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Advanced Sensing Technologies and Intelligent Systems: Selected Papers From the 24th International Symposium on Advanced Intelligent Systems (ISIS)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 4342

Special Issue Editors

Prof. Dr. Youngchul Bae

E-Mail Website
Guest Editor
Division of Electrical and Computer Engineering, Chonnam National University, Daehak-ro 50, Yeosu 59626, Republic of Korea
Interests: intelligent system; deep learning; chaotic dynamics; nonlinear control; energy prediction; fuzzy and neural network; robot control; digital twins and CPS (cyber–physical system)
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Suk-Seung Hwang

E-Mail
Guest Editor
Department of Electronic Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 501-759, Republic of Korea
Interests: adaptive signal processing; wireless communications; location detection technology; interference cancellation; channel estimation; GPS; RFID
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will consist of selected high-quality papers from the 24th ISIS (The 24th International Symposium on Advanced Intelligent Systems), which will be held in Gwangju, Republic of Korea, from the 6th to the 9th December 2023. This international conference is designed to explore a wide variety of ideas. Contributors will be invited to submit and present papers concerning “intelligent systems and the soft computing”. Topics of selected papers will include various sensor techniques, devices, and applications for intelligent systems. These papers are subjected to peer review and are published so as to widely disseminate new research results, including developments and applications.

Topics of interest include, but are not limited to, the following:

  • Vision and sensors.
  • Sensing and communications.
  • Sensors information fusion.
  • Sensing for artificial intelligence, neural networks, neuro-fuzzy systems, chaotic systems, big data analysis, learning and adaptive systems.
  • Human–computer interaction and interface based on sensors.
  • Fault detection and diagnosis, embedded real-time systems, and intelligent transportation systems based on sensors.
  • Sensing for intelligent control and robotics, intelligent manufacturing systems, mechatronics design.

Prof. Dr. Youngchul Bae
Prof. Dr. Suk-Seung Hwang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sensors
  • intelligent system
  • robotics
  • soft computing and its applications
  • artificial intelligence

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Published Papers (3 papers)

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Research

20 pages, 10061 KiB  
Article
Enhanced Vision-Based Taillight Signal Recognition for Analyzing Forward Vehicle Behavior
by Aria Seo, Seunghyun Woo and Yunsik Son
Sensors 2024, 24(16), 5162; https://doi.org/10.3390/s24165162 - 10 Aug 2024
Cited by 1 | Viewed by 1206
Abstract
This study develops a vision-based technique for enhancing taillight recognition in autonomous vehicles, aimed at improving real-time decision making by analyzing the driving behaviors of vehicles ahead. The approach utilizes a convolutional 3D neural network (C3D) with feature simplification to classify taillight images [...] Read more.
This study develops a vision-based technique for enhancing taillight recognition in autonomous vehicles, aimed at improving real-time decision making by analyzing the driving behaviors of vehicles ahead. The approach utilizes a convolutional 3D neural network (C3D) with feature simplification to classify taillight images into eight distinct states, adapting to various environmental conditions. The problem addressed is the variability in environmental conditions that affect the performance of vision-based systems. Our objective is to improve the accuracy and generalizability of taillight signal recognition under different conditions. The methodology involves using a C3D model to analyze video sequences, capturing both spatial and temporal features. Experimental results demonstrate a significant improvement in the model′s accuracy (85.19%) and generalizability, enabling precise interpretation of preceding vehicle maneuvers. The proposed technique effectively enhances autonomous vehicle navigation and safety by ensuring reliable taillight state recognition, with potential for further improvements under nighttime and adverse weather conditions. Additionally, the system reduces latency in signal processing, ensuring faster and more reliable decision making directly on the edge devices installed within the vehicles. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies and Intelligent Systems: Selected Papers From the 24th International Symposium on Advanced Intelligent Systems (ISIS))
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17 pages, 8641 KiB  
Article
Affordable 3D Orientation Visualization Solution for Working Class Remotely Operated Vehicles (ROV)
by Mohammad Afif Kasno, Izzat Nadzmi Yahaya and Jin-Woo Jung
Sensors 2024, 24(16), 5097; https://doi.org/10.3390/s24165097 - 6 Aug 2024
Viewed by 1031
Abstract
ROV operators often encounter challenges with orientation awareness while operating underwater, primarily due to relying solely on 2D camera feeds to manually control the ROV robot arm. This limitation in underwater visibility and orientation awareness, as observed among Malaysian ROV operators, can compromise [...] Read more.
ROV operators often encounter challenges with orientation awareness while operating underwater, primarily due to relying solely on 2D camera feeds to manually control the ROV robot arm. This limitation in underwater visibility and orientation awareness, as observed among Malaysian ROV operators, can compromise the accuracy of arm placement, and pose a risk of tool damage if not handle with care. To address this, a 3D orientation monitoring system for ROVs has been developed, leveraging measurement sensors with nine degrees of freedom (DOF). These sensors capture crucial parameters such as roll, pitch, yaw, and heading, providing real-time data on the ROV’s position along the X, Y, and Z axes to ensure precise orientation. These data are then utilized to generate and process 3D imaging and develop a corresponding 3D model of the operational ROV underwater, accurately reflecting its orientation in a visual representation by using an open-source platform. Due to constraints set by an agreement with the working class ROV operators, only short-term tests (up to 1 min) could be performed at the dockyard. A video demonstration of a working class ROV replica moving and reflecting in a 3D simulation in real-time was also presented. Despite these limitations, our findings demonstrate the feasibility and potential of a cost-effective 3D orientation visualization system for working class ROVs. With mean absolute error (MAE) error less than 2%, the results align with the performance expectations of the actual working ROV. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies and Intelligent Systems: Selected Papers From the 24th International Symposium on Advanced Intelligent Systems (ISIS))
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30 pages, 6907 KiB  
Article
Research on the Multiple Small Target Detection Methodology in Remote Sensing
by Changman Zou, Wang-Su Jeon and Sang-Yong Rhee
Sensors 2024, 24(10), 3211; https://doi.org/10.3390/s24103211 - 18 May 2024
Cited by 3 | Viewed by 1443
Abstract
This study focuses on advancing the field of remote sensing image target detection, addressing challenges such as small target detection, complex background handling, and dense target distribution. We propose solutions based on enhancing the YOLOv7 algorithm. Firstly, we improve the multi-scale feature enhancement [...] Read more.
This study focuses on advancing the field of remote sensing image target detection, addressing challenges such as small target detection, complex background handling, and dense target distribution. We propose solutions based on enhancing the YOLOv7 algorithm. Firstly, we improve the multi-scale feature enhancement (MFE) method of YOLOv7, enhancing its adaptability and precision in detecting small targets and complex backgrounds. Secondly, we design a modified YOLOv7 global information DP-MLP module to effectively capture and integrate global information, thereby improving target detection accuracy and robustness, especially in handling large-scale variations and complex scenes. Lastly, we explore a semi-supervised learning model (SSLM) target detection algorithm incorporating unlabeled data, leveraging information from unlabeled data to enhance the model’s generalization ability and performance. Experimental results demonstrate that despite the outstanding performance of YOLOv7, the mean average precision (MAP) can still be improved by 1.9%. Specifically, under testing on the TGRS-HRRSD-Dataset, the MFE and DP-MLP models achieve MAP values of 93.4% and 93.1%, respectively. Across the NWPU VHR-10 dataset, the three models achieve MAP values of 93.1%, 92.1%, and 92.2%, respectively. Significant improvements are observed across various metrics compared to the original model. This study enhances the adaptability, accuracy, and generalization of remote sensing image object detection. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies and Intelligent Systems: Selected Papers From the 24th International Symposium on Advanced Intelligent Systems (ISIS))
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