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29,922 Article Results

LaSiO3Cl:Ce3+,Tb3+ and Mg2TiO4:Mn4+: quantum dot phosphors for improving the optical properties of WLEDs

10.11591/ijece.v10i5.pp5191-5197
Nguyen Thi Phuong Loan , Nguyen Doan Quoc Anh
In this research, we focus on the solutions to enhance the lighting properties as well as the heat regulation of the white light-emitting diodes (WLEDs) with conventional phosphor and quantum dots (QDs). Although receiving lots of attention for being an innovative lighting solution with good color rendering index, the potentials of WLEDs conjugated with quantum dots (QDS), especially the QDs-phosphor mixed nanocomposites ones, are restrained due to the lacking performance in the aspects mentioned above. The crucial requirement to produce better WLEDs is finding solutions that improve the lacking aspects, therefore, through observing previous studies and applying advanced technique, this research suggest an effective and unique packaging configuration, in which the nanocomposites QDs-phosphor layer is set horizontally to the WLED. This novel packaging configuration allow WLED performance in terms of lighting and heating to reach it peaks. This is the first time four different types of WLEDs, single-layer phosphor, dual-layer remote phosphor with yellow-red and yellow-green, and triple-layer phosphor, were simulated, utilized and compared in one study to decide the best WLED configuration. The results show that the triple-layer phosphor configurations improve the color rendering ability and lumen output better than the other configurations.
Volume: 10
Issue: 5
Page: 5191-5197
Publish at: 2020-10-01

Optimized output-based input shaping for control of single-link flexible manipulator using linear matrix inequality

10.11591/ijeecs.v20.i1.pp109-116
Nura Musa Tahir , Mustapha Muhammad , Bashir Bala Muhammad , Haliru Liman , Aminu Yahaya Zimit , Auwal Shehu Tijjani
Precise hub angle positioning due to tip deflections, flexible motions and under various payloads is enormous tasks in the control of single-link flexible manipulators. In this paper, output-based command shaping (OBCS) was designed using the system output for tip deflections and residuals vibrations suppression, and this was incorporated with a linear matrix inequality (LMI) closed-loop control scheme for precise hub angle positioning.  The robustness of the hybrid control scheme was tested by changing the payloads from 0g to 30g, and 50g. Simulation results showed that endpoint residuals vibrations and tip deflections due to flexible motions were suppressed and hence precise hub angle positioning under various payloads was achieved. Integral absolute error (IAE), integral square error (ISE) and time response analysis (TRA) were used as the performance indexes. Hence, the hybrid control scheme is simple and robust.
Volume: 20
Issue: 1
Page: 109-116
Publish at: 2020-10-01

Determining optimal location and size of capacitors in radial distribution networks using moth swarm algorithm

10.11591/ijece.v10i5.pp4514-4521
Thanh Long Duong , Thuan Thanh Nguyen , Van-Duc Phan , Thang Trung  Nguyen
In this study, the problem of optimal capacitor location and size determination (OCLSD) in radial distribution networks for reducing losses is unraveled by moth swarm algorithm (MSA). MSA is one of the most powerful meta-heuristic algorithm that is taken from the inspiration of the food source finding behavior of moths. Four study cases of installing different numbers of capacitors in the 15-bus radial distribution test system including two, three, four and five capacitors areemployed to run the applied MSA for an investigation of behavior and assessment of performances. Power loss and the improvement of voltage profile obtained by MSA are compared with those fromother methods. As a result, it can be concluded that MSA can give a good truthful and effective solution method for OCLSD problem.
Volume: 10
Issue: 5
Page: 4514-4521
Publish at: 2020-10-01

Viterbi optimization for crime detection and identification

10.12928/telkomnika.v18i5.13398
Reem Razzaq Abdul; University of Information Technology and Communications Hussain , Salih Mahdi; University of Technology Al-Qaraawi , Muayad Sadik; University of Technology Croock
In this paper, we introduce two types of hybridization. The first contribution is the hybridization between the Viterbi algorithm and Baum Welch in order to predict crime locations. While the second contribution considers the optimization based on decision tree (DT) in combination with the Viterbi algorithm for criminal identification using Iraq and India crime dataset. This work is based on our previous work [1]. The main goal is to enhance the results of the model in both consuming times and to get a more accurate model. The obtained results proved the achievement of both goals in an efficient way.
Volume: 18
Issue: 5
Page: 2378-2384
Publish at: 2020-10-01

Sound event detection using deep neural networks

10.12928/telkomnika.v18i5.14246
Suk-Hwan; Keimyung University Jung , Yong-Joo; Keimyung University Chung
We applied various architectures of deep neural networks for sound event detection and compared their performance using two different datasets. Feed forward neural network (FNN), convolutional neural network (CNN), recurrent neural network (RNN) and convolutional recurrent neural network (CRNN) were implemented using hyper-parameters optimized for each architecture and dataset. The results show that the performance of deep neural networks varied significantly depending on the learning rate, which can be optimized by conducting a series of experiments on the validation data over predetermined ranges. Among the implemented architectures, the CRNN performed best under all testing conditions, followed by CNN. Although RNN was effective in tracking the time-correlation information in audio signals,it exhibited inferior performance compared to the CNN and the CRNN. Accordingly, it is necessary to develop more optimization strategies for implementing RNN in sound event detection.
Volume: 18
Issue: 5
Page: 2587-2596
Publish at: 2020-10-01

A review of control algorithm for autonomous guided vehicle

10.11591/ijeecs.v20.i1.pp552-562
Faiza Gul
The autonomous guided vehicle is a efficient and effective platform for control system. Their non-linear nature helps in analysing the control algorithms more efficiently and effectively. The main objective of path planning is to find the optimal and shortest path avoiding the time complexity so environment can be modelled completely for vehicle. The paper includes explanation of different systems together with numerous algorithms have been discussed with advantages and disadvantages for example: Fuzzy control, Neural Control, Back-stepping control, Adaptive control, Sliding mode control and PID control and linear quadratic regulator. The conclusion includes the hybrid system integration based on the advantages and disadvantages presented in this paper.
Volume: 20
Issue: 1
Page: 552-562
Publish at: 2020-10-01

Investigation and design of ion-implanted MOSFET based on (18 nm) channel length

10.12928/telkomnika.v18i5.15958
Firas Natheer; University of Mosul Abdul-kadir , Khalid khaleel; Ninevah University Mohammad , Yasir; Tishk International University Hashim
The aim of this study is to invistgate the characteristics of Si-MOSFET with 18 nm length of ion implemented channel. Technology computer aided design (TCAD) tool from Silvaco was used to simulate the MOSFET’s designed structure in this research. The results indicate that the MOSFET with 18 nm channel length has cut-off frequency of 548 GHz and transconductance of 967 μS, which are the most important factors in calculating the efficiency and improving the performance of the device. Also, it has threshold voltage of (-0.17 V) in addition obtaining a relatively small DIBL (55.11 mV/V). The subthreshold slope was in high value of 307.5 mV/dec. and this is one of the undesirable factors for the device results by short channel effect, but it does not reduce its performance and efficiency in general.
Volume: 18
Issue: 5
Page: 2635-2641
Publish at: 2020-10-01

Sr[Mg3SiN4]Eu2+ phosphor: solution for enhancing the optical properties of the 5600K remote-packaging WLEDs

10.12928/telkomnika.v18i5.13860
Phu Tran; Ton Duc Thang University Tin , Duy Hung; Ton Duc Thang University Ha , Minh; Ton Duc Thang University Tran
In the last decade, light-emitting diodes (LEDs), which based on spontaneous light emission in semiconductors can be considered as the main light sources for civil and industrial purposes. In this paper, we presented and investigated the effect of the Sr[Mg3SiN4]Eu2+ concentration on the optical properties of the 5600K remote-packaging WLEDs (RP-WLEDs). We use the Mat Lab and the LightTool software to investigate the effect of the Sr[Mg3SiN4]Eu2+ concentration on the CRI, CQS, D-CCT and LO of the 5600K RP-WLEDs. From the result, we can state that the concentration of the Sr[Mg3SiN4]Eu2+ influenced on the CRI, CQS, D-CCT and LO of the RP-WLEDs. The red Sr[Mg3SiN4]Eu2+ phosphor can be considered as the novel recommendation for LEDs industry.
Volume: 18
Issue: 5
Page: 2385-2390
Publish at: 2020-10-01

Hidden Markov model technique for dynamic spectrum access

10.12928/telkomnika.v18i5.14470
Jayant P; Research Scholar Sant Gadge Baba Amaravati University Pawar , Prashant; PRMIT & R V. Ingole
Dynamic spectrum access is a paradigm used to access the spectrum dynamically. A hidden Markov model (HMM) is one in which you observe a sequence of emissions, but do not know the sequence of states the model went through to generate the emissions. Analysis of hidden Markov models seeks to recover the sequence of states from the observed data. In this paper, we estimate the occupancy state of channels using hidden Markov process. Using Viterbi algorithm, we generate the most likely states and compare it with the channel states. We generated two HMMs, one slowly changing and another more dynamic and compare their performance. Using the Baum-Welch algorithm and maximum likelihood algorithm we calculated the estimated transition and emission matrix, and then we compare the estimated states prediction performance of both the methods using stationary distribution of average estimated transition matrix calculated by both the methods.
Volume: 18
Issue: 5
Page: 2780-2786
Publish at: 2020-10-01

Fault classification on transmission line using LSTM network

10.11591/ijeecs.v20.i1.pp231-238
Abdul Malek Saidina Omar , Muhammad Khusairi Osman , Mohammad Nizam Ibrahim , Zakaria Hussain , Ahmad Farid Abidin
Deep Learning has ignited great international attention in modern artificial intelligence techniques. The method has been widely applied in many power system applications and produced promising results. A few attempts have been made to classify fault on transmission lines using various deep learning methods. However, a type of deep learning called long short-term memory (LSTM) has not been reported in literature. Therefore, this paper presents fault classification on transmission line using LSTM network as a tool to classify different types of faults. In this study, a transmission line model with 400 kV and 100 km distance was modelled. Fault free and 10 types of fault signals are generated from the transmission line model. Fault signals are pre-processed by extracting post-fault current signals. Then, these signals are fed as input to the LSTM network and trained to classify 10 types of faults. The white Gaussian noise of level 20 dB and 30 dB signal to noise ratio (SNR) is also added to the fault current signals to evaluate the immunity of the proposed model. Simulation results show promising classification accuracy of 100%, 99.77% and 99.55% for ideal, 30 dB and 20 dB noise respectively. Results has been compared to four different methods which can be seen that the LSTM leading with the highest classification accuracy. In line with the purpose of the LSTM functions, it can be concluded that the method has a capability to classify fault signals with high accuracy.
Volume: 20
Issue: 1
Page: 231-238
Publish at: 2020-10-01

Excellent color quality of phosphor converted white light emitting diodes with remote phosphor geometry

10.12928/telkomnika.v18i5.13575
Thinh Cong; Ton Duc Thang University Tran , Nguyen Doan Quoc; Ton Duc Thang University Anh , Nguyen Thi Phuong; Posts and Telecommunications Institute of Technology, Loan
The remote phosphor structure is disadvantageous in color quality but more convenient in luminous flux when compared to conformal phosphor or in-cup phosphor structure. From this disadvantage, there are many studies to improve the color quality of the remote phosphor structure. This research will propose a dual-layer remote phosphor structure to improve color rendering index (CRI) and color quality scale (CQS) of WLEDs. The WLED package with color temperature of 8500 K is utilized in this study. The idea of the study is to locate a layer of phosphor green Y2O2S:Tb3+ or red ZnS:Sn2+ on the yellow phosphor YAG:Ce3+ film, and then finding the suitable added concentration of ZnS:Sn2+ to match the highest color quality. The results showed that ZnS:Sn2+ brings great benefits to increase CRI and CQS. The greater the ZnS:Sn2+ concentration is, the higher the CRI and CQS become owing to the rise in red light components in WLEDs. Meanwhile, the green Y2O2S:Tb3+ phosphor brings benefits to luminous flux. However, the decrease in luminous flux and color quality occurs when the concentration of ZnS:Sn2+ and Y2O2S:Tb3+ exceeds the corresponding level. This is proved by applying Mie-scattering theory and Lambert-Beer's law. The results of articles are important for WLEDs’ fabrication having higher white light quality.
Volume: 18
Issue: 5
Page: 2757-2763
Publish at: 2020-10-01

Precipitation prediction using recurrent neural networks and long short-term memory

10.12928/telkomnika.v18i5.14887
Mishka Alditya; Institut Teknologi Bandung Priatna , Esmeralda C.; Universitas Jenderal Achmad Yani Djamal
Prediction of meteorological variables such as precipitation, temperature, wind speed, and solar radiation is beneficial for human life. The variable observations data is available from time to time for more than thirty years, scattered each observation station makes the opportunity to map patterns into predictions. However, the complexity of weather variables is very high, one of which is influenced by Decadal phenomena such as El-Nino Southern Oscillation and IOD. Weather predictions can be reviewed for the duration, prediction variables, and observation stations. This research proposed precipitation prediction using recurrent neural networks and long short-term memory. Experiments were carried out using the prediction duration factor, the period as a feature and the amount of data set used, and the optimization model. The results showed that the time-lapse as a shorter feature gives good accuracy. Also, the duration of weekly predictions provides more accuracy than monthly, which is 85.71% compared to 83.33% of the validation data.
Volume: 18
Issue: 5
Page: 2525-2532
Publish at: 2020-10-01

Error rate detection due to primary user emulation attack in cognitive radio networks

10.11591/ijece.v10i5.pp5385-5391
N. Armi , W. Gharibi , W.Z. Khan
Security threat is a crucial issue in cognitive radio network (CRN). These threats come from physical layer, data link layer, network layer, transport layer, and application layer. Hence, security system to all layers in CRN has a responsibility to protect the communication between among Secondary User (SU) or to maintain valid detection to the presence of Primary User (PU) signals. Primary User Emulation Attack (PUEA) is a threat on physical layer where malicious user emulates PU signal. This paper studies the effect of exclusive region of PUEA in CRN. We take two setting of exclusive distances, 30m and 50m, where this radius of area is free of malicious users. Probability of false alarm (Pf) and miss detection (Pm) are used to evaluate the performances. The result shows that increasing distance of exclusive region may decrease Pf and Pm.
Volume: 10
Issue: 5
Page: 5385-5391
Publish at: 2020-10-01

Simulation and optimization of tuneable microstrip patch antenna for fifth-generation applications based on graphene

10.11591/ijece.v10i5.pp5546-5558
Hamzah M. Marhoon , Nidal Qasem
Microstrip patch antennas (MPAs) are known largely for their versatility in terms of feasible geometries, making them applicable in many distinct circumstances. In this paper, a graphene-based tuneable single/array rectangular microstrip patch antenna (MPA) utilizing an inset feed technique designed to function in multiple frequency bands are used in a fifth-generation (5G) wireless communications system. The tuneable antenna is used to eliminate the difficulties caused by the narrow bandwidths typically associated with MPAs. The graphene material has a reconfigurable surface conductivity that can be adjusted to function at the required value, thus allowing the required resonance frequency to be selected. The simulated tuneable antenna comprises a copper radiating patch with four graphene strips used for tuning purposes and is designed to cover a wide frequency band. The proposed antenna can be tuned directly by applying a direct current (DC) voltage to the graphene strips, resulting in a variation in the surface impedance of the graphene strips and leading to shifts in the resonance frequency.
Volume: 10
Issue: 5
Page: 5546-5558
Publish at: 2020-10-01

Comparative study on machine learning algorithms for early fire forest detection system using geodata

10.11591/ijece.v10i5.pp5507-5513
Zouiten Mohammed , Chaaouan Hanae , Setti Larbi
Forest fires have caused considerable losses to ecologies, societies and economies worldwide. To minimize these losses and reduce forest fires, modeling and predicting the occurrence of forest fires are meaningful because they can support forest fire prevention and management. In recent years, the convolutional neural network (CNN) has become an important state-of-the-art deep learning algorithm, and its implementation has enriched many fields. Therefore, a competitive spatial prediction model for automatic early detection of wild forest fire using machine learning algorithms can be proposed. This model can help researchers to predict forest fires and identify risk zonas. System using machine learning algorithm on geodata will be able to notify in real time the interested parts and authorities by providing alerts and presenting on maps based on geographical treatments for more efficacity and analyzing of the situation. This research extends the application of machine learning algorithms for early fire forest prediction to detection and representation in geographical information system (GIS) maps.
Volume: 10
Issue: 5
Page: 5507-5513
Publish at: 2020-10-01
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