JAREE (Journal on Advanced Research in Electrical Engineering)
JAREE is an Open Access Journal published by the Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember (ITS), Surabaya – Indonesia. Published twice a year every April and October, JAREE welcomes research papers with topics including power and energy systems, telecommunications and signal processing, electronics, biomedical engineering, control systems engineering, as well as computing and information technology.
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<![CDATA[Current Mode Control of Non-Isolated Multi-Phase Interleaved Buck-Boost Converter for Military Battery Charging Application. ]]>
<![CDATA[Ali Shaibu]]>;
<![CDATA[Ahmet Karaarslan]]>
<![CDATA[ JAREE (Journal on Advanced Research in Electrical Engineering) Vol 6, No 2 (2022): October ]]>
Publisher : <![CDATA[Department of Electrical Engineering ITS and FORTEI ]]>
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DOI: 10.12962/jaree.v6i2.315
<![CDATA[Power conversion devices are essential in a remote off-grid site, typically for military purposes where several types of equipment with varying input characteristics are supplied essentially by the same energy source, necessitating the use of a buck-boost converter for DC-DC applications. Because the conventional buck-boost converter's inverting output voltage is incompatible with some applications, the two-switch buck-boost converter is recommended for battery charging in military applications. The nominal input voltage range of the five-phase interleaved buck-boost converter is 9 – 36 V, the output voltage is 28 V, the output power is 3.3 kW, and the switching frequency is 100 kHz. The simulation of the converter with current mode control in the MATLAB/Simulink simulation environment was performed to confirm validity of the operational theory and the simulation results show consistency with theoretical values. Maximum of 74.41 ms and 51.35 % were recorded under all conditions of operation for the settling and overshoot respectively. The obtained data indicates a 1–2 % variation between the measured and theoretical duty cycle.]]>
<![CDATA[Comparison of Amplifiers Utilization in Instrumentation to Record Muscle Signals in the Neck for Electrolarynx Applications ]]>
<![CDATA[Muhammad Hilman Fatoni]]>;
<![CDATA[Nabilah Ashriyah]]>;
<![CDATA[Tri Arief Sardjono]]>;
<![CDATA[Mohammad Nuh]]>
<![CDATA[ JAREE (Journal on Advanced Research in Electrical Engineering) Vol 6, No 2 (2022): October ]]>
Publisher : <![CDATA[Department of Electrical Engineering ITS and FORTEI ]]>
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DOI: 10.12962/jaree.v6i2.329
<![CDATA[Electrolarynx is an assistive technology commonly used by speech impaired people to speak. The speech impaired people who have lost their larynx (laryngectomee) or have damaged larynx use an electrolarynx device to be able to speak again. The use of the electrolarynx is generally equipped with a button to turn on and start the generation of sound from the electrolarynx. Several studies have tried to use other control methods by using the muscles in the neck, namely the sternohyoid muscle. Activation of the sternohyoid muscle has an influence on sound formation. The sternohyoid muscle is a small and long muscle so recording EMG (electromyograph) signals from this muscle is quite difficult. If the recording process of this signal can be carried out properly, then the electrolarynx control by empowering this muscle will be another solution in using the electrolarynx. In this study, instrumentation amplifier which is an important stage of recording EMG signals of neck muscle was tested and compared. There are two types of instrumentation amplifier tested. The first instrumentation amplifier uses a single IC from IC AD620 while the other is a combination circuit of IC OP07. The EMG signal in the subject's neck muscles was then recorded using the instrumentation amplifiers. The subject will sit down and pronounce the vowels "a", and "i". From the testing process, it was found that the average gain on IC AD620 (minimum 1.74362 volts and maximum 3.70538 volts) was greater than the gain on IC OP07 (minimum 0.57779 volts and maximum 1.71190 volts). IC AD620 also has an overall use area of 5.61 cm2 with the use of 4 external components. Thus, it can be concluded that the best instrumentation amplifier for recording EMG neck muscle for electrolarynx application is by using IC AD620.]]>
<![CDATA[Design and Implementation MPPT-CPG for Constant Power Battery Charger ]]>
<![CDATA[Farah Namira Fajrianingrum]]>;
<![CDATA[Renny Rakhmawati]]>;
<![CDATA[Eka Prasetyono]]>
<![CDATA[ JAREE (Journal on Advanced Research in Electrical Engineering) Vol 6, No 2 (2022): October ]]>
Publisher : <![CDATA[Department of Electrical Engineering ITS and FORTEI ]]>
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DOI: 10.12962/jaree.v6i2.319
<![CDATA[The power produced by photovoltaic is very dependent on irradiation conditions and temperature so that the power can be low or high. The load cannot work if the power is low, but it harms the load if the power is too high. Two modes are used for this system, they are MPPT and CPG mode. When PV power is less than limit power (Plimit), then it works on MPPT mode. The MPPT finds maximum power, then if PV power reaches Plimit or more, it works on CPG mode. During CPG mode, SEPIC converter output power is maintained constant at Plimit so the battery can be charged using the Constant Power Method. The Algorithm used for each mode is the variable Step Size Climbing. The Variable Step Size Hill-Climbing on MPPT performs maximum power according to irradiation conditions. Variables Step Size Hill-Climbing on CPG stabilizes the output power value of SEPIC being constant to charge the battery using the power constant method by keeping the power as constant as its limit power, voltage and current electricity bring into line with charge conditions. The results of the hardware integrated test for MPPT-CPG, the Variable Step Size Hill Climbing algorithm can search for the maximum power point (MPP) generated by PV and can produce an output power of 27.97 W in average.]]>
<![CDATA[Gamelan Demung Music Transcription Based on STFT Using Deep Learning ]]>
<![CDATA[Andi Rokhman Hermawan]]>;
<![CDATA[Eko Mulyanto Yuniarno]]>;
<![CDATA[Diah Puspito Wulandari]]>
<![CDATA[ JAREE (Journal on Advanced Research in Electrical Engineering) Vol 6, No 2 (2022): October ]]>
Publisher : <![CDATA[Department of Electrical Engineering ITS and FORTEI ]]>
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DOI: 10.12962/jaree.v6i2.276
<![CDATA[Learning to play a gamelan instrument would be easier when there’s a musical notation guide. The process of converting a musical signal into a notation guide is called transcription. In this paper, we would like to transcript the gamelan music especially the Demung instrument using the Deep Learning method. Each Demung’s note from 6-low until 1-high would be converted to the time-frequency domain using STFT (Short-Time Fourier Transform). Then, those data will be treated as an input for the multilayers perceptron. The training method is a single label of each notation. The output returned by the model is a music roll transcription.]]>
<![CDATA[Modified Extremum Seeking Control for Target Tracking and Formation Control in Pursuit-Evasion Game ]]>
<![CDATA[Fachruddin Ari Setiawan]]>;
<![CDATA[Trihastuti Agustinah]]>;
<![CDATA[Muhammad Fuad]]>
<![CDATA[ JAREE (Journal on Advanced Research in Electrical Engineering) Vol 6, No 2 (2022): October ]]>
Publisher : <![CDATA[Department of Electrical Engineering ITS and FORTEI ]]>
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DOI: 10.12962/jaree.v6i2.320
<![CDATA[In a pursuit-evasion game, the mobile robot pursuer's ability to navigate from its initial position to the evader while maintaining a safe distance from other objects requires a good obstacle avoidance system. This study aims to perform target tracking in evader sieges and obstacle avoidance against other pursuer robots and static obstacles by proposing a modified extreme seeking controller (ESC). A modified backstepping control (BC) was used as an autopilot control for a nonholonomic mobile robot to execute the modified ESC command. The modified BC based on the modified ESC requires the positions of the targeted evader, pursuers, and obstacles. The pursuer uses this information to capture an evader by arranging the desired formation without colliding with static obstacles or other robots. The results of the simulations show that the pursuers successfully surround the evader and construct the formation without colliding with obstacles. The proposed method resulted in the closest distance of 2.071 m between the pursuers, 1.954 m between each pursuer and the evader, and 2.425 m between the pursuers and static obstacles.]]>
<![CDATA[digiRESCUE: A Smart Personal Emergency Rescue System ]]>
<![CDATA[Benjamin Kommey]]>;
<![CDATA[Elvis Tamakloe]]>;
<![CDATA[Bright Yeboah Akowuah]]>
<![CDATA[ JAREE (Journal on Advanced Research in Electrical Engineering) Vol 6, No 2 (2022): October ]]>
Publisher : <![CDATA[Department of Electrical Engineering ITS and FORTEI ]]>
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DOI: 10.12962/jaree.v6i2.278
<![CDATA[It is of no doubt that, emergencies occur without warning, and one does not predict the outcome. This project seeks to design a personal emergency or panic alert system (digiRESCUE) to aid in inevitable and dangerous situations. The digiRESCUE system has two main parts: the embedded hardware and software application. The hardware part is made up of a button embedded with a chip. This button when pressed triggers the chip, which then activates the software, portion of the system to send an automated message including the individual’s location to the selected contacts saved in the software application. The software part of the system is in the form of a mobile application, which can be downloaded onto a mobile device. This software would allow users to record or write an SOS message and input selected contacts (including friends, family, and authorities) who would be alerted of the user’s predicament when the external button in pressed. This software application can tap into the mobile device’s GPS module to inform the selected contacts of the individual’s location. The hardware would interact with the software application via Bluetooth connectivity therefore making the process of seeking for help, a lot quicker and easier. In situations where the individual is held as a hostage, he or she can easily call for help without drawing attention to oneself]]>
<![CDATA[Tracking Control of Autonomous Car with Attention to Obstacle Using Model Predictive Control ]]>
<![CDATA[Ali Fatoni]]>;
<![CDATA[Eka Iskandar]]>;
<![CDATA[Yasmina Alya]]>
<![CDATA[ JAREE (Journal on Advanced Research in Electrical Engineering) Vol 6, No 2 (2022): October ]]>
Publisher : <![CDATA[Department of Electrical Engineering ITS and FORTEI ]]>
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DOI: 10.12962/jaree.v6i2.321
<![CDATA[Previous research of MPC for path tracking and obstacle avoidance showed the car was able to evade obstacles while tracking the path but ineffectively and path tracking tests show an oscillating movement of the car. The research was done by varying cost function weights and the car was assumed to have a constant velocity. The best performance was obtained when the error weight is greater than the input weight. This research aims to use MPC for trajectory tracking and obstacle avoidance by using Linear Time Variant MPC (LTV MPC), where the trajectory tracking problem is defined by using a time-varying reference. MPC parameter is varied to find the best performing design. In the obstacle avoidance system, obstacle detection is done by measuring the distance between the instant car position and the obstacle position. While an obstacle is detected, a new lateral position constraint is calculated. Trajectory tracking tests are done using 2 types of tracks, sine wave, and lane changing. Obstacle avoidance tests are done using 1 obstacle and 2 obstacles. Results are evaluated using RMSE of car position, cost function, and the nearest distance between car and obstacle. Results show that MPC was able to evade obstacles while tracking the time-varying reference with 0.4 s delay. However, some variations were not able to meet the safe zone constraints for obstacle avoidance.]]>
<![CDATA[Cooperative Position-based Formation-pursuit of Moving Targets by Multi-UAVs with Collision Avoidance ]]>
<![CDATA[Siti Nurjanah]]>;
<![CDATA[Trihastuti Agustinah]]>;
<![CDATA[Muhammad Fuad]]>
<![CDATA[ JAREE (Journal on Advanced Research in Electrical Engineering) Vol 6, No 2 (2022): October ]]>
Publisher : <![CDATA[Department of Electrical Engineering ITS and FORTEI ]]>
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DOI: 10.12962/jaree.v6i2.310
<![CDATA[The paper focuses on the issue of capturing a moving target for multiple unmanned aerial vehicles (UAVs). The problem involves a group of UAVs to create a formation-pursuit in the encirclement of a moving target. Dynamic task allocation algorithm is used in 3D dynamic environments to efficiently allocate the target to several existing UAVs. Target information is disseminated to neighbor UAVs by the temporary leader of UAVs. For the formation-pursuit using a position-based strategy, destination points to create formation are made at the sphere coordinates around a moving target. Then the destination points are tracked using a fuzzy state feedback controller. Optimized artificial potential field (APF) algorithm is used to avoid collisions with targets, other UAVs, and static obstacles. Each UAV can choose the optimal trajectory to avoid obstacles and reset the formation after passing them. The simulation results show that multi-UAVs successfully surrounded and formed formation-pursuit of a moving target without colliding with the closest Euclidean distance between UAVs of 1.32957 m. UAVs with a target is 1.94359 m, and UAVs with static obstacles within a range of 1.60632 m.Keywords—formation-pursuit, multi-UAVs, obstacle avoidance, task allocation, tracking control.]]>
<![CDATA[Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review ]]>
<![CDATA[Arslan Ishtiaq]]>;
<![CDATA[Zubair Saeed]]>;
<![CDATA[Misha Urooj Khan]]>;
<![CDATA[Aqsa Samer]]>;
<![CDATA[Mamoona Shabbir]]>;
<![CDATA[Waqar Ahmad]]>
<![CDATA[ JAREE (Journal on Advanced Research in Electrical Engineering) Vol 6, No 2 (2022): October ]]>
Publisher : <![CDATA[Department of Electrical Engineering ITS and FORTEI ]]>
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DOI: 10.12962/jaree.v6i2.323
<![CDATA[Falls are a major public health concern among the elderly and the number of gadgets designed to detect them has increased significantly in recent years. This document provides a detailed summary of research done on fall detection systems, with comparisons across different types of studies. Its purpose is to be a resource for doctors and engineers who are planning or conducting field research. Following the examination, datasets, limitations, and future imperatives in fall detection were discussed in detail. The quantity of research using context-aware approaches continues to rise, but there is a new trend toward integrating fall detection into smartphones, as well as the use of artificial intelligence in the detection algorithm. Concerns with real-world performance, usability, and reliability are also highlighted.]]>
<![CDATA[Measurement And Characterization of Radio Propagation Channels from The Patient Room Hub to The Nurse Station Server for WBAN Medical Applications ]]>
<![CDATA[Achmad Mauludiyanto]]>;
<![CDATA[Gamantyo Hendrantoro]]>;
<![CDATA[Darien Raditya Krisdaniawan]]>
<![CDATA[ JAREE (Journal on Advanced Research in Electrical Engineering) Vol 6, No 2 (2022): October ]]>
Publisher : <![CDATA[Department of Electrical Engineering ITS and FORTEI ]]>
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DOI: 10.12962/jaree.v6i2.314
<![CDATA[As time passes, technology is demanded to be more sophisticated, efficient, and fully automatic. Technology development also impacts the medical field since hospitals require a tool that can automatically, thoroughly, and accurately record the physical condition of patients in the inpatient room and forward the results to be checked by the nurses on duty at the nurse station. Automation of patient data recording needs to be performed flexibly and comfortably for patients; thus, a wireless device channelling sensors on the patient's body to the nurse station is required. On this account, it demands a measurement to characterise the radio propagation channels. Therefore, this paper highlights the radio channel between the inpatient room hubs and the nurse station server. The measurement was performed in two rooms separated by a wall in the Antenna and Propagation Laboratory, representing an inpatient room and the nurse station. Further, the channel frequency was set at 3 GHz with a bandwidth of 200 MHz. Through the Vector Network Analyzer (VNA), the result revealed the channel response characteristic that the denser the propagation channels, the lower the power received and vice versa.]]>
