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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 9,174 Documents
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Control of inventory system with random demand and product damage during delivery using the linear quadratic gaussian method Sutrisno Sutrisno; Widowati Widowati; R. Heru Tjahjana
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1748-1753

Abstract

This study formulates a dynamical system for the control of a single product inventory system in accordance with the random value of demand and the percentage of damaged product during the delivery process. The formulated model has the form of a linear state-space system comprising of two disturbances, which represents the random value of demand and the percentage of the damaged product during delivery. The optimal value of the product amount ordered to the supplier is properly calculated by using the linear quadratic gaussian (LQG) method. The controller is used by the manager to make inventory level decisions under the uncertainty of demand and damaged items during the product delivery process. The result showed that the optimal product order for each review time was achieved, and the inventory level was used to obtain the right set point properly. Moreover, based on comparison with other research results, the proposed model was well performed.
On the dispatch of minigrids with large penetration levels of variable renewable energy Anan A. Dweekat; Mohamed Shaaban; Sze Song Ngu
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 2: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i2.pp673-681

Abstract

The continuous use of fossil fuels for decades in electricity generation has led to dire environmental consequences. This has fostered the incorporation of variable renewable energy resources (RES) to improve the environmental outlook and minimize emissions. This paper presents an approach for the dispatch of a minigrid considering variable solar photovoltaic (PV) generation. Due to the variability of the solar irradiance received from the sun during daytime only, solar irradiance is modeled as a stochastic random variable that is fitted into a Beta probability density function (PDF). The minigrid dispatch problem, modeled using stochastic optimization, is then approximated into a linear equivalent to become a mixed integer linear programming (MILP) problem that can be solved efficiently. The proposed approach is implemented on the modified IEEE 14-bus test system to verify its capability in solving the minigrid dispatch under various test case scenarios.  
Efficient wireless power transmission to remote the sensor in restenosis coronary artery Mokhalad Alghrairi; Nasri Sulaiman; Wan Zuha Wan Hasan; Haslina Jaafar; Saad Mutashar
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp771-779

Abstract

In this study, the researchers have proposed an alternative technique for designing an asymmetric 4 coil-resonance coupling module based on the series-to-parallel topology at 27 MHz industrial scientific medical (ISM) band to avoid the tissue damage, for the constant monitoring of the in-stent restenosis coronary artery. This design consisted of 2 components, i.e., the external part that included 3 planar coils that were placed outside the body and an internal helical coil (stent) that was implanted into the coronary artery in the human tissue. This technique considered the output power and the transfer efficiency of the overall system, coil geometry like the number of coils per turn, and coil size. The results indicated that this design showed an 82% efficiency in the air if the transmission distance was maintained as 20 mm, which allowed the wireless power supply system to monitor the pressure within the coronary artery when the implanted load resistance was 400 Ω.
Enhancement of cloud performance metrics using dynamic degree memory balanced allocation algorithm Aparna Shashikant Joshi; Shayamala Devi Munisamy
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1697-1707

Abstract

In cloud computing, load balancing among the resources is required to schedule a task, which is a key challenge. This paper proposes a dynamic degree memory balanced allocation (D2MBA) algorithm which allocate virtual machine (VM) to a best suitable host, based on availability of random-access memory (RAM) and microprocessor without interlocked pipelined stages (MIPS) of host and allocate task to a best suitable VM by considering balanced condition of VM. The proposed D2MBA algorithm has been simulated using a simulation tool CloudSim by varying number of tasks and keeping number of VMs constant and vice versa. The D2MBA algorithm is compared with the other load balancing algorithms viz. Round Robin (RR) and dynamic degree balance with central processing unit (CPU) based (D2B_CPU based) with respect to performance parameters such as execution cost, degree of imbalance and makespan time. It is found that the D2MBA algorithm has a large reduction in the performance parameters such as execution cost, degree of imbalance and makespan time as compared with RR and D2B CPU based algorithms
An intelligent irrigation system based on internet of things (IoT) to minimize water loss Samar Amassmir; Said Tkatek; Otman Abdoun; Jaafar Abouchabaka
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp504-510

Abstract

This paper proposes a comparison of three machine learning algorithms for a better intelligent irrigation system based on internet of things (IoT) for differents products. This work's major contribution is to specify the most accurate algorithm among the three machine learning algorithms (k-nearest neighbors (KNN), support vector machine (SVM), artificial neural network (ANN)). This is achieved by collecting irrigation data of a specific products and split it into training data and test data then compare the accuracy of the three algorithms. To evaluate the performance of our algorithm we built a system of IoT devices. The temperature and humidity sensors are installed in the field interact with the Arduino microcontroller. The Arduino is connected to Raspberry Pi3, which holds the machine learning algorithm. It turned out to be ANN algorithm is the most accurate for such system of irrigation. The ANN algorithm is the best choice for an intelligent system to minimize water loss for some products.
An approach of adaptive notch filtering design for electrocardiogram noise cancellation Rahmad Hidayat; Ninik Sri Lestari; Herawati Herawati; Givy Devira Ramady; Sudarmanto Sudarmanto; Farhan Adani
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1303-1311

Abstract

An electrocardiogram (ECG) is a means of measuring and monitoring important signals from heart activity. One of the major biomedical signal issues such as ECG is the issue of separating the desired signal from noise or interference. Different kinds of digital filters are used to distinguish the signal components from the unwanted frequency range to the ECG signal. To address the question of noise to the ECG signal, in this paper the digital notch filter IIR 47 Hz is designed and simulated to demonstrate the elimination of 47 Hz noise to obtain an accurate ECG signal. The full architecture of the structure and coefficient of the IIR notch filter was carried out using the FDA Tool. Then the model is finished with the help of Simulink and the MATLAB script was to filter out the 47 Hz noise from the signal of ECG. For this purpose, the normalized least mean square (NLMS) algorithm was used. The results indicate that before being filtered and after being filtered it clearly shows the elimination of 47 Hz noise in the signal of the ECG. These results also show the accuracy of the design technique and provide an easy model to filter out noise in the ECG signal.
Deadlock detection in distributed system Kshirod Kumar Rout; Debani Prasad Mishra; Surender Reddy Salkuti
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1596-1603

Abstract

In highly automated devices, deadlock is a case that occurs when no system can permit its event which may give irrelevant economic losses. A process can request or release resources that are either available or are on hold by others. If a process requesting a resource is not available at any time, then that process enters into the waiting state. But if a waiting state is not converted into its present state, it enters more than two processes are having an indefinite waiting state. The proposed algorithm gives an efficient way for deadlock detection. For the implementation of this work, C++ and python as the basic programming language are used. It gives an idea about how resources are allocated, and how few processes result in deadlock.
Enhancing the feature-based 3D deformable face recognition using hybrid PCA-NN Cahyo Darujati; Supeno Mardi Susiki Nugroho; Deny Kurniawan; Mochamad Hariadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp215-221

Abstract

Facial recognition is one of the most important advancements in image processing. An important job is to build an automated framework with the same human capacity’s for recognizing face. The face is a complex 3D graphical model, and constructing a computational model is a challenging task. This paper aims at a facial detection technique focused on the coding and decoding of the facial feature object theory approach to data. One of the most natural and common principal component analysis (PCA) method. This approach transforms the face features into a minimal set of basic attributes, peculiarities, which are the critical components of the original learning image collection (or the training package). The proposed technique is a combination of the PCA system and the identification of components using the neural network (NN) feed-forward propagation method. This experiment proves that recognition of deformed 3D face is doable. By taking into account almost all forms of feature extraction and engineering, the NN yields a recognition score of 95%.
A hybrid strategy for emotion classification Hussah Nasser Aleisa
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1400-1406

Abstract

Human emotion recognition is an upcoming research field of human computer interaction based on facial gestures and is being used for real-time analysis in classifying cognitive affective states from a facial video data. Since computers have become an integral part of life, many researchers are using emotion recognition and classification of data based on audio and text. But these approaches offer limited accuracy and relevance in emotion classification. Therefore we have introduced and analyzed a hybrid approach which could outperform the existing strategies that uses an innovative approach supported by selection of audio and video data characteristics for classification. The research uses SVM for classifying the data using audio-visual savee database and the results obtained show maximum classification accuracy with respect to audio data about 91.6 could be improved to 99.2% after the application of hybrid strategy.
Analyzing semantic similarity amongst textual documents to suggest near duplicates Devarajan, Viji; Subramanian, Revathy
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1703-1711

Abstract

Data deduplication techniques removing repeated or redundant data from the storage. In recent days, more data has been generated and stored in the storage environment. More redundant and semantically similar content of the data occupied in the storage environment due to this storage efficiency will be reduced and cost of the storage will be high. To overcome this problem, we proposed a method hybrid bidirectional encoder representation from transformers for text semantics using graph convolutional network hybrid bidirectional encoder representation from transformers (BERT) model for text semantics (HBTSG) word embedding-based deep learning model to identify near duplicates based on the semantic relationship between text documents. In this paper we hybridize the concepts of chunking and semantic analysis. The chunking process is carried out to split the documents into blocks. Next stage we identify the semantic relationship between documents using word embedding techniques. It combines the advantages of the chunking, feature extraction, and semantic relations to provide better results.

Page 642 of 918 | Total Record : 9174

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