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Bulletin of Electrical Engineering and Informatics
Published by Institute of Advanced Engineering and Science
ISSN : -     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
Bulletin of Electrical Engineering and Informatics ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication, computer engineering, computer science, information technology and informatics from the global world. The journal publishes original papers in the field of electrical (power), electronics, instrumentation & control, telecommunication and computer engineering; computer science; information technology and informatics. Authors must strictly follow the guide for authors. Please read these instructions carefully and follow them strictly. In this way you will help ensure that the review and publication of your paper is as efficient and quick as possible. The editors reserve the right to reject manuscripts that are not in accordance with these instructions.
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Articles 12 Documents
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Search results for , issue "Vol 6, No 2: June 2017" : 12 Documents clear
VTrace-A Tool for Visualizing Traceability Links among Software Artefacts for an Evolving System C J Satish; Anand M
Bulletin of Electrical Engineering and Informatics Vol 6, No 2: June 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (692.316 KB) | DOI: 10.11591/eei.v6i2.652

Abstract

Traceability Management plays a key role in tracing the life of a requirement through all the specifications produced during the development phase of a software project. A lack of traceability information not only hinders the understanding of the system but also will prove to be a bottleneck in the future maintenance of the system. Projects that maintain traceability information during the development stages somehow fail to upgrade their artefacts or maintain traceability among the different versions of the artefacts that are produced during the maintenance phase. As a result the software artefacts lose the trustworthiness and engineers mostly work from the source code for impact analysis. The goal of our research is on understanding the impact of visualizing traceability links on change management tasks for an evolving system. As part of our research we have implemented a Traceability Visualization Tool-VTrace that manages software artefacts and also enables the visualization of traceability links. The results of our controlled experiment show that subjects who used the tool were more accurate and faster on change management tasks than subjects that didn’t use the tool.
Comparison of Tropical Thunderstorm Estimation between Multiple Linear Regression, Dvorak, and ANFIS Wayan Suparta; Wahyu Sasongko Putro
Bulletin of Electrical Engineering and Informatics Vol 6, No 2: June 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (882.822 KB) | DOI: 10.11591/eei.v6i2.648

Abstract

Thunderstorms are dangerous and it has increased due to highly precipitation and cloud cover density in the Mesoscale Convective System area. Climate change is one of the causes to increasing the thunderstorm activity. The present studies aimed to estimate the thunderstorm activity at the Tawau area of Sabah, Malaysia based on the Multiple Linear Regression (MLR), Dvorak technique, and Adaptive Neuro-Fuzzy Inference System (ANFIS). A combination of up to six inputs of meteorological data such as Pressure (P), Temperature (T), Relative Humidity (H), Cloud (C), Precipitable Water Vapor (PWV), and Precipitation (Pr) on a daily basis in 2012 were examined in the training process to find the best configuration system. By using Jacobi algorithm, H and PWV were identified to be correlated well with thunderstorms. Based on the two inputs that have been identified, the Sugeno method was applied to develop a Fuzzy Inference System. The model demonstrated that the thunderstorm activities during intermonsoon are detected higher than the other seasons. This model is comparable to the thunderstorm data that was collected manually with percent error below 50%.

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