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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 2,901 Documents
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Development of regional load management system based on rural, semi urban and urban loads-a critical analysis Ayandeep Ganguly; Arindam Kumar Sil
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3460

Abstract

The sharp rise in population during the last half century has created immense pressure on the resources required for generation of energy essential to lead a comfortable and healthy lifestyle. The drive towards 100% electrification in developing countries like India has also contributed to this increase in demand. Till recently, fossil fuel was use to supply the bulk of this power. Now, the world is moving more and more towards renewable energy. This paper presents a model where several regions are combined together based on the demand profile of the regions segregated as urban, semi urban and rural along with the flexibility to schedule loads on the basis of availability of renewable energy sources within the area of the regions. The main focus is on detailed neural-networking based load forecasting and developing a load management system to manage load based on availability of distributed generation capacity and available tariff system. A solution is proposed in this paper based on a new approach to answer load management on the basis of region, population demographics and per capita energy consumption. A considerable amount of improvement to manage demand is intended to be attained and has been demonstrated in this research work.
Early faults diagnosis and severity assessment of rolling element bearings on wireless signal transfer Ghulam Mustafa; Shahab Khushnood
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3548

Abstract

Machine condition monitoring in remote locations and harsh environment where network infrastructure is not feasible, or hardwired network connectivity is not possible, wireless communications provides an alternative which also offer installation cost savings, improve reliability and quicker deployment. This paper describes the implementation of wireless sensor network (WSN) for early fault diagnosis of rolling element bearings based on signal autocorrelation technique. A low-power 2.4 GHz wireless HART transceiver, a low-cost wireless vibration transmitter, 26.76 mv/g accelerometers and a 1420 wireless gateway with AMS software was implemented. The research describes the methodology of acquiring peak values data in high frequency region. The noise was averaged out by applying four-time averaging and natural frequencies or fault frequencies of bearing elements was captured. The experimental results show that the signal autocorrelation algorithm can successfully diagnose the roller bearing faults at early stage on wireless signal transfer. As the raw data was processed before wireless transmission on analyzing unit and spectrum was transferred in JPG format on display unit, minimum power consumption has been noted. The technique provided a better alternative of wired system for real time condition monitoring of roller bearings in rotating equipment installed in remote area.
Universal cyber physical system, a prototype for predictive maintenance Keh-Kim Kee; Simon Lau Boung Yew; Yun Seng Lim; Yip Ping Ting; Ramli Rashidi
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3216

Abstract

Industrial 4.0 technology of cyber-physical system enables real-time monitoring, sensing and actuating of physical machinery for predictive maintenance that replaces the conventional labor-intensive approach. This paper presents the design and development of a universal, cost-effective and internet of thing (IoT)-based proof-of-concept prototype universal cyber-physical system (UniCPS) with a cloud platform with an open and modular-based design of three-tier system architecture. The prototype demonstrates promising precision and accuracy for predictive maintenance on a pilot use case with MAPE of 3.77%, and average RMSE of 0.50. Besides, real-time visualization and detection of anomaly were also demonstrated with a cloud-based solution. The maintenance alert sent out by the actuator serves to notify the authorized personnel immediately for corrective action. As an extension to this work, a wireless sensor network can be incorporated in future work to acquire various data from diverse locations to overcome the limitations of sensor data.
Cellular network bandwidth improvement using subscribers’ classification and Wi-Fi offloading Adewale Adeyinka Ajao; Ben Obaje Abraham; Etinosa Noma Osaghae; Okesola Olatunji; Edikan Ekong; Abdulkareem Ademola
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.2575

Abstract

Cellular networks are highly prone to congestion especially at peak traffic periods. This is compounded by the fact that the blocking probability increases. In this study, a machine learning based subscriber classification along with an adaptive Wi-Fi offloading scheme is proposed to improve the throughput and lower the blocking probability of the network. The proposed subscriber classification was implemented using a back propagation based artificial neural network. The result of the subscriber classification was used to develop an adaptive Wi-Fi offloading algorithm based on bandwidth utilization and system throughput. The developed neural network models are shown to be effective, with 94.6% in one experiment, in classifying a user into user classes or levels based on previous data usage. The levenberg–marquardt (LM) algorithm gave the highest accuracy in categorizing the four classes. A relatively large sample size was used for the neural network training cycle and the resulting neural network was then made to use many neurons in its hidden layer. The implementation of the proposed subscriber classification and adaptive Wi-Fi offloading scheme led to a 20% drop in blocking probability and a 50.53% increase in the system throughput.
Enhanced constrained local models for gender prediction Ayah Alsarayreh; Fatma Susilawati Mohamad
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.2948

Abstract

Face land-marking, defined as the detection and positioning of distinctive characteristics, is a crucial goal shared by various organizations, ranging from biometric recognition to mental state comprehension. Despite its apparent simplicity, this problem has been extensively investigated because of inherent face variability and a variety of confusing variables such as posture, voice, illumination, and occlusions. In this paper, an integrated mount model is created to increase the power of constrained local models, and a ground-breaking result for feature detection is obtained using this model. Furthermore, four classifiers have been used in the level of gender prediction. The results of the experiment showed that the proposed model performs admirably.
Feature selection for urban land cover classification employing genetic algorithm Ali Alzahrani; Md. Al-Amin Bhuiyan
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3399

Abstract

Feature selection has attained substantial research interest in image processing, computer vision, pattern recognition and so on due to tremendous dimensional reduction in image analysis. This research addresses a genetic algorithm based feature selection strategy for urban land cover classification. The principal purpose of this research is to monitor the land cover alterations in satellite imagery for urban planning. The method is based on object based classification by detecting the object area of a given image with the knowledge of visual information of the object from remote sensing images. The classification system is organized through a multilayer perceptron with genetic algorithm (MLPGA). Experimental results explicitly indicate that this MLPGA based hybrid feature selection procedure performs classification with sensitivity 94%, specificity 90% and precision 89%, respectively. This MLPGA centered hybrid feature selection scheme attains better performance than the counterpart methods in terms of classification accuracy.
Cloud resources modelling using smart cloud management Haitham Salman Chyad; Raniah Ali Mustafa; Dena Nadir George
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3286

Abstract

Cloud computing complexity is growing rapidly with the advancements that it is witnessing. It has created a requirement to simplify the process of configuring cloud and re-configuring it when required, it also involves tasks like auto scaling of infrastructure, elastic computing and maintaining the health of the servers. The proposed method introduces a smart cloud management using knowledge base, which models the resources of cloud; it handles service level agreement and its evaluations. The proposed knowledge base supports representational state transfer (REST/RESTful) services to store and manipulate different cloud aspects like type of application, business configuration, and metrics value and its type; it also implements the strategy for efficient resource management for smart clouds. The proposed architecture consists of smart cloud engine (which provides autonomous services, which help to exploit cloud resources for service optimization and to perform service automation), knowledge base (KB) (provide a cloud ontology which will help in the management of resources and provides intelligence to the smart cloud), server and cloud enrolment, designated monitoring tool and moderator. The resulted module is easy to integrate with any of the existing cloud management tool or orchestrator. As It is developed using REST protocol and extensible markup language (XML) language it is also easy to integrate with existing monitoring tool or application programming interface (APIs).
Ide-cabe: chili varieties identification and classification system based leaf Wiwin Suwarningsih; Purnomo Husnul Khotimah; Andri Fachrur Rozie; Andria Arisal; Dianadewi Riswantini; Ekasari Nugraheni; Devi Munandar; Rinda Kirana
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3276

Abstract

Identifying good quality chili varieties can be done by observing their leaves. It is required for seed testing and certification processes. Currently, a manual leaf identification method is used in which human experts inspect a wide range of leaves every one to two months. An automatic method could increase the identification process. Deep learning has proven to be a prominent method for image classification. We investigate the performance of deep CNN models, as: AlexNet, VGG16, Inception-v3 and DenseNet-121; to classify chili variety. In this paper, we took images of leaves aged 10 days. A preprocessing strategy was taken to enrich the dataset and to boost the classification performance and to assess the proposed models’ quality. From this study, we acquired 12 classes of chili leaves dataset. We acquired performance accuracy ranging from 70.18% to 78.37%. Further, the classification results by DenseNet-121 obtained the highest accuracy of 78.37% and recall of 74.83%. The classifiers investigated in this study perform well despite the relatively small number of our dataset. These results encourage the application of such an approach in practice.
Performance analysis of peak signal-to-noise ratio and multipath source routing using different denoising method Kannadhasan Suriyan; Nagarajan Ramaingam; Sudarmani Rajagopal; Jeevitha Sakkarai; Balakumar Asokan; Manjunathan Alagarsamy
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3332

Abstract

The problem of denoising iris pictures for iris identification systems will be discussed, as well as a novel solution based on wavelet and median filters. Different salt and pepper extraction algorithms, as well as Gaussian and speckle noises, were used. Because diverse sounds decrease picture quality during image collection, noise reduction is even more important. To reduce sounds like salt and pepper, Gaussian, and speckle, filtering (median, wiener, bilateral, and Gaussian) and wavelet transform are utilised. Provide better results as compared to other ways. A study of several efficiency indicators such as peak signal-to-noise ratio (PSNR) and mean squared error will be used to demonstrate the superiority of the proposed technique (MSE).
A java servlet based transaction broker for internet of things edge device communications Zainatul Yushaniza Mohamed Yusoff; Mohamad Khairi Ishak; Lukman AB Rahim
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3455

Abstract

Internet of things (IoT) technology is growing exponentially in almost every sphere of life. IoT offers several innovation capabilities and features, but they are also prone to security vulnerabilities and risks. These vulnerabilities must be studied to protect these technologies from being exploited by others. Cryptography techniques and approaches are commonly used to address and deal with security vulnerabilities. In general, the message queuing telemetry transport (MQTT) is an application layer protocol vulnerable to various known and unknown security issues. One possible solution is to introduce an encryption algorithm into the MQTT communication protocol for secure transmission. This study aims to solve the security problem of IoT traffic by using a secure and lightweight communication proxy. The strategy behind this communication broker acts as a network gateway providing secure transaction keys to all IoT nodes in the network. This task uses a java servlet and elliptic curve cryptography (ECC) algorithm to generate identity encryption keys in a component-based web transaction infrastructure. This approach encrypts the data before it is sent via the MQTT protocol to secure the communication channel and raise the security device and network transactions. 

Page 133 of 291 | Total Record : 2901

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