bluuebunny/crossref_metadata_2025 huggingface dataset:
Experiment-based Study on the Impact of Soiling on PV System’s Performance
Authors: Jamil Wan Juzaili , Abdul Rahman Hasimah , Baharin Kyairul Azmi
Published: October 2019

Soiling refers to the accumulation of dust on PV modules which plays a small but significant role in degrading solar photovoltaics system efficiency. Its effect cannot be generalized because the severity is location and environment dependent. Currently, there are limited studies available on the soiling effect in the hot and humid Malaysian tropical climate. This paper presents an experimental-based approach to investigate the effect of soiling on PV module performance in a tropical climate. The experiment involved a full day exposure of a polycrystalline PV module in the outdoors with accelerated artificial dust loading and an indoor experiment for testing variable dust dimensions. The findings show that for the worst case, the module’s output can be reduced by as much as 20%.
Performance Analysis of Digital Modulation for Coherent Detection of OFDM Scheme on Radio over Fiber System
Authors: Khair Fauza , Hario P Fakhriy , Mustika I Wayan , Setiyanto Budi
Published: October 2019

Radio over fiber (RoF) system with the coherent detection offers high linearity for the transparent transport of high-frequency microwave signals, and better receiver sensitivity compared with intensity-modulated direct detection systems. The purpose of this paper is to analyze the performance of digital modulation for coherent detection of orthogonal frequency division multiplexing (OFDM) scheme on RoF system at 10 Gbps up to 100 km fiber length. The results show that coherent detection of OFDM-RoF system with 16 quadrature amplitude modulation (16-QAM) has the value of  bit error rate (BER) and the symbol error rate (SER) is very low and its constellation is better compared with other modulation formats (4-QAM, quadrature phase shift keying (QPSK), 8-PSK and 16-PSK), which BER 16-QAM is 0.053 and SER is 15.7%. The results also show that BER value of 4-QAM and QPSK relatively similar to fiber length variations. In general, an increasing value of the BER and SER for each modulation format are almost equal to the fiber length of 60-70 km (Region I and II). However, there is a significant increase in the value of BER in fiber length of 80-100 km (Region III. A and III. B) for the modulation of 4-QAM, QPSK, 8-PSK, and 16-PSK.
Efficient Low-Complexity Digital Predistortion for Power Amplifier Linearization
Authors: Monther Yousif Siba , M. Sidek Roslina , Sabah Mekki Anwer , Sulaiman Nasri , Varahram Pooria
Published: October 2019

In this paper, a low-complexity model is proposed for linearizing power amplifiers with memory effects using the digital predistortion (DPD) technique. In the proposed model, the linear, low-order nonlinear and high-order nonlinear memory effects are computed separately to provide flexibility in controlling the model parameters so that both high performance and low model complexity can be achieved. The performance of the proposed model is assessed based on experimental measurements of a commercial class AB power amplifier by applying a single-carrier wideband code division multiple access (WCDMA) signal. The linearity performance and the model complexity of the proposed model are compared with the memory polynomial (MP) model and the DPD with single-feedback model. The experimental results show that the proposed model outperforms the latter model by 5 dB in terms of adjacent channel leakage power ratio (ACLR) with comparable complexity. Compared to MP model, the proposed model shows improved ACLR performance by 10.8 dB with a reduction in the complexity by 17% in terms of number of floating-point operations (FLOPs) and 18% in terms of number of model coefficients.
An Adaptive Neuro-Fuzzy Inference System in Assessment of Technical Losses in Distribution Networks
Authors: Mlakić Dragan , Nikolovski Srete N , Knežević Goran
Published: October 2019

The losses in distribution networks have always been key elements in predicting investment, planning work, evaluating the efficiency and effectiveness of a network. This paper elaborates on the use of fuzzy logic systems in analyzing the data from a particular substation area predicting losses in the low voltage network. The data collected from the field were obtained from the Automatic Meter Reading (AMR) and Automatic Meter Management (AMM) systems. The AMR system is fully implemented in EPHZHB and integrated within the network infrastructure at secondary level substations 35/10kV and 10(20)/0.4 kV. The AMM system is partially implemented in the areas of electrical energy consumers; precisely, in accounting meters. Daily information gathered from these systems is of great value for the calculation of technical and non-technical losses. Fuzzy logic in combination with the Artificial Neural Networks implemented via the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. Finally, FIS Sugeno, FIS Mamdani and ANFIS are compared with the measured data from smart meters and presented with their errors and graphs.
Machine Learning Techniques on Multidimensional Curve Fitting Data Based on R- Square and Chi-Square Methods
Authors: P Vidyullatha , Rao D. Rajeswara
Published: October 2019

Curve fitting is one of the procedures in data analysis and is helpful for prediction analysis showing graphically how the data points are related to one another whether it is in linear or non-linear model. Usually, the curve fit will find the concentrates along the curve or it will just use to smooth the data and upgrade the presence of the plot. Curve fitting checks the relationship between independent variables and dependent variables with the objective of characterizing a good fit model. Curve fitting finds mathematical equation that best fits given information. In this paper, 150 unorganized data points of environmental variables are used to develop Linear and non-linear data modelling which are evaluated by utilizing 3 dimensional ‘Sftool’ and ‘Labfit’ machine learning techniques. In Linear model, the best estimations of the coefficients are realized by the estimation of R- square turns in to one and in Non-Linear models with least Chi-square are the criteria. 
A Feed forward Neural Network MPPT Control Strategy Applied to a Modified Cuk Converter
Authors: Makhloufi Mohamed Tahar , Abdessemed Yassine , Khireddine Mohamed Salah
Published: October 2019

This paper presents an intelligent control strategy that uses a feedforward artificial neural network in order to improve the performance of the MPPT (Maximum Power Point Tracker) MPPT photovoltaic (PV) power system based on a modified Cuk converter. The proposed neural network control (NNC) strategy is designed to produce regulated variable DC output voltage. The mathematical model of Cuk converter and artificial neural network algorithm is derived. Cuk converter has some advantages compared to other type of converters. However the nonlinearity characteristic of the Cuk converter due to the switching technique is difficult to be handled by conventional controller. To overcome this problem, a neural network controller with online learning back propagation algorithm is developed. The NNC designed tracked the converter voltage output and improve the dynamic performance regardless load disturbances and supply variations. The proposed controller effectiveness during dynamic transient response is then analyze and verified using MATLAB-Simulink. Simulation results confirm the excellent performance of the proposed NNC.
A Fuzzy Logic Control Strategy for Buck PFC Converter Based 4- Switch VSI Fed BLDC Motor Drive
Authors: Ramesh V. , Latha Y. Kusuma
Published: October 2019

In this paper, a new Buck PFC dc–dc converter topology along with fuzzy logic control for a permanent magnet (PM) brushless dc motor (PMBLDCM) has been proposed. The proposed buck-PFC converter topology is on single stage power factor correction converter. A concept of dc link voltage control which is proportional to speed of the PMBLDCM is used in this paper. The stator currents of the PMBLDCM during step change in the reference speed are controlled within the specified limits by an addition of a rate limiter in the reference dc link voltage. The effectives of the proposed control strategy of PMBLDCM drive is validated through simulation results.
Vision-based Crack Identification on the Concrete Slab Surface using Fuzzy Reasoning Rules and Self-Organizing
Authors: Kim Kwang Baek , Park Hyun Jun , Song Doo Heon
Published: October 2019

Identifying cracks on the surface of concrete slab structure is important for structure stability maintenance. In order to avoid subjective visual inspection, it is necessary to develop an automated identification and measuring system by vision based method. Although there have been some intelligent computerized inspection methods, they are sensitive to noise due to the brightness contrast and objects such as forms and joints of certain size often falsely classified as cracks. In this paper, we propose a new fuzzy logic based image processing method that extracts cracks from concrete slab structure including small cracks that were often neglected as noise. We extract candidate crack areas by applying fuzzy method with three color channel values of concrete slab structure. Then further refinement processes are performed with Self Organizing Map algorithm and density based noise removal process to obtain basic crack characteristic attributes for further analysis. Experimental result verifies that the proposed method is sufficiently identified cracks with various sizes with high accuracy (97.3%) among 1319 ground truth cracks from 30 images.
Feasibility of Power Control for Multi-Channel User in Inter-Femtocell Network
Authors: Isnawati Anggun Fitrian , Hidayat Risanuri , Sulistyo Selo , Mustika I Wayan
Published: October 2019

The importance of power control feasibility is closely related to the direct implementation of the system, which is related to interference between users in the femtocell network and also related to the optimal use of power transmit that can create a long-lasting battery. Implementation of the feasibility of power control in this study is focused on the centralized femtocell network with a multi-channel user. The research method in this study is based on the use of feasible solution algorithms in power control by observing the output of power vector that should be valued non-negative, which means that it can be implemented. This result indicates that all of user can reach the specified target SINR. SINR users will increase when there are additional channels for user groups. The average power of user will also decrease while increasing the amount of provided channels. The greater number of users in a user group, the less value SINR can be obtained
Parasitic Strip Loaded Dual Band Notch Circular Monopole Antenna with Defected Ground Structure
Authors: Sundar P Syam , K Kotamraju Sarat , Madhav B T P , Sreehari M , Rao K Raghavendra , Prathyusha L , Pravallika Y
Published: October 2019

In this article a parasitic strip loaded monopole antennas are designed to notch dual and triple bands. The designed models are constructed on one side of the substrate material and on the other end defected ground structures are implemented. The basic antenna comprises a tuning stub and a ground plane with tapered shape slot as DGS. Another model is constructed with circular monopole radiating element on front side and similar kind of ground structure used in the basic rectangular tuning stub antenna. To create notched bands with tuning stubs, two symmetrical parasitic slits are placed inside the slot of the ground plane. The basic model is of the rectangular stub notching triple band and the circular tuning stub antenna notching dual band. Dual band notched circular tuning stub antenna is prototyped on FR4 substrate and measured results from vector network analyzer are compared with simulation results of HFSS for validation.
Behavioral Intention to Use IoT Health Device: The Role of Perceived Usefulness, Facilitated Appropriation, Big Five Personality Traits, and Cultural Value Orientations
Authors: Prayoga Tommy , Abraham Juneman
Published: October 2019

As one of the biggest technology breakthrough, Internet of Things (IoT) has been gaining much attention in every industry it’s involved in. Especially in the healthcare sector, IoT health devices have been proved to have a great impact on individual’s health regulation. Through many beneficial features from tracking sleep patterns to tracking chemicals in the blood, individuals will gain a better understanding of their body and how to keep it healthy. However, great benefit is one thing, and it does not immediately equal users’ acceptance. In this study, we seek to find elements that we propose to have an impact in predicting user’s intention of using IoT health device and integrate them into a model. We will analyze user’s technology acceptance from the perspective of Technology Acceptance Model, using Perceived Usefulness as the main predictor for Behavioral Intention. We also integrate Personality Traits and Facilitated Appropriation as factors to determine Perceived Usefulness, and finally using Hofstede’s Cultural Value, we seek to determine the antecedent of Facilitated Appropriation. The research design was correlational predictive, with path analysis technique. Participants of this study were 186 college students from IT campus (75 males, 111 females; mean of age = 20.86 years of old, standard deviation of age = 2.01 years) across Greater Jakarta. The results showed that Perceived Usefulness can predict intention to use the device. However, Personality Traits were found to be irrelevant in predicting perceived usefulness. Only Facilitated Appropriation could predict perceived usefulness. Among cultural values proposed, only the Long-term Orientation can predict user’s tendency to engage in Facilitated Appropriation. Also, we did not find any correlation between age and all technology acceptance factors (Behavioral Intention, Perceived Usefulness, and Facilitated Appropriation).
An Optimizing Approach for Multi Constraints Reassignment Problem of Human Resources
Authors: Said Tkatek , Otman Abdoun , Jaafar Abouchabaka , Najat Rafalia
Published: October 2019

This paper presents an effective approach to optimize the reassignment of Human Resources in the enterprise that is formed by several units of productions to take into consideration the human characteristics. This approach consists of two steps; the first step is to formalize the studied problem that is practically take the form of the generalized assignment problem (GAP) known as NP-hard problem. Additionally, the variables in the formulation of our problem are interlinked by certain constraints. These two proprieties can to justify the important complexity of this problem. The second step is focused to solve this complex problem by using the genetic algorithm. We present the experimentally result for justifying the validity of the proposed approach. So, the solution obtained allowed us to get an optimal assignment of personnel that can lead to improve the average productivity or ratability or at least ensure its equilibration within sites of enterprise.
An Investigation Study on Optimizing Enterprise Resource Planning (ERP) Implementation in Emerging Public University: Al Baha University Case Study
Authors: Shakkah Moh’D Suliman , Alaqeel Khaled , Alfageeh Ali , Budiarto Rahmat
Published: October 2019

This project investigates the correlation between the organizational readiness in Albaha University (ABU) and the respective Critical Success Factors (CSFs) with regards to the Enterprise Resource Planning (ERP) implementation. The investigation also considers some suggestions to improve the ABU’s ERP systems and roadmap towards the self –development strategy and reduce vendor-dependency. A survey regarding ERP to the end-user, expert and developer in ABU was conducted. The analysis of the results in this work confirmed with the results of an existing work. The four significance success factors: Project Management, Business Process Re-engineering (BPR), System Integration, and Training and Education are recommended to be adopted to assure the smooth adoption of ERP at Albaha University
Design of Observer-Based Robust Power System Stabilizers
Authors: Soliman Hisham M. , Soliman Mahmoud
Published: October 2019

Power systems are subject to undesirable small oscillations that might grow to cause system shutdown and consequently great loss of national economy. The present manuscript  proposes two  designs for observer-based robust power system stabilizer (PSS) using Linear Matrix Inequality (LMI) approach to damp such oscillations. A model to describe power system dynamics for different loads is derived in the norm-bounded form. The first controller design is based on the derived model to achieve  robust stability against load variation. The design is based on a new Bilinear matrix inequality (BMI) condition. The BMI optimization  is solved interatively in terms of Linear Matrix Inequality (LMI) framework. The condition contains a symmetric positive definite full matrix to be obtained, rather than the commonly used block diagonal form. The difficulty in finding a feasible solution is thus alleviated. The resulting LMI is of small size, easy to solve. The second PSS design shifts the closed loop poles in a desired region so as to achieve a favorite  settling time and damping ratio via a non-iterative solution to a set of LMIs.  The approach provides a systematic way to design a robust output feedback PSS which  guarantees good dynamic performance for different loads. Simulation results based on single-machine and multi-machine power system models verify the ability of the proposed PSS to satisfy control objectives for a wide range of load conditions.
Five-Phase Permanent Magnetic Synchronous Motor Fed by Fault Tolerant Five Phase Voltage Source Inverter
Authors: Kesraoui Hichem , Echeikh Hamdi , Iqbal Atif , Mimouni Med Faouzi
Published: October 2019

Multiphase machines have gained attention in numerous fields of pplications such as Aircraft, ship propulsion, petrochemical and automobiles, where high reliability is required. The additional number of phases guarantees that the system continues to operate in faulty conditions compared to the traditional three-phase machine due to the high degree of freedom. Among faults able to affect multiphase system, break between a machine phase and the voltage source inverter (VSI) degrade the performance of the control. In this paper, a five-phase permanent magnet synchronous machine (PMSM) is fed through a fault tolerant voltage source inverter with new structure to ensure drive continuity when open circuit occurs. The five phase PMSM is controlled with fuzzy logic regulator to minimize disturbance impact that can arise fault condition. Paper is accomplished with real time simulations using MATLAB-Simulink in order to validate the new topology and show the effectiveness of the proposed solution.
Development of a Measuring Sensory System Based on LabVIEW for Determining Elastic Proprieties of Solid Materials
Authors: Ezzouine Zakaryae , Nakheli Abdelrhani
Published: October 2019

This article develops also a measure and prototype to allow the acquisition of real time data for display, analysis, control and storage with a proposed test program for determining the model parameters. The aim is to be able to measure, and apply moment to a specimen, and collect data from the resulting deformation in the material. At the same time, the reliability of this test system has been proved by precision analysis and data processing for a simple test validation (metal wire). The force-deformation curves of solids materials in this tensile test are measured accurately in real time, to obtain the values of solid materials mechanical property parameters, The minimal change in length of the test Specimen that can be resolved by this system is 1µm, which yields the sensitivity comprised between 10-4µm and 10-5 µm. Based on the experience that compressive tensile test have the smallest statistical scatter and that they are simplest to carry out. The measuring device can improve the measuring efficiency and accuracy distinctly while has advantages of simple configuration, low cost and high stability.
An Unsupervised Classification Technique for Detection of Flipped Orientations in Document Images
Authors: CS Vijayashree , Rani Shobha , T Vasudev
Published: October 2019

Detection of text orientation in document images is of preliminary concern prior to processing of documents by Optical Character Reader. The text direction in document images should exist generally in a specific orientation, i.e.,   text direction for any automated document reading system. The flipped text orientation leads to an unambiguous result in such fully automated systems. In this paper, we focus on development of text orientation direction detection module which can be incorporated as the perquisite process in automatic reading system. Orientation direction detection of text is performed through employing directional gradient features of document image and adapts an unsupervised learning approach for detection of flipped text orientation at which the document has been originally fed into scanning device. The unsupervised learning is built on the directional gradient features of text of document based on four possible different orientations. The algorithm is experimented on document samples of printed plain English text as well as filled in pre-printed forms of Telugu script. The outcome attained by algorithm proves to be consistent and adequate with an average accuracy around 94%.
Sliding-Mode Controller Based on Fractional Order Calculus for a Class of Nonlinear Systems
Authors: Bouarroudj Noureddine , Boukhetala Djamel , Boudjema Fares
Published: October 2019

This  paper  presents  a  new  approach  of  fractional  order  sliding  mode
controllers  (FOSMC)  for  a  class  of  nonlinear  systems  which  have  a  single input and two outputs (SITO). Firstly, two fractional order sliding surfaces S1 and S2 were proposed with an intermediate variable z transferred from S2 to S1 in order to hierarchy the two sliding surfaces. Secondly, a control law was determined  in  order  to  control  the  two  outputs.  A  sliding  control  stability condition  was  obtained  by  using  the  properties  of  the  fractional  order calculus.  Finally,  the  effectiveness  and  robustness  of  the  proposed  approach  were demonstrated by comparing its performance with the one of the conventional sliding mode controller (SMC), which is based on integer order derivatives. Simulation results were provided for the cases of controlling a ball-beam and inverted pendulum systems.
An Unequal Cluster-based Routing Protocol Based on Data Controlling for Wireless Sensor Network
Authors: Chelbi Slaheddine , Abdouli Majed , Kaddes Mourad , Duvallet Claude , Bouaziz Rafik
Published: October 2019

Wireless Sensor Networks (WSN) differ from traditional wireless communication networks in several characteristics. One of these characteristics is power awarness, due to the fact that the batteries of sensor nodes have a restricted lifetime and are difficult to be replaced. Therefore, all protocols must be designed to minimize energy consumption and preserve the longevity of the network. In this paper, we propose (i) to fairly balance the load among nodes. For this, we generate an unequal clusters size where the cluster heads (CH) election is based on energy availability, (ii) to reduce the energy consumption due to the transmission by using multiple metrics in the CH jointure process and taking into account the link cost, residual energy and number of cluster members to construct the routing tree and (iii) to minimize the number of transmissions by avoiding the unnecessary updates using sensitive data controller. Simulation results show that our Advanced Energy-Efficient Unequal Clustering (AEEUC) mechanism improves the fairness energy consumption among all sensor nodes and achieves an obvious improvement on the network lifetime.
Social Media Success for Knowledge Sharing: Instrument Content Validation
Authors: Assegaff Setiawan , Kurniabudi Kurniabudi , Hendri Hendri
Published: October 2019

Knowledge sharing is important activity for create a new knowledge. Information technology today brings big oppurtubity for people in conduct knowledge sharing. This media provides effective and competitive technology tool for knowledge sharing. The aimed of this study is to report the on process research that investigates the success of social media for sharing knowledge among scholars in Indonesia. This study focus to discuss the instrument development stages from the research especially discuss how content validity conduct for in instrument validation progress. Method for content validation progress was adopting from Beck and Gale approach in nursing area. This study resulted in a validated instrument from content validation approach.
An Optimal Energy Management System for PV/Battery Standalone System
Authors: Jyothi Vellanki Mehar , Muni T. Vijay , Lalitha S V N L
Published: October 2019

This paper presents an optimal energy management for PV/battery stand-alone system. The system comprises photovoltaic array, battery, inverter and AC loads. PV array is an uncontrollable source due to the variations in irradiations and temperature. To deliver the highest point to the load a maximum power point tracking technique is used. In synchronization with battery bank the system becomes controllable. Modes of operation of the system are defined by the battery bank. Modes of operation are based on the difference between supply and load demand. This paper project proposes an optimal energy management strategy to improve the performance of the PV system. The anticipated system performance is evaluated for varying loads in MATLAB/simulink.
Overvoltage Effects on Ground Grid Systems of Single and Two Neighboring High Voltage Substations
Authors: Luewattana Krung , Pruksanubal Aphibal
Published: October 2019

The power networks with reliable transmission and distribution systems require high voltage substations with effective ground grid systems. Therefore, this study aimed to analyze the effects of overvoltage on ground grid systems within high voltage substations in two cases, including a single substation and the two neighboring substations, by using ATP/EMTP and ANSYS softwares. The simulations were performed to show both graphs and 3D results when the fault current flowed into the ground grids of substations at various positions. In case of the single high voltage substation, the overvoltage was highest when the fault current was injected at the corner of ground grid. However, in case of two neighboring high voltage substations, the levels of overvoltage were similar between that caused by injecting the fault current at the center and at the corner of ground grid. The simulation showed that overvoltage which occurring at the ground grid of high voltage substation A could permeate to the ground grid of nearby high voltage substation B, which nevertheless depending on the how the fault current was injected at different spots. The data from these simulations and analysis can be useful for future designs of ground grid systems with high reliability.
Optimal Location of SVC using Particle Swarm Optimization and Voltage Stability Indexes
Authors: Dazahra M.N. , Elmariami F. , Belfqih A. , Boukherouaa J.
Published: October 2019

Flexible AC transmission system are widley used in power sytems to ensure voltage stability, in virtue of their high cost the choise of the best location in electrical network is essential. The porpose of this paper is to present a new method for finding the optimal location, size and number of Static Var Compensator in order to enhace the voltage stability of electrical network. The optimal solution has been found by using the evolutionary programming algorithm, particle swarm optimization, combined with voltage stability indexes used for the estimation of the voltage collapse in power systems.The proposed algorithm has been validated by application on both simulation network model IEEE 30-Buses under different load cases and the electric network model of Casablanca region in Morocco.The results of the application have been analysed and compared in each case in order to get the optimal number of Static Var Compensator to be used.
Retinal Area Segmentation using Adaptive Superpixalation and its Classification using RBFN
Authors: Singh Nimisha , Gill Rana
Published: October 2019

Retinal disease is the very important issue in medical field. To diagnose the disease, it needs to detect the true retinal area. Artefacts like eyelids and eyelashes are come along with retinal part so removal of artefacts is the big task for better diagnosis of disease into the retinal part.  In this paper, we have proposed the segmentation and use machine learning approaches to detect the true retinal part. Preprocessing is done on the original image using Gamma Normalization which helps to enhance the image  that can gives detail information about the image. Then the segmentation is performed on the Gamma Normalized image by Superpixel method. Superpixel is the group of pixel into different regions which is based on compactness and regional size. Superpixel is used to reduce the complexity of image processing task and provide suitable primitive image pattern. Then feature generation must be done and machine learning approach helps to extract true retinal area. The experimental evaluation gives the better result with accuracy of 96%.
Virtual Maintenance, Reality, and Systems: A Review
Authors: Jenab Kouroush , Moslehpour Saeid , Khoury Sam
Published: October 2019

Virtual Reality is a computer-generated, mock environment that can allow people to interact with it in a seemingly real way by using certain types of specialized equipment. It is mainly used for training or educational purposes and allows for “real-life” training in a safe and monitored environment. Virtual training can be used in many different fields such as medical, military, biomedical research, aviation, and many others. However, this paper reviews the most cited publications related to the application of virtual reality for training in the United States Military. As a result, researchers can find research venues based on the challenges, risk, and infrastructures.
Improved Denoising Method for Ultrasonic Echo with Mother Wavelet Optimization and Best-Basis Selection
Authors: Doost Mohammadi Mohammad Hossein
Published: October 2019

Weak features of ultrasonicnondestructive test signals are usually immersed in noisy signals. So, in this paper, we proposed an improved scheme for noise reduction and feature extraction based on discrete wavelet transform. The basis of the mother wavelet was selected to be matched to a given signal. Three different constraints were presented to minimize the error between the denoised and the given signal. It should be mentioned that such an optimum wavelet can represent the signal more compactly with a few large coefficients which can be considered as the signal features. Standard signals and simulated ultrasonic echo were used to evaluate the performance of the presented algorithms. Signal to error ratio was used to compare the designed wavelet performance with that of standard wavelets. Simulation results revealed that the proposed method outperformed the other presented methods and even standard wavelets. The results also has shown that the signal-based noise reduction algorithms make the feature extraction more reliable. Finally, the performance of the proposed algorithm was compared with other methods from different literatures.
The Contour Extraction of Cup in Fundus Images for Glaucoma Detection
Authors: Septiarini Anindita , Hamdani Hamdani , Khairina Dyna Marisa
Published: October 2019

Glaucoma is the second leading cause of blindness in the world; therefore the detection of glaucoma is required. The detection of glaucoma is used to distinguish whether a patient's eye is normal or glaucoma. An expert observed the structure of the retina using fundus image to detect glaucoma. In this research, we propose feature extraction method based on cup area contour using fundus images to detect glaucoma. Our proposed method has been evaluated on 44 fundus images consisting of 23 normal and 21 glaucoma. The data is divided into two parts: firstly, used to the learning phase and secondly, used to the testing phase. In order to identify the fundus images including the class of normal or glaucoma, we applied Support Vector Machines (SVM) method. The performance of our method achieves the accuracy of 94.44%.
Using Attribute Oriented Induction High Level Emerging Pattern (AOI-HEP) to Mine Frequent Patterns
Authors: Spits Warnars Harco Leslie Hendric
Published: October 2019

Frequent patterns in Attribute Oriented Induction High level Emerging Pattern (AOI-HEP), are recognized when have maximum subsumption target (superset) into contrasting (subset) datasets (contrasting ⊂ target) and having large High Emerging Pattern (HEP) growth rate and support in target dataset. HEP Frequent patterns had been successful mined with AOI-HEP upon 4 UCI machine learning datasets such as adult, breast cancer, census and IPUMS with the number of instances of 48842, 569, 2458285 and 256932 respectively and each dataset has concept hierarchies built from its five chosen attributes. There are 2 and 1 finding frequent patterns from adult and breast cancer datasets, while there is no frequent pattern from census and IPUMS datasets. The finding HEP frequent patterns from adult dataset are adult which have government workclass with an intermediate education (80.53%) and America as native country(33%). Meanwhile, the only 1 HEP frequent pattern from breast cancer dataset is breast cancer which have clump thickness type of AboutAverClump with cell size of VeryLargeSize(3.56%). Finding HEP frequent patterns with AOI-HEP are influenced by learning on high level concept in one of chosen attribute and extended experiment upon adult dataset where learn on marital-status attribute showed that there is no finding frequent pattern.
Software Reliability Using SPRT: Burr Type III Process Model
Authors: Smitha CH. , Prasad R. Satya , Kumar R. Kiran
Published: October 2019

Increased dependence on software systems elicited the assessment of their reliability, a crucial task in software development. Effective tools and mechanisms are required to facilitate the assessment of software reliability. Classical approaches like hypothesis testing are significantly time consuming as the conclusion can only be drawn after collecting huge amounts of data. Statistical method such as Sequential Analysis can be applied to arrive at a decision quickly. This paper implemented Sequential Probability Ratio Test (SPRT) for Burr Type III model based on time domain data. For this, parameters were estimated using Maximum Likelihood Estimation to apply SPRT on five real time software failure datasets borrowed from different software projects. The results exemplify that the adopted model has given a rejection decision for the used datasets.
A Novel Human STR Similarity Method using Cascade Statistical Fuzzy Rules with Tribal Information Inference
Authors: Widyanto M. Rahmat , N. Hartono Reggio , Soedarsono Nurtami
Published: October 2019

A novel human STR (Short Tandem Repeat) similarity method using cascade statistical fuzzy rules with tribal information inference is proposed. The proposed method consists of two cascade Fuzzy Inference Systems (FIS). The first FIS is to discriminate the tribal similarity, and the second FIS is to calculate the STR similarity. By using the allele marker’s statistical distribution probability density function as the membership function in the Fuzzy Rules of the first FIS, the new method makes it possible to tell the tribal similarity between two STR profiles. A 727 data acquired from tribal groups of Indonesia is used to examine the method produced promising result, being able to indicate higher tribal similarity score within a tribal group and lower similarity between tribal groups. In the light of Indonesia’s diverse tribal groups, these properties are able to be leveraged as a new way to improve the versatility of existing DNA matching algorithm.
Performance Forecasting of Share Market using Machine Learning Techniques: A Review
Authors: Kamley Sachin , Jaloree Shailesh , Thakur R.S.
Published: October 2019

Forecasting share performance becomes more challenging issue due to the enormous amount of valuable trading data stored in the stock database. Currently, existing forecasting methods are insufficient to analyze the share performance accurately. There are two main reasons for that: First, the study of existing forecasting methods is still insufficient to identify the most suitable methods for share price prediction. Second, the lack of investigations made on the factors affecting the share performance. In this regard, this study presents a systematic review of the last fifteen years on various machine learning techniques in order to analyze share performance accurately. The only objective of this study is to provide an overview of the machine learning techniques that have been used to forecast share performance. This paper also highlights a how the prediction algorithms can be used to identify the most important variables in a share market dataset. Finally, we could have succeeded to analyze share performance effectively. It could bring benefits and impacts to researchers, society, brokers and financial analysts.
Combination a Skeleton Filter and Reduction Dimension of Kernel PCA Based on Palmprint Recognition
Authors: Kusban Muhammad , Susanto Adhi , Wahyunggoro Oyas
Published: October 2019

Palmprint identification is part of biometric recognition, which attracted many researchers, especially when fusion with face identification that will be applied in the airport to hasten knowing individual identity. To accelerate the process of verification feature palms, dimension reduction method is the dominant technique to extract the feature information of palms.The mechanism will boost if the ROI images are processed prior to get normalize image enhancement.In this paper with three sample input database, a kernel PCA method used as a dimension reduction compared with three others and a skeleton filter used as a image enhancement method compared with six others. The final results show that the proposed method successfully achieve the target in terms of the processing time of $ 0.7415 $ second, the EER performance rate of 0.19 % and the success of verification process about 99,82 %.
A Novel of Repulsive Function on Artificial Potential Field for Robot Path Planning
Authors: Triharminto H. H. , Wahyunggoro O. , Adji T. B. , Cahyadi A. I. , Ardiyanto I.
Published: October 2019

In this paper, the issue of local minima associated with GNRON (Goal Nonreachable with Obstacles Nearby) has been solved on the Artificial Potential Field (APF) for robot path planning. A novel of repulsive potential function is proposed to solve the problem. The consideration of surrounding repulsive forces gives a trigger to escape from the local mi- nima. Addition of signum function on the repulsive force which considers relative distance between the robot and the goal ensures that the goal position is the global optima of the total potential. Simulation conducted to prove that the proposed algorithm can solve GNRON and local minima problem on APF. Scenario of each simulation set in different type of obs- tacle and goal condition. The results show that the proposed method is able to handle local minima and GNRON problem.
Real Time Implementation of Fuzzy Adaptive PI-sliding Mode Controller for Induction Machine Control
Authors: Habbab Mohamed , Hazzab Abdeldjebar , Sicard Pierre
Published: October 2019

In this work, a fuzzy adaptive PI-sliding mode control is proposed for Induction Motor speed control. First, an adaptive PI-sliding mode controller with a proportional plus integral equivalent control action is investigated, in which a simple adaptive algorithm is utilized for generalized soft-switching parameters. The proposed control design uses a fuzzy inference system to overcome the drawbacks of the sliding mode control in terms of high control gains and chattering to form a fuzzy sliding mode controller. The proposed controller has implemented for a 1.5kW three-Phase IM are completely carried out using a dSPACE DS1104 digital signal processor based real-time data acquisition control system, and MATLAB/Simulink environment. Digital experimental results show that the proposed controller can not only attenuate the chattering extent of the adaptive PI-sliding mode controller but can provide high-performance dynamic characteristics with regard to plant external load disturbance and reference variations. 
A Novel Technique to Enhance the Lifetime of Wireless Sensor Networks through Software Realization
Authors: Sagar Adarsh , Basavaraju T. G. , Surekha K. B.
Published: October 2019

In the most of the real world scenarios, wireless sensor networks are used. Some of the major tasks of these types of networks is to sense some information and sending it to monitoring system or tracking some activity etc. In such applications, the sensor nodes are deployed in large area and in considerably large numbers [1]-[3]. Each of these node will be having constrained resources whether it might be energy, memory or processing capability. Energy is the major resource constraint in these types of networks. Hence enough care to be taken in all aspects such that energy can be used very efficiently. Different Activities which will be taking place in a sensor node are sensing, radio operations and receiving and computing. Among all these operations, radio consumes maximum power. Hence there is a need of reducing the power consumption in such radio operations. In the proposed work a software module is developed which will reduce the number of transmissions done to the base station. The work compares the consecutively sensed data and if these data are same then the old data then the old data will be retained. In other case the newly sensed data will be sent to the sink node. This technique reduces the number of data transmissions in a significant way. With the reduced number of transmissions, the energy saved in each node will be more, which will increase the lifetime of the entire network.
Multi-objective optimal placement of distributed generations for dynamic loads
Authors: Hossain Shah Mohazzem , Chowdhury Abdul Hasib
Published: October 2019

Large amount of active power losses and low voltage profile are the two major issues concerning the integration of distributed generations with existing power system networks. High R/X ratio and long distance of radial network further aggravates the issues. Optimal placement of distributed generators can address these issues significantly by alleviating active power losses and ameliorating voltage profile in a cost effective manner. In this research, multi-objective optimal placement problem is decomposed into minimization of total active power losses, maximization of bus voltage profile enhancement and minimization of total generation cost of a power system network for static and dynamic load characteristics. Optimum utilization factor for installed generators and available loads is scaled by the analysis of yearly load-demand curve of a network. The developed algorithm of N-bus system is implemented in IEEE-14 bus standard test system to demonstrate the efficacy of the proposed method in different loading conditions.
Portable and Efficient Fingerprint Authentication System Based on a Microcontroller
Authors: García Vargas Mauricio , Hoyos Fredy Edimer , Candelo John Edwin
Published: October 2019

This paper presents the design of a fingerprint authentication system based on a simple microcontroller and the fingerprint sensor. The circuit diagram and details regarding the procedure are included. The system was programed in MPLAB and then embedded into the microcontroller. Communication between the PIC and sensor is by RS232 protocol. The results show that the system recognizes the fingerprint in less than 1 second. It is portable and there is no need for image processing. Furthermore, the system shows a high effectiveness when storing and verifying fingerprints.
Performance Evaluation of Quarter Shift Dual Tree Complex Wavelet Transform Based Multifocus Image Fusion Using Fusion rules
Authors: N. Radha , Babu T.Ranga
Published: October 2019

In this paper, multifocus image fusion using quarter shift dual tree complex wavelet transform is proposed. Multifocus image fusion is a technique that combines the partially focused regions of multiple images of the same scene into a fully focused fused image. Directional selectivity and shift invariance properties are essential to produce a high quality fused image. However conventional wavelet based fusion algorithms introduce the ringing artifacts into fused image due to lack of shift invariance and poor directionality. The quarter shift dual tree complex wavelet transform has proven to be an effective multi-resolution transform for image fusion with its directional and shift invariant properties. Experimentation with this transform led to the conclusion that the proposed method not only produce sharp details (focused regions) in fused image due to its good directionality but also removes artifacts with its shift invariance in order to get high quality fused image. Proposed method performance is compared with traditional fusion methods in terms of objective measures. 
Computer Vision Based 3D Reconstruction : A Review
Authors: Ham Hanry , Wesley Julian , Hendra Hendra
Published: October 2019

3D reconstruction are used in many fields starts from the object reconstruction such as site, and cultural artifacts in both ground and under the sea levels. The scientist are beneficial for these task in order to learn and keep the environment into 3D data due to the extinction. In this paper explained vision setup that is commonly used such as single camera, stereo camera, Kinect / Structured Light/ Time of Flight camera and fusion approach. The prior works also explained how the 3D reconstruction perform in many fields and using various algorithms.
Autonomous Abnormal Behaviour Detection Using Trajectory Analysis
Authors: Shuaau Muhammed , Thang Ka Fei , Lai Nai Shyan
Published: October 2019

Abnormal behaviour detection has attracted signification amount of attention in the past decade due to increased security concerns around the world. The amount of data from surveillance cameras have exceeded human capacity and there is a greater need for anomaly detection systems for crime monitoring. This paper proposes a solution to this problem in a reception area context by using trajectory extraction through Gaussian Mixture Models and Kalman Filter for data association. Here, trajectory analysis was performed on extracted trajectories to detect four different anomalies such as entering staff area, running, loitering and squatting down. The developed anomaly detection algorithms were tested on videos captured at Asia Pacific University’s reception area. These algorithms were able to achieve a promising detection accuracy of 89% and a false positive rate of 4.52%.