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Artificial Intelligence and Software Modeling Approaches in Autonomous Vehicles for Safety Management: A Systematic Review

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Information (Switzerland) 2023年第10期14卷 555-555页

作者： Abbasi, Shirin Rahmani, Amir Masoud Computer Engineering Department Science and Research Branch Islamic Azad University Tehran*** Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

Autonomous vehicles (AVs) have emerged as a promising technology for enhancing road safety and mobility. However, designing AVs involves various critical aspects, such as software and system requirements, that must be carefully addressed. This paper investigates safety-aware approaches for AVs, focusing on the software and system requirements aspect. It reviews the existing methods based on software and system design and analyzes them according to their algorithms, parameters, evaluation criteria, and challenges. This paper also examines the state-of-the-art artificial intelligence-based techniques for AVs, as AI has been a crucial element in advancing this technology. This paper reveals that 63% of the reviewed studies use various AI methods, with deep learning being the most prevalent (34%). The article also identifies the current gaps and future directions for AV safety research. This paper can be a valuable reference for researchers and practitioners on AV safety. © 2023 by the authors.

关键词： Internet of things

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Research on underwater biological detection method based on multi-scale fusion and attention mechanism 4

Research on underwater biological detection method based on ...

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4th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2022

作者： Tang, Longyu Yang, Yunong Wang, Hong College of Computer and Information Science Chongqing Normal University Chongqing China Digital Education Engineering Center for Technology and Applied Research Chongqing Normal University Chongqing China Modern Education Operation Section Chongqing Normal University Chongqing China

ISBN: (纸本)9781450398343

Underwater robots are an important tool for exploiting marine resources, while underwater target detection is a key technology for marine fishing by underwater robots. For the problem that the traditional algorithm is difficult to detect small targets and multiple scales in the underwater environment, this paper proposes an underwater biological detection algorithm based on the multi-scale feature fusion and attention mechanism. First, a YOLOv5 network is constructed, and a bi-directionally weighted BiFPN feature pyramid structure is fed into the neck network so that multiscale target features can be efficiently merged. Second, a channel and spatial convolutional attention mechanism is introduced between the neck network and the prediction network in order to increase the saliency of the underwater target that needs to be detected, which effectively reduces the missed and erroneous detection of underwater targets. The experimental results show that the proposed algorithm can accurately detect multi-scale underwater targets in complex underwater environments with an average accuracy of 80.18%, which is 2.87% better than YOLOv5, and an average recall of 85.06%, which is 4.81% better than YOLOv5. © 2022 ACM.

关键词： Feature extraction

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Paper Defects Recognition Based on Deformable Convolution

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Palpu Chongi Gisul/Journal of Korea Technical Association of the Pulp and Paper Industry 2022年第2期54卷 37-50页

作者： Qu, Yun-Hui Tang, Wei Feng, Bo Computer Teaching and Research Section Xi’an Medical University China Department of Electric and Control Engineering Shaanxi University of Science & Technology China

There are some problems in traditional paper defects classification, such as the poor generalization performance, less types of recognition, and insufficient recognition accuracy. The deep learning method provides a new scheme for paper defects classification. However, convolutional neural network has strict requirements for the size of the input image. This requires that in the process of practical engineering application, for the collected paper defect images to be classified, the area containing paper defect must be segmented during preprocessing, and then the size of the paper defect area must be adjusted to meet the input requirements of the adopted classifier. To solve the above problems, the two-stage target detection network Faster R-CNN (Region-Convolutional Neural Network) was used in paper defects recognition to solve the problem of the size requirements of the input image;In addition, the deformable convolution layer was added after the traditional convolution layer to learn the characteristics of paper defects more efficiently and accurately, so as to improve the accuracy and accuracy of paper defects recognition and classification;Finally, the deformable RoI (Region-of-Interest) pooling layer was used to replace the RoI pooling layer of classic Faster R-CNN to locate and classify the paper defects area more accurately. Experiments show that the proposed algorithm has a further improvement in accuracy and scalability compared with the previous algorithm. © 2022 Korean Technical Assoc. of the Pulp and Paper Industry. All rights reserved.

关键词： Convolution

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Frequency Comb of Spectrally Pure Biphotons Using Time-Varying Cavities

Frequency Comb of Spectrally Pure Biphotons Using Time-Varyi...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Jordan A. Gaines Karthik V. Myilswamy Joseph M. Lukens Andrew M. Weiner School of Electrical and Computer Engineering and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana Research Technology Office and Quantum Collaborative Arizona State University Tempe Arizona Quantum Information Science Section Oak Ridge National Laboratory Oak Ridge Tennessee

We propose and analyze the use of linear, time-variant cavities to spectrally compress broadband frequency-correlated photon pairs into combs of spectrally pure biphotons. Our approach relies on rapid switching of inp... 详细信息

ISBN: (纸本)9798350369311

关键词： Couplings Time-frequency analysis Switches Broadband communication Electro-optical waveguides Photonics

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Through the Sci-Fi Lens: Exploring Social Media’s Influence on Body Image and Bullying Among Adolescents in Boarding Schools

Through the Sci-Fi Lens: Exploring Social Media’s Influence...

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Learning and Technology Conference (L&T)

作者： Jawahir Che Mustapha Muhammad Azmin Mohamed Ghazali Munaisyah Abdullah Nor Intan Saniah Sulaiman Hezlin Aryani Abd Rahman Informatics & Analytics Section Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur Malaysia Computer Engineering Tech. Section Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur Malaysia Software Engineering Section Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur Malaysia College of Arts and Science School of Quantitative Sciences Universiti Utara Malaysia Kedah Malaysia Centre of Statistical and Decision Science Studies Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Selangor Malaysia

This research investigates the complex relationships between social media, body image, and bullying among adolescent boarding school students, with an emphasis on the Malaysian context. Utilizing science fiction as a conceptual lens, the study presents hypothetical future scenarios to investigate how technological advancements could impact these interconnected issues. The paper seeks to provide nuanced insights into the ethical and policy implications of social media use in boarding schools by drawing on key theories such as Social Comparison Theory, Social-Ecological Model and Uses and Gratifications Theory. Through comparative analysis of hypothetical scenarios, this study aims to provide educators, policymakers, and technologists with a comprehensive understanding of the challenges and opportunities at this intersection. The ultimate objective is to contribute to the ongoing discourse concerning adolescent mental health and to propose actionable strategies for mitigating negative outcomes in the unique context of Malaysian boarding schools.

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Artificial Intelligence Scoring Algorithm for English Translation Based on DNN 2

Artificial Intelligence Scoring Algorithm for English Transl...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Priya, R. Krishna Aanandha Saravanan, K. Gadgil, Aashish A. Sabarmathi, K.R. Niranjanamurthy, M. Azarudeen, K. Department of Engineering-Electrical section College of Engineering and Technology University of Technology and Applied Sciences Shinas Oman Department of ECE Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and technology Chennai India Department of ECE KLS GOGTE institute of Technology Karnataka Belagavi India Department of Information Technology Bannari Amman Institute of Technology Tamilnadu Sathyamangalam India Department of AI and ML BMS Institute of Technology and Management Avalahalli. Karnataka Bangalore India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798350305418

With the popularity of languages and the increasing demand for language translation, the optimization of artificial intelligence scoring algorithm for English translation has become the urgent need of the translation market. Based on the deep neural network(DNN) algorithm, it continuously integrates modern technology to realize information collection, storage, analysis and application, which promotes the deep integration of the DNN algorithm in the field of intelligent identification and expands the application scope of artificial intelligence identification technology. This paper focuses on how to efficiently optimize the model of English translation artificial intelligence scoring algorithm by DNN, specifically studying the DNN model, by studying the mathematical model and algorithm flow of the DNN optimization algorithm, and the basic principle of the DNN model;Based on the DNN model, a learning ability analysis model of English translation algorithm is proposed. Experiments show that this model can optimize the artificial intelligence scoring algorithm of English translation. © 2023 IEEE.

关键词： Artificial intelligence scoring algorithm DNN English translation

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ARiViT: attention-based residual-integrated vision transformer for noisy brain medical image classification

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European Physical Journal Plus 2024年第5期139卷 440页

作者： Hameed, Madiha Zameer, Aneela Khan, Saddam Hussain Raja, Muhammad Asif Zahoor Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences (PIEAS) Islamabad Pakistan Department of Computer System Engineering The University of Engineering and Applied Science (UEAS) Swat Pakistan Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan

Brain tumor detection in medical image processing presents a formidable challenge due to the complex behavior exhibited by these tumors. Their intricate nature arises from a variety of shapes and textures within brain tumor images, exacerbated by their diverse cellular origins. This complexity is further compounded by the presence of noisy images, which add an additional layer of difficulty. The overlapping image intensities in tumor and non-tumor areas pose a significant hurdle in extracting meaningful insights from raw data using predictive models. In response to these challenges, we introduce ARiViT, a novel framework based on residual learning. ARiViT merges vision transformers, convolutions, and adversarial learning to tackle the complexities of brain tumor detection. ARiViT’s core generator integrates RiT blocks, merging residual convolutions and transformers, with residual connections, channel compression, and strategic weight sharing for enhanced capabilities and computational efficiency. ARiViT demonstrates exceptional robustness in handling noisy images and adapts well to various modality setups. Our systematic approach involves dataset splitting into training, testing, and validation sets, maintaining distributions of 80:20 and subsequently 70:30, ensuring rigorous evaluation. Additionally, our data preprocessing strategies are tailored to handle noisy and low-quality images, as well as those with unusual tumor shapes. The GAN architecture embedded in ARiViT effectively manages the complexities inherent in such image variations, ensuring reliable tumor detection even in challenging scenarios. Through exhaustive comparative evaluations, ARiViT proves its superiority over existing CNN and transformer techniques, as evidenced by both qualitative and quantitative analyses. Our efforts culminate in remarkable achievements, including an F1-score of 98.09%, establishing a pioneering solution in the demanding field of brain tumor detection. © The Author(s), under

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Enhancing Diabetic Retinopathy Screening with Sequential Deep Learning Models

Enhancing Diabetic Retinopathy Screening with Sequential Dee...

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International Conference on Image Information Processing

作者： Vankadari Mohith Kalpana Raja I. R. Oviya Department of Computer Science and Engineering (AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven USA

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

Diabetic retinopathy is a serious medical disorder that, if left untreated, can result in visual impairment or blindness. The precise and timely categorization of its severity is critical for appropriate medical management. This research describes an automated method for determining the severity of diabetic retinopathy using deep learning algorithms. The suggested methodology adopts a two-step process for identifying diabetic retinopathy and then categorizing its severity. In this technique, two pre-trained deep learning models, InceptionResNetV2 and DenseNet121, are used. For training and assessment purposes, the “APTOS 2019 Blindness Detection” dataset which consists of five severity classes is used as the foundation. To overcome class imbalance, data augmentation approaches are used to improve the model’s performance. The results of the experiment show that the strategy is effective, with an accuracy of 83.10 % and an F1 score of 82.77 %. This study contributes to the development of automated systems for assessing diabetic retinopathy, perhaps enhancing early detection and treatment.

关键词： Deep learning Training Diabetic retinopathy Visual impairment Transfer learning Blindness Information processing

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CMOS photonic integrated source of broadband polarization-entangled photons

Optica Quantum

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Optica Quantum 2024年第4期2卷 254-259页

作者： Alexander Miloshevsky Lucas M. Cohen Karthik V. Myilswamy Muneer Alshowkan Saleha Fatema Hsuan-Hao Lu Andrew M. Weiner Joseph M. Lukens Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratoryhttps://***/01qz5mb56 Oak Ridge Tennessee 37831 USA School of Electrical and Computer Engineering and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA Research Technology Office and Quantum Collaborative Arizona State University Tempe Arizona 85287 USA

We showcase a fully on-chip CMOS-fabricated silicon photonic integrated circuit employing a bidirectionally pumped microring and polarization splitter-rotators tailored for the generation of broadband (>9 THz), high-fidelity (90–98%) polarization-entangled photons. Spanning the optical C+L-band and producing over 116 frequency-bin pairs on a 38.4-GHz-spaced grid, this source is ideal for flex-grid wavelength-multiplexed entanglement distribution in multiuser networks.

关键词： Beam splitters Entanglement distribution Optical directional couplers Parametric down conversion Silicon photonics Superconducting nanowire single photon detectors

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Novel design of recurrent neural network for the dynamical of nonlinear piezoelectric cantilever mass–beam model

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European Physical Journal Plus 2024年第1期139卷 16-16页

作者： Kausar, Aneela Chang, Chuan-Yu Raja, Muhammad Asif Zahoor Zameer, Aneela Shoaib, Muhammad Graduate School of Engineering Science and Technology (Computer Science and Information Engineering) National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences Islamabad Nilore 45650 Pakistan AI Center Yuan Ze University Taoyüan 320 Taiwan

Piezoelectricity spans a wide range of fields, showcasing its practical significance. In the realm of consumer electronics, piezoelectric materials are utilized in buzzers, sensors, and actuators. These components find applications in everyday devices such as mobile phones, wearable gadgets, and home appliances. The aim of study is to present a novel design of layered recurrent neural network (LRNNs) for solving nonlinear piezoelectric cantilever mass–beam (PE-CMB) model representing the excitation frequency dynamics. The proposed PE-CMB model is mathematically represented with highly nonlinear second-order differential system. The synthetic dataset is created using Adams numerical scheme for piezoelectric cantilever beam system, and the data are randomly segmented for training samples to formulate the network and testing samples to record performance on unbiased data and validation samples for controllability of adaptive procedure of training the weights to approximate the solutions of PE-CMB with LRNNs. The efficiency and consistent performance of the LRNNs design are validated through an evaluation process encompassing measures such as achieved accuracy using mean squared error-derived cost functions, plots illustrating learning curves, adaptively controlled parameters, solution dynamics along with associated error analysis, and a statistical analysis involving distribution of error for each instances via histograms and regression metric. © 2024, The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature.

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