abstract: Currently, the Quran is recited by so many reciters with different ways and voices. Some people like to listen to this reciter and others like to listen to other reciters. Sometimes we hear a very nice recitation of al-Quran and want to know who the reciter is. Therefore, this paper is about the development of Quran reciter recognition and identification system based on Mel Frequency Cepstral Coefficient (MFCC) feature extraction and artificial neural network (ANN). From every speech, characteristics from the utterances will be extracted through neural network model. In this paper a database of five Quran reciters is created and used in training and testing. The feature vector will be fed into Neural Network back propagation learning algorithm for training and identification processes of different speakers. Consequently, 91.2% of the successful match between targets and input occurred with certain number of hidden layers which shows how efficient are Mel Frequency Cepstral Coefficient (MFCC) feature extraction and artificial neural network (ANN) in identifying the reciter voice perfectly.

Dimensionality Reduction and Classification of Hyperspectral Images using Genetic Algorithm

Abstract: This paper presents genetic algorithm based band selection and classification on hyperspectral image data set. Hyperspectral remote sensors collect image data for a large number of narrow, adjacent spectral bands. Every pixel in hyperspectral image involves a continuous spectrum that is used to classify the objects with great detail and precision. In this paper, first filtering based on 2-D Empirical mode decomposition method is used to remove any noisy components in each band of the hyperspectral data. After filtering, band selection is done using genetic algorithm in-order to remove bands that convey less information. This dimensionality reduction minimizes many requirements such as storage space, computational load, communication bandwidth etc which is imposed on the unsupervised classification algorithms. Next image fusion is performed on the selected hyperspectral bands to selectively merge the maximum possible features from the selected images to form a single image. This fused image is classified using genetic algorithm. Three different indices, such as K-means Index (KMI) and Jm measure are used as objective functions. This method increases classification accuracy and performance of hyperspectral image than without dimensionality reduction.

SIP Authentication Protocols Based On Elliptic Curve Cryptography: Survey and Comparison

Abstract: Session Initiation Protocol (SIP) is the most popular signaling protocol using in order to establish, modify and terminate the session multimedia between different participants. It was selected by the Third Generation Project Partnership (3GPP) as a multimedia application protocol in 3G mobile networks. SIP is the protocol currently used for signaling ToIP calls. The security of SIP is becoming more and more important. Authentication is the most important security service required by SIP. To ensure a secured communication, many SIP authentication protocols have been proposed. This work provides an overview of the proposed schemes based on elliptic curve cryptography. Those proposed schemes are analyzed in security consideration and the computational cost.

Induction Motors Stator Fault Analysis based on Artificial Intelligence

Abstract: This article presents a method for fault detection and diagnosis of stator inter-turn short circuit in three phase induction machines. The technique is based on the stator current and modelling in the dq frame using an Adaptive Neuro-Fuzzy artificial intelligence approach. The developed fault analysis method is illustrated using MATLAB simulations. The obtained results are promising based on the new fault detection approach.

Image Fusion in Hyperspectral Image Classification using Genetic Algorithm

Abstract: Hyperspectral remote sensors collect image data for a large number of narrow, adjacent spectral bands Every pixel in hyperspectral image involves a continuous spectrum that is used to classify the objects with great detail and precision This paper presents hyperspectral image classification mechanism using genetic algorithm with empirical mode decomposition and image fusion used in preprocessing stage 2-D Empirical mode decomposition method is used to remove any noisy components in each band of the hyperspectral data After filtering, image fusion is performed on the hyperspectral bands to selectively merge the maximum possible features from the source images to form a single image This fused image is classified using genetic algorithm Different indices, such as K-means (KMI), Davies-Bouldin Index (DBI), and Xie-Beni Index (XBI) are used as objective functions This method increases classification accuracy of hyperspectral image

A Novel Selective and Sensitive Electrochemical Sensor for Insulin Detection

Abstract: A novel selective and sensitive electrochemical sensor for insulin detection has been fabricated and investigated. The electrochemical sensor was made from a mixture of silica gel, chitosan and nickel hydroxide (Ni(OH) 2 ) nanoparticles, which was mounted on a silver wire and covered by a glass tube (silica gel/chitosan/Ni(OH) 2 nanoparticles paste electrode). The sensor was characterized using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The anodic and cathodic currents of the silica gel/chitosan paste electrode with Ni(OH) 2 are 580 mA and -750 mA, respectively. Without Ni(OH) 2 , however, the currents are 150 mA and -250 mA, respectively. The sensitivity and limit of detection of the silica gel/chitosan/Ni(OH) 2 nanoparticle paste electrode for insulin detection are 5573 × 10 ­ 5 pA/pMcm 2 and 0.25 pM, respectively. The sensor also shows good reproducibility of measurement for 35 days with an RSD of 0.29%. The fabricated electrodes also show good reproducibility, with an RSD of 1.39%.

Design a Optimum MPPT Controller for Solar Energy System

Abstract: Solar energy is compared to be the best potential source of renewable energy in Pacific region. For this reason a photovoltaic cell is needed to harvest this kind of energy, gathering the most of it and the PV having a good efficiency. The maximum efficiency is achieved when the PV works at its Maximum Power Point which entirely depends on the irradiation and temperature. This paper proposes a new design of hybrid Maximum Power Point Tracking and a comparative study is made with various existing MPPT techniques which include Perturb and Observe method, Incremental Conductance and Fuzzy Logic. From the comprehensive comparison study between existing MPPT technique and the proposed MPPT technique/theory, a hardware setup was demonstrated to verify the proposed design by charge controller in photovoltaic systems to which maximize the output power under various lighting conditions. The design is based on the computed results using the buck-boost DC-DC conveter. From the simulation, the proposed method tends to show better performance with almost no oscillations around the MPP.

A New Hybrid Particle Swarm Optimization and Greedy for 0-1 Knapsack Problem

Abstract: This paper proposes a new binary particle swarm optimization with a greedy strategy to solve 0-1 knapsack problem. Two constraint handling techniques are consider to cooperation with binary particle swarm optimization that are penalty function and greedy. The sigmoid transfer function is used to convert real code to binary code. The experimental results have proven the superior performance of the proposed algorithm.

Mining Association Rules: A Case Study on Benchmark Dense Data

Abstract: Data mining is the process of discovering knowledge and previously unknown pattern from large amount of data. The association rule mining (ARM) has been in trend where a new pattern analysis can be discovered to project for an important prediction about any issues. Since the first introduction of frequent itemset mining, it has received a major attention among researchers and various efficient and sophisticated algorithms have been proposed to do frequent itemset mining. Among the best-known algorithms are Apriori and FP-Growth. In this paper, we explore these algorithms and comparing their results in generating association rules based on benchmark dense datasets. The datasets are taken from frequent itemset mining data repository. The two algorithms are implemented in Rapid Miner 5.3.007 and the performance results are shown as comparison. FP-Growth is found to be better algorithm when encountering the support-confidence framework.