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INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN : 20893272     EISSN : -     DOI : -
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 15 Documents
Search results for , issue "Vol 6, No 4: December 2018" : 15 Documents clear
Synthesis of (Polymer blend-MgO) Nanocomposites and Studying Electrical Properties for Piezoelectric Application Majeed Habeeb; Rehab Shather Abdul Hamza
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 6, No 4: December 2018
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1016.408 KB) | DOI: 10.52549/ijeei.v6i4.511

Abstract

Nanocomposites prepared by casting method with different percentages of nano magnesium oxide (0, 1.5, 3, 4.5 and 6) wt%.The structural and electrical properties of (PAA-CMC-MgO) nanocomposites were studied.The experimental results of Scanning electron microscopy shows the surface morphology of the (PAA-CMC-MgO)  nanocomposites where many aggregates or chunks randomly distributed on the top surface, homogeneous and coherent.The D.C electrical conductivity for (PAA-CMC-MgO) nanocomposites increased with increasing of temperature and magnesium oxid nanoparticles concentration, while activation energy decreases with increasing of the  magnesium oxid nanoparticles concentration.The A.C electrical properties show that the dielectric constant and dielectric loss of the nanocomposites decrease with increasing the frequency of applied electrical field and they increase with the increase of  the concentration of the magnesium oxide  nanoparticles. The A.C electrical conductivity increases with increasing the concentration of magnesium oxide nanoparticles and also increases with the increase frequency, as well as almost constant at high frequency.The results of sensor application showed that the electrical resistance of (PAA-CMC-MgO) nanocomposite decreases with increases in pressure.
Big Data in Smart-Cities: Current Research and Challenges Debajyoti Pal; Tuul Triyason; Praisan Padungweang
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 6, No 4: December 2018
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v6i4.543

Abstract

Smart-cities are an emerging paradigm containing heterogeneous network infrastructure, ubiquitous sensing devices, big-data processing and intelligent control systems. Their primary aim is to improve the quality of life of the citizens by providing intelligent services in a wide variety of aspects like transportation, healthcare, entertainment, environment, and energy. In order to provide such services, the role of big-data and its analysis is extremely important as it enables to obtain valuable insights into the large data generated by the smart-cities.  In this article, we investigate the state-of-art research efforts directed towards big-data analytics in a smart-city context. Specifically, first we present a big-data centric taxonomy for the smart-cities to bring forth a generic overview of the importance of big-data paradigm in a smart-city environment. This is followed by the presentation of a top-level snapshot of the commonly used big-data analytical platforms. Due to the heterogeneity of data being collected by the smart-cities, often with conflicting processing requirements, suitable analytical techniques depending upon the data type are also suggested. In addition to this, a generic four-tier big-data framework comprising of the sensing hub, storage hub, processing hub and application hub is also proposed that can be applied in any smart-city context. This is complemented by providing the common big-data applications in a smart-city and presentation of ten selected case studies of smart-cities across the globe. Finally, the open challenges are highlighted in order to give future research directions.
Internet of Things Based Smart Health Monitoring of Industrial Standard Motors Gayathri R.; Shriram K Vasudevan
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 6, No 4: December 2018
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (450.115 KB) | DOI: 10.52549/ijeei.v6i4.492

Abstract

The Industry 4.0 vision provides recommendations how companies can ease the challenges.  In an industrial environment, it is beneficial to  have a predictive approach to make smart industry using IoT. The Predictive approach includes automating the maintenance activities of machines which help to deliver safety, performance, customer experience, capacity, cost efficiency and sustainability of the key business assets.  It helps to improve work force safety which reduces the need to access the infrastructure, develop technologies to enable activities to be remotely controlled from safe areas and automate processes to remove manual tasks and helps to increase infrastructure reliability.  It also improves the precision and accuracy of data collection, introducing data analytics, removing human bias, improving reproducibility.  This will improve information about asset condition, inform inspection and repair schedules based  on asset risks. By implementing predictive and preventive maintenance, one can improve equipment life and avoid any unplanned maintenance activity and thus reducing unscheduled downtime.  We in this work have an unit which could be easily attached to the motor units and this does not demand any wiring to carried out. The sensor monitor signals from the motor, accurately measuring key parameters at regular interval of time, as desired.  And, the data is sent to the cloud, which in our case is adafruit.  From there, the data is analysed and it produces meaningful information. The  server then sends alert message to the users about critical data of machine.   This will help in fixing any technical issue with ease without incurring much delay.
Fast Denoising Filter for MRI using Parallel Approach Oza, Shraddha Dinesh; Joshi, Kalyani Rajiv
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 6, No 4: December 2018
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v6i4.596

Abstract

Real time medical image processing is necessary in the domain of remote medical care, diagnostics and surgery. To provide fast MRI diagnostics especially for neuro imaging, the research work proposes CUDA GPU based fast denoising filter with a parallel approach. Bilateral filter is the most suitable candidate for denoising, as it has unique ability to retain contours of soft tissue structures of the brain. The work proposes improvised memory optimization techniques for the GPU implementation to achieve superior performance in terms of speed up when compared with existing work. For a 64Megapixel brain MR image, shared memory approach gives speed up of 256.5 while texture memory usage with tiling approach stands the next in speedup with 42.16 over its CPU counterpart. The results indicate that in spite of increase in image size, the execution time of the filter does not increase beyond 500msec keeping the performance real time.
Blur Classification Using Segmentation Based Fractal Texture Analysis Shamik Tiwari
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 6, No 4: December 2018
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v6i4.463

Abstract

The objective of vision based gesture recognition is to design a system, which can understand the human actions and convey the acquired information with the help of captured images. An image restoration approach is extremely required whenever image gets blur during acquisition process since blurred images can severely degrade the performance of such systems. Image restoration recovers a true image from a degraded version. It is referred as blind restoration if blur information is unidentified. Blur identification is essential before application of any blind restoration algorithm. This paper presents a blur identification approach which categories a hand gesture image into one of the sharp, motion, defocus and combined blurred categories. Segmentation based fractal texture analysis extraction algorithm is utilized for featuring the neural network based classification system. The simulation results demonstrate the preciseness of proposed method.
QR Code Integrity Verification Based on Modified SHA-1 Algorithm Rogel Ladia Quilala; Ariel M Sison; Ruji P Medina
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 6, No 4: December 2018
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v6i4.494

Abstract

The modified SHA-1 algorithm was applied in the data integrity verification process of certificates with QR code technology. This paper identified the requirements needed in the certificate verification that uses the modified SHA-1. The application was tested using legitimate and fraudulent certificates. Based on the results, the application successfully generated QR codes, printed certificates, and verified certificates with 100% accuracy. During the trial run of the app, four test cases were seen which involves correct names and QR codes, and three other possible test cases of faking certificates such as modification of the name, regeneration of QR codes using valid hash and a fake name, and modification of the QR code. Although these cases exist, the app successfully verified all thirty certificates correctly. Also, it is noticed that during the scanning, the smartphone camera should be in focus to capture the QR code clearly.
Malayalam Handwritten Character Recognition Using AlexNet Based Architecture Ajay James; Manjusha J; Chandran Saravanan
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 6, No 4: December 2018
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v6i4.518

Abstract

This research article proposes a new handwritten Malayalam character recognition model based on AlexNet based architecture. The Malayalam language consists of a variety of characters having similar features, thus, differentiating characters is a challenging task. A lot of handcrafted feature extraction methods have been used for the classification of Malayalam characters. Convolutional Neural Networks (CNN) is one of the popular methods used in image and language recognition. AlexNet based CNN is proposed for feature extraction of basic and compound Malayalam characters. Furthermore, Support Vector Machine (SVM) is used for classification of the Malayalam characters. The 44 primary and 36 compound Malayalam characters are recognised with better accuracy and achieved minimal time consumption using this model. A dataset consisting of about 180,000 characters is used for training and testing purposes. This proposed model produces an efficiency of 98% with the dataset. Further, a dataset for Malayalam characters is developed in this research work and shared on Internet
An Improved Overlapping Clustering Algorithm to Detect Outlier Alvincent Egonia Danganan; Ariel M. Sison; Ruji P. Medina
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 6, No 4: December 2018
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v6i4.499

Abstract

MCOKE algorithm in identifying data objects to multi cluster is known for its simplicity and effectiveness. Its drawback is the use of maxdist as a global threshold in assigning objects to one or more cluster while it is sensitive to outliers. Having outliers in the datasets can significantly affect the effectiveness of maxdist as regards to overlapping clustering. In this paper, the outlier detection is incorporated in MCOKE algorithm so that it can detect and remove outliers that can participate in the calculation of assigning objects to one or more clusters. The improved MCOKE algorithm provides better identification of overlapping clustering results. The performance was evaluated via F1 score performance criterion. Evaluation results revealed that the outlier detection demonstrated higher accuracy rate in identifying abnormal data (outliers) when applied to real datasets.
A Novel Approach for Feature Selection and Classifier Optimization Compressed Medical Retrieval Using Hybrid Cuckoo Search Vamsidhar, Enireddy; Saichandana, B.; Harikiran, J.
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 6, No 4: December 2018
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v6i4.584

Abstract

Nowadays, huge data bases are required to store the Digital medical images so that they can be accessed easily on requirement. To retrieve the diagnostic images, radiologist and physicians are using Content based image retrieval (CBIR). Algorithms extract features like texture, edge, color and shape from an image in CBIR systems and these extracted features from the input and are compared for similarity with the features of images in database. In this paper, Lossless compression is used for storage and effective transmission in inadequate bandwidth. Visually lossless image compression is obtained using the Daubechies wavelet with Huffman coding. Gabor transforms are utilized to extract the shape and texture features from the images. Features are selected with Mutual Information (MI) and the proposed wrapper based Cuckoo Search (CS) technique. Extracted features are fed as input to the proposed partial Recurrent Neural Networks (RNN) for the classification. The network is optimized hybrid Particle Swarm Optimization and Cuckoo Search. It was observed that the classification accuracy acquired is satisfactory
SoC Estimation and Monitoring of Li-ion Cell using Kalman-Filter Algorithm Premkumar Manoharan; Mohankumar R; Karthick K; Sowmya R
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 6, No 4: December 2018
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v6i4.548

Abstract

With the rise in an energy crisis, electric vehicles have become a necessity. An integral part of the electric/hybrid vehicle is batteries. Out of many types, Li-ion batteries are providing features like high power as well as energy density. The features make Li-ion is an excellent choice for multiple applications from electronic appliances to electric vehicles. Li-ion batteries have their limitations while using in electric vehicles, and battery parameter monitoring like temperature, voltage, current, State of Charge (SoC), etc. is very much essential. The monitoring is dependent on actual physical measurements, which are subject to error contributing factors such as measurement noise, errors etc. With the estimation of SOC and State of Health (SoH) of the battery model, the lifetime of the battery will be calculated out, and along these lines sparing significant cost. In this paper, a study on SoH estimation and Li-ion battery SoC is estimated using a Kalman Filter (KF) algorithm estimation and results are presented to validate the Li-ion operating performance

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