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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 64 Documents
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Search results for , issue "Vol 27, No 2: August 2022" : 64 Documents clear
High-efficiency green and red phosphors enable a broader hue-gamut light-emitting diode backlight for brighter displays Dieu An Nguyen Thi; Phan Xuan Le
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp741-747

Abstract

In this article, we suggest combining a blue InGaN chip with strait-band green (β-sialon:Eu2+), red (K2SiF6:Mn4+) phosphors to create WLED devices with a wide hue range and high effectiveness that may be utilized in LCD backlighting. The highest radiation wavelength of a gas-pressure sintedβ-sialon:Eu2+ is 535 nm, the full width at half maximum (FWHM) is 54 nm, and the outside quantum performance is 54.0% lower than the 450 nm stimulation. We created K2SiF6:Mn4+ in two steps. The phosphor possesses a sharp line radiation spectrum accompanied by the most intense maximum point under 631 nm, an FWHM reaching roughly 3 nm, as well as an exterior quantum effectiveness of 54.5%. When computed at 120 mA, the manufactured three-range wLEDs had an illuminating performance of 91–96 lm/W and a large color temperature of 11,184–13,769 K (i.e., 7,828–8,611 K in LCD screens). The hue range represented by the CIE 1931 and CIE 1976 hue gaps is 85.5-85.9% and 94.3-96.2% of the NTSC requirement, respectively.  The optic characteristics outperform those of phosphor-transformed wLED backlights utilizing broad-range green or red phosphors, indicating the two strait-range phosphors studied are the best luminous substances for producing brighter and livelier screens.
Blockchain adoption barriers in Moroccan sustainable supply chain: a proposed approach Abdesadik Bendarag; Omar Boutkhoum; Driss Abada; Mohamed Hanine
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp892-899

Abstract

Currently, the emerging countries like Morocco seeks to benefit from the potential of blockchain technology to meet its various growing demands, especially in sustainable supply chain management (SSCM). This explains the need for more effort to understand blockchain implementation and identify the barriers influencing the blockchain adoption decision in SSCM, especially, from Moroccan industry and service sectors perspective. In this context, this research paper proposes a group decision-making approach to identify the barriers from a comprehensive literature search, then evaluate them based on intuitionistic fuzzy analytic hierarchy process (IFAHP). Due to the varied importance of the selected barriers, IFAHP is utilized to allocate priority weights for each barrier according to its importance level. The evaluation results reveal that “Government policy and support” and “Challenges in integrating sustainable practices and blockchain technology through sustainable supply chain management (SCM)” are the best ranked barriers that impact the implementation of blockchain technology in Moroccan context. The main objective is to inquire the barriers preventing the blockchain implementation, and assist industry decision-makers in developing supple short- and long-term decision-making strategies for better sustainable supply chain management.
Breast cancer recognition based on performance evaluation of machine learning algorithms Chiman Haydar Salih; Abbas M. Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp980-989

Abstract

Breast cancer is the one common cause of death in both developed worlds and the most death-causing disease diagnosed among women. Early recognition of this condition can help to minimize death rates. The breast problem statement, in brief, is not reliable for accuracy recognition. They have a high degree of classification accuracy as well as diagnostic capabilities. The most common classifications are normal, benign cancer, and malignant cancer. Machine learning (ML) techniques are now widely used in the classification of breast cancer. In this paper, some machine learning technics have been investigated to diagnose breast cancer (BC) on magnetic resonance imaging (MRI) images using multi-step processes. The first step has been to take the MRI image as an input image and have been pre-processing an image, then use feature extraction by using (scale-invariant feature transform (SIFT), histogram of oriented gradient (HOG), local binary patterns (LBP), bag of words (BoW), and edge-oriented histogram (EOH)). Next step we implement the classifying algorithms (KNN, decision tree (DT), naïve Bayes, ANN, SVM, RF, AdaBoost), have been used to detect and classify the normal or breast cancer region for this purpose datasets like ACRIN-Contralateral-Breast-MRI, In and breast cancer MRI dataset) has been collected our breast cancer MRI images from Erbil and Sulaymaniyah hospital the results was 91.9%, the result of ACRIN was 97% and the results Breast Cancer was 92.3%.
Early disease prediction algorithm for hypertension-based diseases using data aware algorithms Yasmeen Shaikh; Vasudev Parvati; Sangappa Ramachandra Biradar
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp1100-1108

Abstract

This paper implements a data aware early prediction of hypertension-based diseases. Automated data preprocessing method that adopts for both balanced and unbalanced data is the data aware method included in the disease classification algorithm. Proposed data aware data preprocessing method is evaluated on the ensemble learning based classification algorithm for early disease prediction. Data aware preprocessing method adopts isolation forest algorithm for outlier detection as part of the automation. Automated sampling method of applying the sampling corresponding to either balanced or unbalanced data is adopted. Performance evaluation of the proposed data aware algorithm using isolation forest algorithm for anomaly detection is experimented. Python based implementation of the proposed data aware classification algorithm inferred a better area under the curve (AUC) receiver operating characteristics (ROC) curve for isolation forest implementation in data preprocessing automation thus developed. While the individual classifiers multilayer perceptron classifier approached till 0.918 (AUC) in the ROC-AUC curve. The ensemble learning algorithm that included multilayer perceptron classifier, logistic regression classifier, support vector classifier and decision tree algorithm with the isolation forest-based anomaly detection algorithm performed better than the individual machine learning algorithm with 0.922 (AUC) in the ROC-AUC curve.
The luminescence efficiency of green phosphor Ca7(PO4)2(SiO4)2:Eu2+ for white light-emitting diode Van Liem Bui; Phan Xuan Le
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp773-779

Abstract

This study examines the green-emission phosphor composition of Eu2+ doped Ca7(PO4)2(SiO4)2 to serve the goal of efficiency enhancement for the white light emitting diode (LED). The process of preparation and photoluminescent investigation of proposed phosphor composition was monitored under near UV excitation wavelength of the LED die. The sample phase was determined using XRD. To explore Ca7(PO4)2(SiO4)2:Eu2+ capabilities, the diffuse reflectance and photoluminescence spectral figures were employed. The ultraviolet absorption of Ca7(PO4)2(SiO4)2:Eu2+ranged from 240 to 440 nm, with a wide band of green emission peaking at 522 nm. Besides the concentration quenching mechanism, we also focus on essential characteristics for white-light-emitting diode(WLED) production like temperature-dependent lumen output and chromaticity coordinates.
In-depth analysis of dynamic degree load balancing technique in public cloud for heterogeneous cloudlets Aparna Shashikant Joshi; Shyamala Devi Munisamy
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp1119-1126

Abstract

Load balancing is one of the challenges of the distributed computing worldview. With the enormous development in clients and their interest for different administrations on the distributed computing stage, compelling or productive asset usage in the cloud climate has turned into an urgent concern. Load balancing is critical to keeping cloud computing running smoothly. This study examines the research using four scheduling algorithms: dynamic degree balance CPU based (D2B_CPU), dynamic degree balanced membership based (D2B_Membership), dynamic degree memory balanced allocation (D2MBA) and hybrid dynamic degree balance (HDDB) algorithm. Central processing unit (CPU) utilisation, bandwidth utilisation, and memory utilisation are used as performance measures to verify the performance of these algorithms. The CloudSim simulation programme was used to simulate these algorithms. The primary goal of this work is to aid in the future construction of new algorithms by researching the behaviour of various existing algorithms.
The application YAG:Ce3+@SiO2 phosphor for improving color deviation of phosphor-converted light-emitting diode Thanh Binh Ly; Phan Xuan Le
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp618-625

Abstract

The yellow phosphor Y3Al5O12:Ce3+ (YAG:Ce3+), which sees its most popular use in white light-emitting diode (wLEDs), possess an optical spectrum that lacks the red element. The following article will propose a fresh solution for this problem, which involves adjusting the properties of Ce3+ spectrum by using exterior dye particles of ATTO-Rho101, possessing dramatic, wide absorption within the zone of green-yellow spectrum of Ce3+ emission and significant release of the red element. The globular YAG:Ce3+, which is micrometer and nanometer in size with significant dispersion (micro/nano-YAG:Ce3+) was created by employing an altered solvothermal technique. The YAG:Ce3+ produced by said technique, along with the heated micro-YAG:Ce3+ and commercial phosphors, were exteriorly covered with SiO2 and immersed in dye at the same time. Effective radiant transmission/reabsorption from Ce3+ within the YAG’s internal bowel to the dye particles of the exterior hull of SiO2, regardless of the phosphors’ size, was displayed in the YAG: Ce3+@SiO2+ dye powder amassed over the stimulation of the light of blue, which boosted the red element of it. The fluorescent microscope was considered an effective device intended for detecting the reabsorption event in grinded substances.
Machine learning model to classify modulation techniques using robust convolution neural network Nadakuditi Durga Indira; Matcha Venu Gopala Rao
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp811-819

Abstract

In wireless ccommunications receiver plays a main role to recognize modulation techniques which were used at the transmitter. While transferring information from transmitter to receiver, the receiver must retrieve original information. In order to achieve this goal we introduced a neural network architecture that recognizes the types of modulation techniques. The applications of deep learning can be categorized into classification and detection. The CNN architecture is used to perform feature extraction based on the layers to build a model which classifies the input data. A model that classifies the radio communication signals using deep learning method. The robust c (RCNN) is used to train the modulated signals; the transformations are used to help the neural network which estimate the signal to noise ratio of each signal ranges from -20dB to 18dB with loss and accuracy of 89.57% at SNR 0dB.
Effect of chaos factor in radiation pattern in planner antenna arrays with chaos adaptive invasive weed optimization Datla Rajitha; Godi Karunakar
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp692-700

Abstract

For mobile communication and spatial detection of antennas should have high directive radiation pattern, in this context pattern synthesis of planar circular antenna arrays is highly significant such design has been done inverse weed optimization. The basic objective e is to study invasive weed optimization in compression with modified chaotic adaptive invasive weed optimization. The focus of the study is the effect of chaotic factors suitable for sinusoidal mapping for chaos as applicable to the context of the design of antennas. Taking various numbers of elements of the antenna and the distance between the antenna’s radiation patterns are studied by varying chaos factors through MATLAB programming. It is found that the critical point of 2.3 for chaos factor makes the map enter into phase of chaos prior to the critical point is a phase of periodicity starting with chaos factor of 2. Below this value there is no chaos but a phase of convergence. These phases are useful having a trade of convergence and chaos. By varying the factor of chaos the impact on the radiation factor of non-uniform planar antennas has been found to give phases of convergence of chaos which are essential for making trade of between exploitation and exploration required in optimization.
A novel singular value decomposition-based ultra wide band time-of-arrival estimation for multiple targets Ibrahim Yassine Nouali; Zohra Slimane; Abdelhafid Abdelmalek
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp876-884

Abstract

It is widely admitted that the estimation of ultra wide band (UWB) time-of-arrival (TOA) for multiple targets in indoor multipath channels is a very challenging task. The existing algorithms deal with a limited number of targets and require a complex exchange of several messages. In this paper, a novel TOA estimation algorithm for multiple targets is developed. The proposed algorithm estimates the first path (FP) TOA of a number of targets without exchanging messages or using collision avoidance techniques. As a first step, the singular value decomposition (SVD) is employed to extract the first path (FP) of each target and then a matched filter, followed by an iterative threshold crossing algorithm, is used to determine the number of targets and the corresponding FP TOAs. The simulation results with four targets, using the CM1 IEEE 802.15.4a channel model, showed that the proposed novel algorithm can effectively detect the FP of each target and estimate its corresponding TOA.

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