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All Journal IAES International Journal of Robotics and Automation (IJRA) Indonesian Journal of Aerospace
Rini Akmeliawati
Intelligent Mechatronics Systems Research Unit, Faculty of Engineering, International Islamic University Malaysia
Aerospace Engineering Astronomy Automotive Engineering Electrical & Electronics Engineering

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Adaptive language processing unit for Malaysian sign language synthesizer Maarif, Haris Al Qodri; Gunawan, Teddy Surya; Akmeliawati, Rini
IAES International Journal of Robotics and Automation (IJRA) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v10i4.pp326-339

Abstract

Language processing unit (LPU) is a system built to process text-based data to comply with the rules of the sign language grammar. This system was developed as an important part of the sign language synthesizer system. Sign language (SL) uses different grammatical rules from the spoken/verbal language, which only involves the important words that hearing/impaired speech people can understand. Therefore, it needs word classification by LPU to determine grammatically processed sentences for the sign language synthesizer. However, the existing language processing unit in SL synthesizers suffers time lagging and complexity problems, resulting in high processing time. The two features, i.e., the computational time and success rate, become trade-offs which means the processing time becomes longer to achieve a higher success rate. This paper proposes an adaptive LPU that allows processing the words from spoken words to Malaysian SL grammatical rule that results in relatively fast processing time and a good success r ate. It involves n-grams, natural language processing (NLP) , and hidden Markov models (HMM)/Bayesian networks as the classifier to process the text-based input. As a result, the proposed LPU system has successfully provided an efficient (fast) processing time and a good success rate compared to LPU with other edit distances (mahalanobis, Levenshtein, and soundex). The system has been tested on 130 text-input sentences with several words ranging from 3 to 10 words. Results showed that the proposed LPU could achieve around 1.497ms processing time with an average success rate of 84.23% for a maximum of ten-word sentences.
POWERED LANDING GUIDANCE ALGORITHMS USING REINFORCEMENT LEARNING METHODS FOR LUNAR LANDER CASE Nugroho, Larasmoyo; Zani, Novanna Rahma; Qomariyah, Nurul; Akmeliawati, Rini; Andiarti, Rika; Wijaya, Sastra Kusuma
Indonesian Journal of Aerospace Vol. 19 No. 1 (2021)
Publisher : BRIN Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.jtd.2021.v19.a3573

Abstract

Any future planetary landing missions, just as demonstrated by Perseverance in 2021 Mars landing mission require advanced guidance, navigation, and control algorithms for the powered landing phase of the spacecraft to touch down a designated target with pinpoint accuracy (circular error precision < 5 m radius). This requires a landing system capable to estimate the craft’s states and map them to certain thrust commands for each craft’s engine. Reinforcement learning theory is used as an approach to manage the mapping guidance algorithm and translate it to engine thrust control commands. This work compares several reinforcement learning based approaches for a powered landing problem of a spacecraft in a two-dimensional (2-D) environment, and identify the advantages/disadvantages of them. Five methods in reinforcement learning, namely Q-Learning, and its extension such as DQN, DDQN, and policy optimization-based such as DDPG and PPO are utilized and benchmarked in terms of rewards and training time needed to land the Lunar Lander. It is found that Q-Learning method produced the highest efficiency. Another contribution of this paper is the use of different discount rates for terminal and shaping rewards, which significantly enhances optimization performance. We present simulation results demonstrating the guidance and control system’s performance in a 2-D simulation environment and demonstrate robustness to noise and system parameter uncertainty.
Co-Authors Andiarti, Rika Larasmoyo Nugroho MAARIF, HARIS AL QODRI Nurul Qomariyah Sastra Kusuma Wijaya Teddy Surya Gunawan Zani, Novanna Rahma