Prototype control and monitoring system safety device from leakage ammonia at marine loading arm with comparison of Neural Network (NN) and Extreme Learning Machine (ELM) method

Hanggara, P. Oktavian; Paradisa, P. Fahrizal; Arifin, Muhamad; Sarena, Sryang Tera; Syai’in, Mat; Adhitya, Ryan Yudha; Haydlaar, Aliy; Atmoko, Rachmad Andri; Asri, P.; Soeprijanto, Adi

doi:10.1109/isesd.2017.8253310

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…This research can be further developed by adding USV facilities that support remote laboratory needs such as the environmental sensors and machine learning algorithm for solving task complexity in water area [21]. Regarding communication, development needs to be done by comparing with other protocols that are developing and used in the Internet of Things application.…”

Section: Discussionmentioning

confidence: 99%

Controlling Unmanned Surface Vehicle Using MQTT Protocol

Atmoko

Yang

Alfiani³

et al. 2019

CNAPC

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Abstract- The communication system at USV is the most important part of ensuring that the USV control system runs well. Communication systems that are commonly used at USV are generally still local networks. The development of a wider range of communications is needed to improve USV scalability so that the benefits can be increased to support the concept of remote laboratory and remote surveillance. USV is generally controlled using a wireless network with a limited work area using WIFI or using another electromagnetic waves transmission. This research proposes the development control and monitoring systems of USV to be controlled over the Internet by using the message queuing telemetry transport(MQTT) Protocol. This idea makes the USV can be controlled remotely, no longer have to operate it in the field. The control speed response of rudder and motor are analyzed. Performance monitoring of the compass sensor, GPS, and cameras also showed good results. As a result, the average delay time generated is 1.6 second.

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Section: Discussionmentioning

confidence: 99%

Controlling Unmanned Surface Vehicle Using MQTT Protocol

Atmoko

Yang

Alfiani³

et al. 2019

CNAPC

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“…Politeknik Perkapalan Negeri Surabaya (PPNS) sebagai politeknik vokasi menunjukkan keunggulannya di bidang otomasi dan mesin melalui inovasi seperti mesin pengisian produk otomatis (E-FILL) dan Aplikasi Rekonstruksi 3D dengan Lidar, dan juga telah membuktikan komitmen luar biasa di berbagai aspek. Sebagai institusi teaching factory yang berpengalaman, PPNS telah berhasil menghasilkan produk unggulan seperti ROV, AIS, Sistem pengapuran otomatis (Auto Liming Unit) dan Sistem monitoring lingkungan, mencerminkan kehandalan dan Jurnal Elkolind Volume 11, Nomor 1, Mei 2024 DOI: http://dx.doi.org/10.33795/elkolind.v11i1.5146 keahlian dalam menerapkan konsep-konsep teknologi terkini [8] . Dalam penelitian mesin pemecah batu, PPNS memperkaya pengalaman dengan mengintegrasikan teknologi terkini melalui penambahan monitoring energi untuk pemantauan efisiensi energi [9].…”

Section: Pendahuluanunclassified

Simulasi Kontrol PID Ziegler-Nichols pada Sistem Penghancuran Batu dengan Motor Induksi 3 Fasa 20HP

Ramadhan

2024

ELK

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Penelitian ini bertujuan untuk mengembangkan pengendalian otomatis pada mesin crusher batu koral menggunakan metode PID Ziegler-Nichols. Mesin crusher otomatis diatur dengan parameter PID yang dihasilkan dari metode Ziegler-Nichols, yaitu Kp=0.0957, Ki=0.9643, dan Kd=0.0024, dengan respons stabil setiap 1 detik setelah terjadi osilasi. Metode PID dipilih karena kemampuannya untuk mengontrol sistem secara otomatis dengan mengkombinasikan proporsional, integral, dan derivatif dari kesalahan sistem. Metode Ziegler-Nichols digunakan untuk menentukan parameter PID dengan melakukan uji respons terhadap sistem dan mengidentifikasi parameter yang optimal. Hasil penelitian menunjukkan bahwa metode PID Ziegler-Nichols memberikan pengendalian yang efisien dan stabil pada mesin crusher, meningkatkan kinerja dan keandalan proses penghancuran batu koral secara keseluruhan. Penelitian ini memberikan kontribusi penting dalam pengembangan sistem pengendalian otomatis untuk industri pemecahan batu koral yang membutuhkan tingkat presisi dan efisiensi yang tinggi.

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“…The MOSFET was used as an input voltage regulator based on the pulse width reference given by the defuzzification results [22], [23]. The resolution of the PWM setting facility is 8 bits or the equivalent of 0-255 decimals [24].…”

Section: Prototype Designmentioning

confidence: 99%

The application of Sugeno fuzzy to control active power load and remaining battery usage time modelling

Pambudi

Firmansyah

Issany

et al. 2022

IJEECS

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This study aimed to propose a control scheme to <span>optimize the active power load using the pulse-width adjustment technique and the MOSFET driver. The power used and stored in the battery is an input for the fuzzy system whose information is obtained through the current (ACS 712) and voltage sensor readings. The use of fuzzy to control the power of two 5-watt lamps is more efficient than the manual technique using an ordinary switch. This is because fuzzy only consumes 3.25 Watt/hour while the manual technique requires 5.7 Watt/hour. Based on the linear regression-based estimation, the fuzzy technique lasts ±17 hours from the initial power of 55.82 Wh, or 5.5 hours longer than the manual technique that lasts only ±11.5 hours from the initial power of 65.62 Wh. Therefore, this study adjusted the load power to extend battery life and increase solar energy use efficiency and innovations in load control based on available resources.</span>

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Prototype control and monitoring system safety device from leakage ammonia at marine loading arm with comparison of Neural Network (NN) and Extreme Learning Machine (ELM) method

Cited by 10 publications

References 12 publications

Controlling Unmanned Surface Vehicle Using MQTT Protocol

Controlling Unmanned Surface Vehicle Using MQTT Protocol

Simulasi Kontrol PID Ziegler-Nichols pada Sistem Penghancuran Batu dengan Motor Induksi 3 Fasa 20HP

The application of Sugeno fuzzy to control active power load and remaining battery usage time modelling

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