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Corrections to “An Autonomous Underwater Vehicle Navigation Technique for Inspection and Data Acquisition in UWSNs”

IEEE Access 2024年 12卷 136025-136025页

作者： Arif Wibisono Md. Jalil Piran Hyoung-Kyu Song Byung Moo Lee Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent Drone Sejong University Seoul South Korea Department of Computer Science and Engineering Sejong University Seoul South Korea Department of Information and Communication Engineering and Convergence Engineering for Intelligent Drone Sejong University Seoul South Korea

In the above article [1], reference 67 was retracted. As the work in this reference is no longer reliable, we are removing it from the reference list and replacing it with [2].

关键词： Autonomous underwater vehicles Navigation Data acquisition Inspection

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Solving water scarcity challenges in arid regions: A novel approach employing human-based meta-heuristics and machine learning algorithm for groundwater potential mapping

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Chemosphere 2024年 363卷 142859页

作者： Razavi-Termeh, Seyed Vahid Sadeghi-Niaraki, Abolghasem Farhangi, Farbod Khiadani, Mehdi Pirasteh, Saied Choi, Soo-Mi Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone XR Research Center Sejong University Seoul Korea Republic of Geoinformation Tech. Center of Excellence Faculty of Geodesy and Geomatics Engineering K.N. Toosi University of Technology Tehran19697 Iran School of Engineering Edith Cowan University 270 Joondalup Drive JoondalupWA6027 Australia Institute of Artificial Intelligence School of Mechanical and Electrical Engineering Shaoxing University China Department of Geotechnics and Geomatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

Addressing water scarcity challenges in arid regions is a pressing concern and demands innovative solutions for accurate groundwater potential mapping (GPM). This study presents a comprehensive evaluation of advanced modeling techniques to enhance the precision of GPM. This study, conducted in the Zayandeh Rood watershed, Iran, employed a spatial database comprising 16 influential factors on groundwater potential and data from 175 wells. This study introduced an innovative approach to GPM by enhancing the Random Forest (RF) algorithm. This enhancement involved integrating three metaheuristic algorithms inspired by human behavior: ICA (Imperialist Competitive Algorithm), TLBO (Teaching-Learning-Based Optimization), and SBO (Student Psychology Based Optimization). The modeling process used 70% training data and 30% evaluation data. Data preprocessing was performed using the multicollinearity test method and frequency ratio (FR) technique to refine the dataset. Subsequently, the GPM was generated using four distinct models, demonstrating the combined power of machine learning and human-inspired metaheuristic algorithms. The performance of the models was systematically assessed through extensive statistical analyses, including root mean squared error (RMSE), mean absolute error (MAE), area under the curve (AUC) for the receiver operating characteristic curve (ROC), Friedman tests, chi-squared tests, and Wilcoxon signed-rank tests. RF-ICA and RF-SPBO emerged as frontrunners, displaying statistically comparable accuracy and significantly outperforming RF-TLBO and the non-optimized RF model. The results of the GPM revealed the exceptional accuracy of RF-ICA, which exhibited a commanding AUC score of 0.865, underscoring its superiority in discriminating between different groundwater potential classes. RF-SPBO also displayed strong performance with an AUC of 0.842, highlighting its effectiveness in inaccurate classification. RF-TLBO and the non-optimized RF model achieved AU

关键词： Geographic information systems

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Optimal Parameter Inflation to Enhance the Availability of Single-Frequency GBAS for intelligent Air Transportation

arXiv

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arXiv 2021年

作者： Lee, Halim Pullen, Sam Lee, Jiyun Park, Byungwoon Yoon, Moonseok Seo, Jiwon School of Integrated Technology Yonsei University Incheon21983 Korea Republic of Department of Aeronautics and Astronautics Stanford University StanfordCA94305 United States Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Department of Aerospace Engineering The Department of Convergence Engineering for Intelligent Drone Sejong University Seoul05006 Korea Republic of Korea Aerospace Research Institute Daejeon34133 Korea Republic of

Ground-based Augmentation System (GBAS) augments Global Navigation Satellite Systems (GNSS) to support the precision approach and landing of aircraft. To guarantee integrity, existing single-frequency GBAS utilizes position-domain geometry screening to eliminate potentially unsafe satellite geometries by inflating one or more broadcast GBAS parameters. However, GBAS availability can be drastically impacted in low-latitude regions where severe ionospheric conditions have been observed. Thus, we developed a novel geometry-screening algorithm in this study to improve GBAS availability in low-latitude regions. Simulations demonstrate that the proposed method can provide 5–8 percentage point availability enhancement of GBAS at Galeão airport near Rio de Janeiro, Brazil, compared to existing methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Global positioning system

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Attention Field Size Alters Patterns of Population Receptive Fields in the Early Visual Cortex

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Neuroscience Bulletin 2022年第2期38卷 205-208页

作者： Bo Liu Xiaochun Wang Le Wang Qiaojun Qu Wei Zhang Bin Wang Jie Xiang Ting Yan Tianyi Yan Hui Zhang College of Medical Imaging Shanxi Medical UniversityTaiyuan 030001China Department of Radiology First Clinical Medical CollegeShanxi Medical UniversityTaiyuan 030001China College of Information and Computer Taiyuan University of TechnologyTaiyuan 030001China Intelligent Imaging Big Data and Functional Nano-imaging Engineering Research Center of Shanxi Province Shanxi Medical UniversityTaiyuan 030001China Translational Medicine Research Center Shanxi Medical UniversityTaiyuan 030001China School of Life Science Beijing Institute of TechnologyBeijing 100081China Ministry of Industry and Information Technology Key Laboratory of Convergence Medical Engineering System and Healthcare TechnologyBeijing Institute of TechnologyBeijing 100081China

Dear Editor,Visual spatial attention is a cognitive process by which prior information about the relevance of spatial locations is used to improve perceptual performance[1].Visual attention is spread across the attention field(AF),a range within visual *** AF reshapes the distribution of activity throughout the visual pathway,including the early visual cortex(EVC)and all areas along the dorsal and ventral visual cortical pathways[2,3].Importantly,the effect of attention depends not only on the spatial position of the AF but also on its size[4].

关键词： Visual Cortex visual

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A Scalable Deep Attention Mechanism of Instance Segmentation for the Investigation of Chromosome

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SLAS technology 2025年 100306页

作者： Neelam Umbreen Sara Ali Hasan Sajid Yasar Ayaz Shrooq Alsenan Yunyoung Nam So Yeon Kim Muhammad Baber Sial National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: neelamumbreen.phd@smme.edu.pk. National University of Sciences and Technology (NUST) Islamabad Pakistan Suzhou Industrial Park (SIP) Monash Research Institute of Science and Technology Monash University 8Q9C+R6W Suzhou Jiangsu China. School of Mechanical and Manufacturing Engineering (SMME) National University of Sciences and Technology (NUST) Islamabad NUST-COVENTRY Human-Robot Interaction (NC-HRI) Laboratory Pakistan Intelligent Field Robotics Laboratory (IFRL) National Center for Artificial Intelligence (NCAI) National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: sarababer@smme.nust.edu.pk. National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: hasan.sajid@smme.nust.edu.pk. Intelligent Field Robotics Laboratory (IFRL) National Center for Artificial Intelligence (NCAI) National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: yasar@smme.nust.edu.pk. Information Systems Department College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia. Electronic address: shalsenan@pnu.edu.sa. Emotional and Intelligent Child Care Convergence Research Center Soonchunhyang University Asan 31538 Korea. Electronic address: ynam@sch.ac.kr. Department of Artificial Intelligence Department of Software and Computer Engineering Ajou University. Electronic address: jebi1771@ajou.ac.kr. School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China. Electronic address: babersial@***.

Chromosome segmentation in metaphase images is a critical yet challenging task in cytogenetics and genomics due to the inherent complexity, variability in chromosome shapes, and the scarcity of high-quality annotated datasets. This study proposes a robust instance segmentation framework that integrates an automated annotation pipeline with an enhanced deep learning architecture to address these challenges. A novel dataset is introduced, comprising metaphase images and corresponding karyograms, annotated with precise instance segmentation information across 24 chromosome classes in COCO format. To overcome the labor-intensive manual annotation process, a feature-based image registration technique leveraging SIFT and homography is employed, enabling the accurate mapping of chromosomes from karyograms to metaphase images and significantly improving annotation quality and segmentation performance. The proposed framework includes a custom Mask R-CNN model enhanced with an Attention-based Feature Pyramid Network (AttFPN), spatial attention mechanisms, and a LastLevelMaxPool block for superior multi-scale feature extraction and focused attention on critical regions of the image. Experimental evaluations demonstrate the model's efficacy, achieving a mean average precision (mAP) of 0.579 at IoU = 0.50:0.95, surpassing the baseline Mask R-CNN and Mask R-CNN with AttFPN by 3.94% and 5.97% improvements in mAP and AP50, respectively. Notably, the proposed architecture excels in segmenting small and medium-sized chromosomes, addressing key limitations of existing methods. This research not only introduces a state-of-the-art segmentation framework but also provides a benchmark dataset, setting a new standard for chromosome instance segmentation in biomedical imaging. The integration of automated dataset creation with advanced model design offers a scalable and transferable solution, paving the way for tackling similar challenges in other domains of biomedical and cytogenetic imagi

关键词： Chromosome segmentation Instance segmentation, cytogenetics Mask R-CNN attention mechanism biomedical imaging multi-scale feature fusion

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Effect of deep levels on the temperature dependence of the capacitance-voltage characteristics and the effective density of NiO/Ga2O3 heterojunction diodes

SSRN

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SSRN 2024年

作者： Senouci, Yasmine Sengouga, Nouredine Almalki, Abdulaziz Henini, Mohamed Qin, Yuan Zhang, Yuhao Labed, Madani University of Biskra Biskra07000 Algeria School of Physics and Astronomy University of Nottingham NottinghamNG7 2RD United Kingdom Physics Department Faculty of Science Taibah University-Yanbu King Khalid Rd. Al Amoedi 46423 Yanbu El-Bahr51000 Saudi Arabia Center for Power Electronics Systems Virginia Polytechnic Institute State University BlacksburgVA24060 United States Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent Drone Sejong University Seoul05006 Korea Republic of

This work investigates the temperature-dependent capacitance-voltage (C-V) characteristics of NiO/Ga2O3 heterojunction diodes (HJDs) before and after thermal annealing. The voltage barrier (VB) and the effective doping density (Neff) are extracted from these characteristics. Neff showed a decrease with increasing temperature, which is an unusual behaviour related to deep defects in Ga2O3. Deep levels revealed by deep level transient spectroscopy (DLTS) and Laplace-DLTS (LDLTS) measurements were exploited to perform simulation using SCAPS. First, an ideal NiO/Ga2O3 HJD is considered, then the defects of the fresh and annealed samples detected by DLTS and LDLTS are introduced. The results confirmed the effect of deep levels and have shown a decrease in the effective doping density of the fresh and annealed samples compared to the ideal NiO/Ga2O3 *** addition, the plots of the band diagram evolution with temperature have provided further insight explanation about this phenomenon. © 2024, The Authors. All rights reserved.

关键词： Nickel oxide

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Effect of Biologically Synthesized Iron-Oxide Nanoparticle Insulating Layer on Current-Voltage Characteristics of Ni/Cr/Ni/N-Gan Schottky Junction

SSRN

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SSRN 2023年

作者： Manjunath, V. Mathakala, Vani Prasad, C. Venkata Alhammadi, Salh Ghfar, Ayman A. Srihari, N.V. Dastagiri, S. Reddy, B. Purusottam Park, Si-Hyun Devi, P. Uma Maheswari Yeungnam University Korea Republic of Department of Applied Microbiology Sri Padmavati Mahila Viswavidyalayam A.P. Tirupati India Department of Electronic Engineering Yeungnam University 280 Daehak-ro Gyongsanbuk-do Gyeongsan-si38541 Korea Republic of Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent Drone Sejong University Seoul05006 Korea Republic of School of Chemical Engineering Yeungnam University 280 Daehak-ro Gyongsanbuk-do Gyeongsan-si38541 Korea Republic of Department of Chemistry College of Science King Saud University P.O.Box 2455 Riyadh11451 Saudi Arabia Department of Physics TRR Government Degree College A.P Kandukur India Department of Physics Sri Krishnadevaraya University A.P Ananthapuramu India

The microstructural and electrical-properties of a prepared Ni/Cr/Ni/FeO/n-GaN metal/oxide/semiconductor (MOS) heterojunction (HJ) (as-deposited and after 600 oC annealing) with biologically synthesized iron oxide (FeO) nanoparticles as an insulating-layer are investigated. The formation of FeO films at the junction is confirmed by FESEM and XRD analysis. The HJ results are associated with Ni/Cr/Ni/n-GaN results. The as-deposited and 600 oC annealed HJs exhibit exceptional rectifying efficiency and low reverse-leakage current than SJ. The annealed HJ had a higher Φb than both as-deposited HJ and SJ. Furthermore, different methods are used to analyze the Φb, RS, and n, and the results indicate consistency and validity. The HJs NSS value is lower than SJ. A transition from PFE at low voltages to SE at high-voltages was seen in SJ. Furthermore, the HJ showed SE. This leads to the conclusion that the synthesized FeO layer is a suitable material for designing novel electronic device applications. © 2023, The Authors. All rights reserved.

关键词： Iron oxides

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Gate-Dependent Rectification and Broadband Photoresponse in a P-N Diode with Van Der Waals Heterostructure

SSRN

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SSRN 2023年

作者： Elahi, Ehsan Nisar, Sobia Rabeel, Muhammad Al-Kahtani, Abdullah A. Iqbal, Muhammad Waqas Irfan, Muhammad Abubakr, Muhammad Aziz, Jamal Dastgeer, Ghulam Department of Physics & Astronomy Sejong University 209 Neungdong-ro Gwangjin-Gu Seoul05006 Korea Republic of Department of Electrical Engineering Sejong University 209 Neungdong-ro Gwangjin-gu Seoul05006 Korea Republic of Department of Convergence Engineering for Intelligent Drone Sejong University Seoul05006 Korea Republic of Chemistry Department Collage of Science King Saud University P. O. Box 2455 Riyadh11451 Saudi Arabia Department of Physics Riphah International University 13 Raiwind Road Lahore Pakistan Department of Electrical Engineering University of Engineering and Technology Faisalabad Pakistan Graduate School of Optical Engineering Sejong University Seoul05006 Korea Republic of School of Electrical Information and Media Engineering Bergische Universität Wuppertal Lise-Meitner-Straße 13 Wuppertal42119 Germany

Two-dimensional van der Waals (vdW) heterostructures possess electrical and optoelectronic properties that offer a promising framework for the development of advanced nanoscale electronic devices. To achieve top-notch integrated optoelectronics, it is imperative to have exceptional p-n heterojunctions comprising two-dimensional atomic layers. This article describes the vdW’s stacking of layers WSe2 and SnS2 to form a p-n junction diode over the Si/SiO2 substrates. The electrical characterizations of the WSe2/SnS2 vdW heterostructure demonstrate ultra-low dark currents due to the depletion area at the junction and strong direct passage of current when lit. The charge transport mechanism is also studied and explained via an energy band diagram. Effectively sweeping the back-gate voltage yields a rectification ratio (R.R) of 2.9x104. Under the irradiation of light with various wavelengths (λ), the WSe2/SnS2 heterostructure's photovoltaic properties have been investigated. A 36 nA variation in photocurrent is observed after illuminating the WSe2/SnS2 vdW heterostructure under the light of wavelength 220 nm. The device indicated a responsivity (R) of 7.3x104 m AW-1, with a significant EQE of 4.11x104 (%), and a detectivity (D*) of about 3.13x1010 Jones under light irradiation at λ=220 nm. The development of such type of high-quality p-n junction makes the fabrication of efficient optoelectronic devices and logic possible. © 2023, The Authors. All rights reserved.

关键词： Heterojunctions

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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

引用

2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 IEEE.

关键词： Data acquisition

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MuTCELM: An optimal multi-TextCNN-based ensemble learning for text classification

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Heliyon 2024年第19期10卷 e38515页

作者： Agbesi, Victor Kwaku Chen, Wenyu Yussif, Sophyani Banaamwini Ukwuoma, Chiagoziem C. Gu, Yeong Hyeon Al-antari, Mugahed A. School of Computer Science and Engineering University of Electronic Science and Technology of China No. 2006 Xiyuan Ave West Hi-Tech Zone Sichuan Chengdu China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology No. 610059 Sichuan Chengdu China Department of Artificial Intelligence and Data Science College of AI Convergence Daeyang AI Center Sejong University Seoul 05006 South Korea

Feature extraction plays a critical role in text classification, as it converts textual data into numerical representations suitable for machine learning models. A key challenge lies in effectively capturing both semantic and contextual information from text at various levels of granularity while avoiding overfitting. Prior methods have often demonstrated suboptimal performance, largely due to the limitations of the feature extraction techniques employed. To address these challenges, this study introduces Multi-TextCNN, an advanced feature extractor designed to capture essential textual information across multiple levels of granularity. Multi-TextCNN is integrated into a proposed classification model named MuTCELM, which aims to enhance text classification performance. The proposed MuTCELM leverages five distinct sub-classifiers, each designed to capture different linguistic features from the text data. These sub-classifiers are integrated into an ensemble framework, enhancing the overall model performance by combining their complementary strengths. Empirical results indicate that MuTCELM achieves average improvements across all datasets in accuracy, precision, recall, and F1-macro scores by 0.2584, 0.2546, 0.2668, and 0.2612, respectively, demonstrating significant performance gains over baseline models. These findings underscore the effectiveness of Multi-TextCNN in improving model performance relative to other ensemble methods. Further analysis reveals that the non-overlapping confidence intervals between MuTCELM and baseline models indicate statistically significant differences, suggesting that the observed performance improvements of MuTCELM are not attributable to random chance but are indeed statistically meaningful. This evidence indicates the robustness and superiority of MuTCELM across various languages and text classification tasks. © 2024 The Author(s)

关键词： Deep learning Ensemble learning Feature extraction Multi-TextCNN Text classification

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