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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,199 Documents
 165   166   167   168   169 
Design of Adaptive Filter for Laser Gyro Xiao Jun Yan; Jin Ming Li; Ze Ming Li; Yan Jiao Yang; Cheng Rui Zhai
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 11: November 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i11.pp7816-7823

Abstract

According to the filtering of laser gyro output signal of low precision, slow speed of dynamic response, the research and implementation of a new method of laser gyro filtering process, the scheme using LMS adaptive filtering algorithm, the dither feedback signal as the iterative filter data input, the dither signal, random noise, white noise as the reference signal filter, digital filter and the external control using FPGA, give the filter algorithm and hardware block diagram. Experimental results show that the filtering module with filter with high precision and wide dynamic response range, can meet the requirements of speed and precision of laser gyro demodulation aerospace fields.
Ensuring Data Integrity Scheme Based on Digital Signature and Iris Features in Cloud Salah H. Abbdal; Thair A. Kadhim; Zaid Ameen Abduljabbar; Zaid Alaa Hussien; Ali A. Yassin; Mohammed Abdulridha Hussain; Salam Waley
Indonesian Journal of Electrical Engineering and Computer Science Vol 2, No 2: May 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v2.i2.pp452-460

Abstract

Cloud computing is a novel paradigm that allows users to remotely access their data through web- based tools and applications. Later, the users do not have the ability to monitor or arrange their data. In this case, many security challenges have been raised. One of these challenges is data integrity. Contentiously, the user cannot access his data directly and he could not know whether his data is modified or not. Therefore, the cloud service provider should provide efficient ways for the user to ascertain whether the integrity of his data is protected or compromised. In this paper, we focus on the problem of ensuring the integrity of data stored in the cloud. Additionally, we propose a method which combines biometric and cryptography techniques in a cost-effective manner for data owners to gain trust in the cloud. We present efficient and secure integrity based on the iris feature extraction and digital signature.  Iris recognition has become a new, emergent approach to individual identification in the last decade. It is one of the most accurate identity verification systems. This technique gives the cloud user more confidence in detecting any block that has been changed. Additionally, our proposed scheme employs user’s iris features to secure and integrate data in a manner difficult for any internal or external unauthorized entity to take or compromise it. Iris recognition is an internal organ that is well protected against damage and wear by a highly transparent and sensitive membrane. Extensive security and performance analysis show that our proposed scheme is highly efficient and provably secure.
Singularity Detection of Magnetic Memory Signal of Steel-Cord Conveyor Belt Qiao Tiezhu; Li Xiaolu; Zhang Xueying
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 9: September 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

Metal magnetic memory technology was an important method for detecting the steel-cord conveyor belt early fault, characteristics of magnetic memory signal extraction is critical for judging of the conveyor belt failure. Generally using of magnetic memory signal maximum gradient value can quickly judge the stress concentration zone, but the magnetic memory signal is susceptible to effected by environmental and noise; In view of the weak and non-stationary characteristics of magnetic memory signal, this paper has proposed the singularity detection method based on wavelet transform modulus maximum for metal magnetic memory signal, the method could exactly judged the stress concentration zone of joints and located the fault points of the steel-cord belt, the characteristic gradient of magnetic memory signal and the Lipschitz exponent were extracted. The result of simulation indicated the technology was effectively for judging the stress concentration zone and fault point. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.2866
Optimization Research of Energy Structure in Hebei Province Jinying Li; Zhifen Xu; Chunlian Zhang
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 3: March 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

In the case of excessive energy consumption and energy consumption structure is irrational in Hebei Province, energy structure optimization has become increasingly important. This paper establishes a multi-objective decision model of energy structure optimization through regression analysis by collecting large amounts of data of energy consumption and energy-related investments and pollution control over the years, through a comprehensive and systematic analysis of the model, then using genetic algorithms to solve the model ,the result verifies the validity of the method .Finally,the paper analyzes the result and clarifies the future direction of development of the energy structure in Hebei Province. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.3846
Design of Smart Waste Bin and Prediction Algorithm for Waste Management in Household Area Siti Hajar Yusoff; Ummi Nur Kamilah Abdullah Din; Hasmah Mansor; Nur Shahida Midi; Syasya Azra Zaini
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: November 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i2.pp748-758

Abstract

Maintaining current municipal solid waste management (MSWM) for the next ten years would not be efficient anymore as it has brought many environmental issues such as air pollution. This project has proposed Artificial Neural Network (ANN) based prediction algorithm that can forecast Solid Waste Generation (SWG) based on household size factor. Kulliyyah of Engineering (KOE) in International Islamic University Malaysia (IIUM) has been chosen as the sample size for household size factor. A smart waste bin has been developed that can measure the weight, detect the emptiness level of the waste bin, stores information and have direct communication between waste bin and collector crews. This study uses the information obtained from the smart waste bin for the waste weight while the sample size of KOE has been obtained through KOE’s department. All data will be normalized in the pre-processing stage before proceeding to the prediction using Visual Gene Developer. This project evaluated the performance using R2 value. Two hidden layers with five and ten nodes were used respectively. The result portrayed that the average rate of increment of waste weight is 2.05 percent from week one until week twenty. The limitation to this study is that the amount of smart waste bin should be replicated more so that all data for waste weight is directly collected from the smart waste bin.
Parallel Implementation of Classification Algorithms Based on Cloud Computing Environment Lijuan Zhou; Hui Wang; Wenbo Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 5: September 2012
Publisher : Institute of Advanced Engineering and Science

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Abstract

As an important task of data mining, Classification has been received considerable attention in many applications, such as information retrieval, web searching, etc. The enlarging volumes of information emerging by the progress of technology and the growing individual needs of data mining, makes classifying of very large scale of data a challenging task. In order to deal with the problem, many researchers try to design efficient parallel classification algorithms. This paper introduces the classification algorithms and cloud computing briefly, based on it analyses the bad points of the present parallel classification algorithms, then addresses a new model of parallel classifying algorithms. And it mainly introduces a parallel Naïve Bayes classification algorithm based on MapReduce, which is a simple yet powerful parallel programming technique. The experimental results demonstrate that the proposed algorithm improves the original algorithm performance, and it can process large datasets efficiently on commodity hardware. DOI: http://dx.doi.org/10.11591/telkomnika.v10i5.1353
Power Loss Minimization in Radial Distribution Networks Using Reconfiguration and DGs Sarfaraz Nawaz; Ajay Kumar Bansal; Mahaveer Prasad Sharma
Indonesian Journal of Electrical Engineering and Computer Science Vol 7, No 3: September 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v7.i3.pp583-592

Abstract

A novel approach is proposed in this paper to achieve the objective of real power loss minimization and voltage profile enhancement. Network reconfiguration and allocation of various DG units are used to meet the objective. Selective particle swarm Optimization (SPSO) and novel analytical techniques are used to solve the problem of network reconfiguration and allocation of DG units simultaneously.  A new constant, Power Voltage Sensitivity Constant (PVSC), has been proposed to solve the allocation problem. The formulated mathematical expression (PVSC) determines site and size of DG units.  The level of DG penetration is considered in a range of 0–50% of total system load. A novel index is also proposed which incorporates level of DG penetration and % reduction in real power losses. Standard 69 bus system is used to validate the results obtained by proposed hybrid approaches. To show the efficacy and strength of the proposed hybrid approach, it has been compared with various techniques
Embedded adaptive mutation evolutionary programming for distributed generation management Muhammad Fathi Mohd Zulkefli; Ismail Musirin; Shahrizal Jelani; Mohd Helmi Mansor; Naeem M. S. Honnoon
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 1: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i1.pp364-370

Abstract

Distribution generation (DG) is a widely used term to describe additional supply to a power system network. Normally, DG is installed in distribution network because of its small capacity of power. Number of DGs connected to distribution system has been increasing rapidly as the world heading to increase their dependency on renewable energy sources. In order to handle this high penetration of DGs into distribution network, it is crucial to place the DGs at optimal location with optimal size of output. This paper presents the implementation of Embedded Adaptive Mutation Evolutionary Programming technique to find optimal location and sizing of DGs in distribution network with the objective of minimizing real power loss. 69-Bus distribution system is used as the test system for this implementation. From the presented case studies, it is found that the proposed embedded optimization technique successfully determined the optimal location and size of DG units to be installed in the distribution network so that the real power loss is reduced.
The prediction of Granulating Effect Based on BP Neural Network Fang Li; Kaigui Wu; Guanyin Zhao
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 6: June 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

During the granulation process of Iron ore sinter mixture, there are many factors affect the granulating effect, such as chemical composition, size distribution, surface feature of particle, and so on. Some researchers use traditional fitting calculation methods like least square method and regression analysis method to predict granulation effects, which exists big error. In order to predict it better, we build improved BP (Back propagation) neural network model to carry out data analysis and processing, and then obtain better effect than traditional fitting calculation methods. DOI : http://dx.doi.org/10.11591/telkomnika.v12i6.5481
The Establishment of Field Intensity Model of Wireless Telemetry Signal in Man-made Forests Yun-Jie Xu; Wei Zhang
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 3: March 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

On the basis of the configuration characteristics of man-made forests, the spread model of wireless sensor signal in man-made forests has been simplified according to electromagnetic wave theory and Fermat principle. The man-made forests is decomposed into independent medium with four layers, the spread model is simplified into free space model, attenuation screen model and absorption screen model. The field intensity calculation formulas of UHF frequency range attenuation screen and absorption screen has been discussed, which provides basis for optimization layout of sensor and offers theoretical basis of other radio frequency communication research in man-made forests. DOI: http://dx.doi.org/10.11591/telkomnika.v11i3.2186

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