Design and Application of an Autonomous Surface Vehicle with an AI-Based Sensing Capability

Tsai, Chia-Ming; Lai, Yi; Perng, Jau-Woei; Tsui, I-Fong; Chung, Yu-Jen

doi:10.1109/ut.2019.8734350

Cited by 14 publications

(4 citation statements)

References 8 publications

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“…Autonomous Submarine Surface Vehicle telah mengalami kemajuan teknologi membuat potensi ASSV meningkat [2]. Peningkatan ini membuat perubahan pada komunikasi jarak jauh, pemetaan cerdas 3D,dan deteksi gambar secara real time.…”

Section: Pendahuluanunclassified

Kontrol Kestabilan Kapal Autonomous Submarine Surface Vehicle Dengan Metode Fuzzy Logic

Abyan Faruq,

Endrasmono,

Rachman

et al. 2023

infotekmesin

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The Autonomous Submarine Surface Vehicle is a type of unmanned underwater vehicle. When the ship performs maneuvers, there are large Pitch and Roll motions. This research aims to control the stability of the Autonomous Submarine Surface Vehicle with Fuzzy. The first process is taking Pitch and Roll data followed by the Fuzzification process to change input data with firm values into Fuzzy values. The next stage is Inference by using the rules (if – then) and the Deffuzification process to change the results of the inference stage into output values. The last is the process of stabilizing the ship with a Thruster dc motor. When the system is activated, it has a time of 0.518 seconds faster to steady state than the deactivated system with a roll tilt of (plus) 11°. On the roll tilt of (plus) 20° the highest PWM rotation is 1600µs with a time of 9,342 seconds to steady state and the roll tilt is (plus) 11° with the highest PWM of 1500µs with a time of 4,335 seconds. Based on this research, the Fuzzy Method can control the stability of the Autonomous Submarine Surface Vehicle ship.

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Section: Pendahuluanunclassified

Kontrol Kestabilan Kapal Autonomous Submarine Surface Vehicle Dengan Metode Fuzzy Logic

Abyan Faruq,

Endrasmono,

Rachman

et al. 2023

infotekmesin

View full text Add to dashboard Cite

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“…One of the problems of MSHIF is that the amount of data increases and the network structure becomes more complicated to increase the recognition accuracy. References [84] proposed a fusion platform, which significantly reduced the parameters of network structure and improved the speed of network learning by Mobilenet V2. Similarly, reference [85] made the learning speed of the autonomous vehicle to the data increase by 20% based on the reinforcement learning method.…”

Section: F Multi-sensor Fusion and Analysismentioning

confidence: 99%

Multi-Sensor Fusion in Automated Driving: A Survey

2020

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With the significant development of practicability in deep learning and the ultra-highspeed information transmission rate of 5G communication technology will overcome the barrier of data transmission on the Internet of Vehicles, automated driving is becoming a pivotal technology affecting the future industry. Sensors are the key to the perception of the outside world in the automated driving system and whose cooperation performance directly determines the safety of automated driving vehicles. In this survey, we mainly discuss the different strategies of multi-sensor fusion in automated driving in recent years. The performance of conventional sensors and the necessity of multi-sensor fusion are analyzed, including radar, LiDAR, camera, ultrasonic, GPS, IMU, and V2X. According to the differences in the latest studies, we divide the fusion strategies into four categories and point out some shortcomings. Sensor fusion is mainly applied for multi-target tracking and environment reconstruction. We discuss the method of establishing a motion model and data association in multi-target tracking. At the end of the paper, we analyzed the deficiencies in the current studies and put forward some suggestions for further improvement in the future. Through this investigation, we hope to analyze the current situation of multi-sensor fusion in the automated driving process and provide more efficient and reliable fusion strategies.

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“…MobileNetV2 architecture, which reduces the network computing cost to a minimum by reducing the high network size, is a deep learning architecture developed by targeting mobile or lower cost devices [49,50]. It has applications in many fields from medicine to military in the literature [51,52]. The MobileNetV2 architecture has two important features that enable it to be implemented in mobile environments.…”

Section: Mobilenetv2mentioning

confidence: 99%

Disease Detection From Cassava Leaf Images With Deep Learning Methods in Web Environment

Metlek

2021

International Journal of 3D Printing Technologies and Digital Industry

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In this article, it is aimed to classify healthy and four different plant diseases from Cassava plant leaf images. For this purpose, the "Cassava-Leaf-Disease-Classification" data set, which is an up-to-date and difficult data set published in 2020, was used. The used data set includes a total of 21,397 images consisting of healthy and four different diseases. In the study, in the MATLAB environment, the images were first subjected to the Chan-Vese (CV) Segmentation method and the area of interest was determined. Features were extracted with the ResNet 50 and MobileNetV2 deep learning architectures from the detected areas. Extracted features are classified by Support Vector Machine and K-Nearest Neighbor algorithms. The images are divided into two as training and testing according to the K-fold 5 value. The average highest success rates in training and test data were achieved by using the ResNet50 architecture and SVM classifier together, as a result of segmentation. As a result of training and testing processes, 85.4% and 84.4% success rates were obtained, respectively. At the end of the test process of the study, a trained network was obtained according to ResNet50, where the highest success rates were obtained, and MobileNetV2, another deep learning architecture used in the study. It has been compiled with MATLAB Builder NE in order to run these two networks in the web environment. Finally, it has been made available to manufacturers with a web-based embedded interface. Thus, a deep learning-based decision support system has been developed that can be easily used by all manufacturers in the web environment.

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Design and Application of an Autonomous Surface Vehicle with an AI-Based Sensing Capability

Cited by 14 publications

References 8 publications

Kontrol Kestabilan Kapal Autonomous Submarine Surface Vehicle Dengan Metode Fuzzy Logic

Kontrol Kestabilan Kapal Autonomous Submarine Surface Vehicle Dengan Metode Fuzzy Logic

Multi-Sensor Fusion in Automated Driving: A Survey

Disease Detection From Cassava Leaf Images With Deep Learning Methods in Web Environment

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