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EMITTER International Journal of Engineering Technology
Published by Politeknik Elektronika Negeri Surabaya
ISSN : 2355391x     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
EMITTER International Journal of Engineering Technology is a BI-ANNUAL journal published by Politeknik Elektronika Negeri Surabaya (PENS). It aims to encourage initiatives, to share new ideas, and to publish high-quality articles in the field of engineering technology and available to everybody at no cost. It stimulates researchers to explore their ideas and enhance their innovations in the scientific publication on engineering technology. EMITTER International Journal of Engineering Technology primarily focuses on analyzing, applying, implementing and improving existing and emerging technologies and is aimed to the application of engineering principles and the implementation of technological advances for the benefit of humanity.
Arjuna Subject : -
Articles 14 Documents
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Search results for , issue "Vol 6 No 2 (2018)" : 14 Documents clear
Comparison of Adaptive Ant Colony Optimization for Image Edge Detection of Leaves Bone Structure Febri Liantoni; Rifki Indra Perwira; Daniel Silli Bataona
EMITTER International Journal of Engineering Technology Vol 6 No 2 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (455.566 KB) | DOI: 10.24003/emitter.v6i2.306

Abstract

Leaf bone structure has a characteristic that can be used as a reference in digital image processing. One form of digital image processing is image edge detection. Edge detection is the process of extracting edge information from an image. In this research, Adaptive Ant Colony Optimization algorithm is proposed for edge image detection of leaf bone structure. The Adaptive Ant Colony Optimization method is a modification of Ant Colony Optimization, in which the initial an ant dissemination process is no longer random, but it is done by a pixel placement process that allows for an edge based on the value of the image gradient. As a comparison also performed edge detection using Robert and Sobel method. Based on the experiments performed, Adaptive Ant Colony Optimization algorithm is capable of producing more detailed image edge detection and has thicker borders than others. Keywords: edge detection, ant colony optimization, robert, sobel
Assembly Sequence Planning by Probabilistic Tree Transformation Takeshi Murayama; Yuichi Mine; Hiroshi Fujinaka; Toru Eguchi
EMITTER International Journal of Engineering Technology Vol 6 No 2 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (269.235 KB) | DOI: 10.24003/emitter.v6i2.308

Abstract

Various types of computer systems including CAD/CAM systems have been introduced in machine industry. Some of the systems can handle assembly sequence planning, however it requires long time for planning. This paper proposes a method of generating assembly sequences efficiently. This method extracts some parts and/or subassemblies whose possibilities of being removed from a product are strong, and tests whether they can be removed without any geometric interference. By performing these operations repeatedly, the method generates a disassembly sequence of the product, and obtains an assembly sequence by reversing it. The extraction of some parts and/or subassemblies is performed, based on probabilistic tree transformation. The authors present a calculation example by using a software tool integrated with a CAD system.
Simulation design of an Intelligent system for Automotive transmission Gearbox Based on FPGA Azzad Bader SAEED
EMITTER International Journal of Engineering Technology Vol 6 No 2 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (930.128 KB) | DOI: 10.24003/emitter.v6i2.310

Abstract

In this paper, an artificial  intelligent system has been designed, realized, and downloaded into  FPGA (Field Programmable Gate Array), which is used to control five speed ratio steps ( 1,2,3,4,5) of an electrically controlled type of  automotive transmission gearbox of a vehicle, the first speed ratio step (1) is characterized by the  highest torque, a lowest velocity, while, the  fifth step is characterized by the lowest torque, and highest velocity.The Back-propagation neural network has been used as the intelligent system for the proposed system. The proposed neural network is composed from   eight neurons in the input layer, five neurons in the hidden layer, and five neurons in the output layer. For real downloading into the FPGA, Satlins and Satlin linear activation function has been used for the hidden and output layers respectively. The training function Trainlm ( Levenberg-Marqurdt training) has been used as a learning method for the proposed neural network, which it has a powerful algorithm. The proposed simulation system has been designed and downloaded into the FPGA using MATLAB and ISE Design Suit software packages.
Power Generation Forecasting of Dual-Axis Solar Tracked PV System Based on Averaging and Simple Weighting Ensemble Neural Networks Budiman Putra Asma'ur Rohman; Catur Hilman; Erik Tridianto; Teguh Hady Ariwibowo
EMITTER International Journal of Engineering Technology Vol 6 No 2 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (12.589 KB) | DOI: 10.24003/emitter.v6i2.341

Abstract

Solar power is a renewable energy interest many researchers around the world to be explored for human life beneficial especially for electric power generation. Photovoltaic (PV) is one of technology developed massively to exploit the solar power for this purpose. However, its performance is very sensitive to environmental condition such as solar irradiance, weather, and climatic behavior. Thus, the hybrid power generation systems are developed to solve this output uncertainty problem. To support this such hybrid system, this paper proposes an ensemble neural network based forecaster of the power output of PV systems which will lead an efficient power management. The object of this research is the PV systems equipped with two axes automated solar tracking with peak power 10Wp. The proposed ensemble forecaster model employs four multi-layer perceptron neural networks with two hidden layers as base forecasters while the input number of historical data is varied in order to exploit the forecaster diversity. The final prediction is calculated both by conventional averaging and simple weighting optimized by the least square fitting technique. According to the research results, the both proposed approaches provide low error rate. Moreover, in term of comparison, the ensemble model with averaging combining technique gives the highest accuracy comparing to the other ensemble and conventional neural network structures.

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