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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 63 Documents
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Search results for , issue "Vol 10, No 2: April 2021" : 63 Documents clear
A wearable device for machine learning based elderly's activity tracking and indoor location system Nour Eddin Tabbakha; Chee Pun Ooi; Wooi Haw Tan; Yi-Fei Tan
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2737

Abstract

The number of older people is increasing in many countries. By 2030, it is estimated that 15% of the overall population will be comprised of people aged 65 and above. Hence, the monitoring and tracking of elder activities to ensure they live an active life has become a major research topic in recent years. In this work, an elderly sub-activity tracking system is developed to detect the sub-activity of the elderly based on their physical activities and indoor location. The physical activities tracking system and indoor location system is combined in this project to enhance the context of the elderly activities (i.e. sub-activities as defined in this project). An indoor location system is developed by using Bluetooth Low Energy (BLE) beacon and BLE scanners to measure the Received Signal Strength Indicator (RSSI) signal to detect the location of the elderly. The activity tracking is carried out via a waist wearable device worn by the elderly. Random forest and Support Vector Machine (SVM) are used as machine learning classifiers to predict the activity and indoor location with an accuracy of 95.03% and 86.58%, respectively. The data from activity tracking and indoor location sub-systems will then be combined to derive the sub-activity and push to an online Internet of Things (IoT) platform for remote monitoring and notification.
Threshold benchmarking for feature ranking techniques Ruchika Malhotra; Anjali Sharma
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2752

Abstract

In prediction modeling, the choice of features chosen from the original feature set is crucial for accuracy and model interpretability. Feature ranking techniques rank the features by its importance but there is no consensus on the number of features to be cut-off. Thus, it becomes important to identify a threshold value or range, so as to remove the redundant features. In this work, an empirical study is conducted for identification of the threshold benchmark for feature ranking algorithms. Experiments are conducted on Apache Click dataset with six popularly used ranker techniques and six machine learning techniques, to deduce a relationship between the total number of input features (N) to the threshold range. The area under the curve analysis shows that ≃ 33-50% of the features are necessary and sufficient to yield a reasonable performance measure, with a variance of 2%, in defect prediction models. Further, we also find that the log2(N) as the ranker threshold value represents the lower limit of the range.
Studying strictly positive secure capacity in cognitive radio-based non-orthogonal multiple access Chi-Bao Le; Dinh-Thuan Do
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2117

Abstract

This paper studies a downlink security-aware secure outage performance in the secondary network of cognitive radio-assisted non-orthogonal multiple access network (CR-NOMA). The multiple relay is employed to assist transmission from the secondary source to destinations. The security-aware performance is subject to constraints in fixed power allocation factor assigned to each secondary user. The security-aware secure performance is based on channel state information (CSI) at the physical layer in which an eavesdropper intends to steal information. According to the considered system, exact expressions of Strictly positive secure capacity (SPSC) are proved to analyze system in terms of secure performance. Finally, the secondary user secure problem is evaluated via Monte-Carlo simulation method. The main results indicate that the secure performance of proposed system can be improved significantly.
Real-time monitoring of clinic risks using an integrated RFID-FA scheme Nisreen A. Hussein; Mohammed M. Fayyadh
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2365

Abstract

Patient safety is a global public health concern because of increases in the number of mistreatments due to the improper identification of patients or the improper administration of drugs. Risk in clinic management refers to the systematic process used to specify, control, and analyze organizational risks. The present article developed a new method to detect different objects automatically in real-time by monitoring and controlling the hospital workflow using radio frequency identification (RFID). The system methodology starts with identifying the functional area by detecting the room optical characters. Then clustering and matching the symmetrical functional area using histogram matching technique. For the monitoring process, the radio frequency network planning RNP has been used. Density-based scan algorithm (DBSCAN) was used for clustering and extracting the area, then all gathered data transferred to the firefly algorithm to track drug distribution and specify doctor and nurse locations. The simulation results observe real-time tracking and identification of people and drugs based on hospital zone designs. The results present 87% tag real-time coverage for managing and monitoring human inside the hospital. The effectiveness of this system shows that it was useful in monitoring clinic operations and effective for a hospital network solution.
Overview about GIS multi-criteria spatial analysis for micro hydropower plant site suitability in South Ogan Komering Ulu District, South Sumatera, Indonesia Wawan Hendriawan Nur; Yuliana Yuliana; Yuliana Susilowati; Yugo Kumoro; Yunarto Yunarto
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2770

Abstract

Morphology in South OKU District is the potential of a micro hydropower plant (MHPP) as an alternative power source. This potential has not been fully utilized, although many un-electrified villages are in several remote areas. Identification planning for MHPP is one of the most critical planning tasks and requires excellent multi-criteria spatial analysis. GIS and multi-criteria analysis have played an essential role in analyzing suitable locations for MHPP development. GIS and multi-criteria spatial analysis consist of detailed investigations of ongoing sites and suitability for specific planning. This research aims to overview GIS multi-criteria spatial analysis for MHPP site suitability based on electricity South OKU demands. The most critical data and criteria to decide the best site suitability are un-electrified villages, rivers, land use, slope, landslide vulnerability, and elevation. All of the data were generated into the raster data format. Quantitative modeling used AHP as a multi-criteria analysis method, and a weighted score is determined by considering the comparison of each criterion. Finally, the criterion layer was calculated by open-source QGIS to create a site suitability map. The field study verified the resulting map, and there is a match between the preferred locations and the field survey. The research results preferred Sungai Are, Sindang Danau, and Kisam Tinggi Sub-district as the best suitability for MHPP development.
A new T-circuit model of wind turbine generator for power system steady state studies Rudy Gianto; Kho Hie Khwee
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2306

Abstract

Modeling of wind power plant (WPP) is a crucial issue in power system studies. In this paper, a new model of WPP for steady state (i.e. load flow) studies is proposed. Similar to the previous T-circuit based models, it is also developed based on equivalent T-circuit of the WPP induction generator. However, unlike in the previous models, the mathematical formulation of the new model is shorter and less complicated. Moreover, the derivation of the model in the present work is also much simpler. Only minimal mathematical operations are required in the process. Furthermore, the rotor voltage value of the WPP induction generator is readily available as an output of the proposed new model. This rotor voltage value can be used as a basis to calculate the induction generator slip. Validity of the new method is tested on a representative 9-bus electrical power system installed with WPP. Comparative studies between the proposed method (new model) and other method (previous model) are also presented
Locating and sizing of capacitor banks and multiple DGs in distribution system to improve reliability indexes and reduce loss using ABC algorithm Mehrdad Ahmadi Kamarposhti; Seyed Mohsen Mousavi Khormandichali; Ahmed Amin Ahmed Solyman
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2641

Abstract

DG sources have been introduced as one of the most widely used and effective methods among various methods providing losses reduction in power systems. In this paper, the artificial bee colony algorithm has been employed with the aim of determining location and capacity of distributed generations (DGs) and capacitor banks (CBs) in distribution systems. The proposed objective function includes power losses and ENS reliability index, which is used by deploying weight coefficients as objective function in the algorithms. Accordingly, the standard 37-bus networks have been used for studies. The simulation results demonstrate that the artificial bee colony algorithm is more effective in all sections and has higher capability in reducing losses and improving reliability as well.
Optimization of triple-junction hydrogenated silicon solar cell nc-Si:H/a-Si:H/a-SiGe:H using step graded Si1‑xGex layer Nji Raden Poespawati; Rizqy Pratama Rahman; Junivan Sulistianto; Retno Wigajatri Purnamaningsih; Tomy Abuzairi
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2765

Abstract

This paper shows the attempt to increase the performance of triple-junction hydrogenated silicon solar cells with structure nc-Si:H/a-Si:H/a-SiGe:H. The wxAMPS software was used to simulate and optimize the design. In an attempt to increase the performance, an a-SiC:H layer on the p-layer was replaced with an a-Si:H layer and an a-SiGe layer was replaced with a step graded Si1-xGex layer. Then, to achieve the best performing device, we optimized the concentration of germanium and thickness of the step graded Si1-xGex layer. The result shows that the optimum concentration of germanium in the p-i upper layer and i-n lower layer are 0.86 and 0.90, respectively and the optimum thicknesses are 10 nm and 230 nm, respectively. The optimized device performed with an efficiency of 19.08%, adding 3 more percent of efficiency from the original design. Moreover, there is a significant possibility of increasing the efficiency of a triple-junction solar cell by modifying it into a step graded Si1-xGex layer.
A GMM supervector approach for spoken Indian language identification for mismatch utterance length Aarti Bakshi; Sunil Kumar Kopparapu
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2861

Abstract

Gaussian mixture model-universal background model (GMM UBM) supervectors are used to identify spoken Indian languages. The supervectors are calculated from short-time MFCC, its first and sec derivatives. The UBM builds a generalized Indian language model, and mean adaptation transforms it to a duration normalized language-specific GMM. Multi-class support vector machine and artificial neural network classifiers are used to identify language labels from the supervectors. Experimental evaluations are performed using 30 sec speech utterances from nine Indian languages comprised five Indo-Aryan and four Dravidian languages, extracted from all India radio broadcast news data-set. Eight smaller duration data-sets were manually derived to study the effect of training and test duration mismatch. In mismatch conditions, identification accuracy decreases with a decrease in test and train utterance duration. Investigations showed that the 32-mixture model with ANN classifier has optimal performance.
Scaled conjugate gradient ANN for industrial sensors calibration Karam Mazin Zeki Othman; Abdulkreem M Salih
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2738

Abstract

In this paper, artificial neural network is used to calibrate sensors that are commonly used in industry. Usually, such sensors have nonlinear input output characteristic that makes their calibration process rather inaccurate and unsatisfied. Artificial neural network is utilized in an inverse model learning mode to precisely calibrate such sensors. The scaled conjugate gradient (SCG) algorithm is used in the learning process. Three types of industrial sensors which are gas concentration sensor, force sensors and humidity sensors are considered in this work. It is found that the proposed calibration technique gives fast, robust and satisfactory results.

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