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arXiv 2024年

作者： Bansal, Ayoosh Wang, Duo Yeghiazaryan, Mikael Li, Yangge Tao, Chuyuan Yoon, Hyung-Jin Arora, Prateek Papachristos, Christos Voulgaris, Petros Mitra, Sayan Sha, Lui Hovakimyan, Naira University of Illinois at Urbana-Champaign Illinois Urbana61801 United States University of Nevada RenoNV89557 United States Tennessee Technological University CookevilleTN38505 United States Postdoctoral Research Associate Computer Science 201 North Goodwin Avenue UrbanaIL61801 United States Mechanical Engineering Department 1664 N. Virginia Street RenoNV89557 United States UIUC Mechanical Science & Engineering Department 105 S Mathews Ave UrbanaIL61801 United States Graduate Student Electrical and Computer Engineering 306 N. Wright St. UrbanaIL61801 United States Mechanical Science & Engineering Department 105 S Mathews Ave UrbanaIL61801 United States Mechanical Engineering Department 1 William L Jones Dr CookevilleTN38505 United States Department of Computer Science & Engineering 1664 N. Virginia Street RenoNV89557 United States Department of Computer Science & Engineering 1664 N. Virginia Street RenoNV89557 United States Mechanical Engineering Department 1664 N. Virginia Street RenoNV89557 United States Electrical and Computer Engineering 306 N. Wright St. UrbanaIL61801 United States Donald B. Gillies Chair in Computer Science Computer Science 201 North Goodwin Avenue UrbanaIL61801 United States Mechanical Science & Engineering Department 105 S Mathews Ave UrbanaIL61801 United States

Autonomous air taxis are poised to revolutionize urban mass transportation. A key challenge inhibiting their adoption is ensuring the safety and reliability of the autonomy solutions that will control these vehicles. Validating these solutions on full-scale air taxis in the real world presents complexities, risks, and costs that further convolute the challenge of ensuring safety and reliability of these autonomous vehicles. Verification and Validation (V&V) frameworks play a crucial role in the design and development of highly reliable systems by formally verifying safety properties and validating algorithm behavior across diverse operational scenarios. Advancements in high-fidelity simulators have significantly enhanced their capability to emulate real-world conditions, encouraging their use for validating autonomous air taxi solutions, especially during early development stages. This evolution underscores the growing importance of simulation environments, not only as complementary tools to real-world testing but as essential platforms for evaluating algorithms in a controlled, reproducible, and scalable manner. This work presents a V&V framework for a vision-based landing system for air taxis with vertical take-off and landing (VTOL) capabilities. Specifically, we use Verse, a tool for formal verification, to model and verify the safety of the system by obtaining and analyzing the reachable sets. To conduct this analysis, we utilize a photorealistic simulation environment. The simulation environment, built on Unreal Engine, provides realistic terrain, weather, and sensor characteristics to emulate real-world conditions with high fidelity. To validate the safety analysis results, we conduct extensive scenario-based testing to assess the reachability set and robustness of the landing algorithm in various conditions. This approach showcases the representativeness of high-fidelity simulators, offering an effective means to analyze and refine algorithms before real-wor

关键词： Mass transportation

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Deep learning-based detection of incisal translucency patterns

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Journal of Prosthetic Dentistry 2025年

作者： Shetty, Sthithika Gali, Sivaranjani R, Venkatesh MS, Jeswin MN, Thippeswamy Postgraduate student Department of Prosthodontics and Crown & Bridge Faculty of Dental Sciences M.S. Ramaiah University of Applied Sciences Bangalore India Head Department of Prosthodontics and Crown & Bridge Faculty of Dental Sciences M.S. Ramaiah University of Applied Sciences Bangalore India Graduate student Department of Computer Science & Engineering (Artificial Intelligence & Machine Learning) Ramaiah Institute of Technology Bangalore India Principal Dr. Ambedkar Institute of Technology Bangalore India

Statement of problem: The evaluation of incisal translucency in anterior teeth greatly influences esthetic treatment outcomes. This evaluation is mostly subjective and often overlooked among dental professionals. The application of artificial intelligence-based models to detect the incisal translucency of anterior teeth may be of value to dentists in their restorative dental practice, but studies are lacking. Purpose: The purpose of this study was to assess the accuracy of deep learning models in predicting the translucency patterns of anterior teeth. Material and methods: Approximately 240 Joint Photographic Experts Group (JPEG) images of anterior teeth from participants over 18 years were collected using a smartphone. These images were resized to 224×224 pixels and classified by the presence or absence of translucency. Augmentation techniques enhanced the training dataset, and a 3-model deep learning approach was used: YOLOv5 detected central incisors, Vision Transformers (ViT) identified translucency, and U-Net segmented the translucent areas. The images were split 80 to 20 for training and testing, with performance evaluated using accuracy, precision, recall, F1 score, confusion matrix, and dice scores. Results: YOLOv5 achieved a precision of 1.00 at a confidence threshold of 0.910. The ViT system showed an accuracy of 91.66%, with 58 of 64 images predicting correctly with an F1 score of 94.83%. U-Net segmentation after training with annotated images achieved an accuracy of 91% with a dice score of 0.948. Conclusions: The integration of YOLOv5 for detection, ViT for classification, and U-Net for segmentation demonstrates a comprehensive approach to addressing the classification of incisal translucencies. By leveraging the strengths of deep learning models, high accuracy and precision can be achieved in detecting the incisal translucency patterns of anterior teeth. © 2025 Editorial Council for The Journal of Prosthetic Dentistry

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Multi-core synthesis and maximum satisfiability applied to optimal sizing of solar photovoltaic systems

TechRxiv

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TechRxiv 2021年

作者： Trindade, Alessandro Galvão, Edilson Cordeiro, Lucas The Post-graduate Program in Electrical Engineering Manaus Brazil Department of Electricity Federal University of Amazonas Manaus Brazil School of Computer Science The University of Manchester United Kingdom

Annual global energy consumption growth is around 1.3% with forecasts until 2040. Photovoltaic systems became a suitable alternative to nuclear and fossil energy generation. In order to support this technology’s dissemination, we develop and evaluate an automated formal synthesis approach that assists in decision-making for off-grid systems. Our proposed approach, called PVz, is based on a variant of the counterexample-guided inductive synthesis;it has a multi-core feature, which can obtain the optimal sizing of photovoltaic systems focusing on Life Cycle Cost analysis. Given the electrical needs of a home, we seek a set of electrical equipment with the best possible combination of devices that meet the specified requirements. We calculate all costs related to maintenance over 20 years. The results presented are based on seven case studies;some of them are real ones from the Amazon region in Brazil. The same case studies were solved by a commercial optimization tool. Our technique and the commercial tool results were validated with popular simulation software to perform a fair comparison. Furthermore, we analyze some topics such as run-time, optimal solution, and configuration of the resulting systems. We claim that our technique is advantageous compared to the existing approaches in the literature. © 2021, CC BY-NC-SA.

关键词： Decision making

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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

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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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Machine learning and deep learning based psoriasis recognition system: evaluation, management, prognosis—where we are and the way to the future

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Artificial Intelligence Review 2025年第9期58卷

作者： Shehzadi, Nimra Rehman, Arshia Naz, Saeeda Rehman, Saba Khalifa, Fahmi Department of Computer Science Govt Girls Post Graduate College No.1 Abbottabad Pakistan Department of Computer Science Comsats University of Islamabad Abbottabad Pakistan Department of Psychology COMSATS University of Islamabad Abbottabad Pakistan Electrical and Computer Engineering Department School of Engineering Morgan State University BaltimoreMD21251 United States

Psoriasis, a chronic inflammatory skin disease, poses significant diagnostic challenges due to its heterogeneous clinical manifestations and overlap with other dermatological conditions. The inconsistencies in diagnostic standards and variability in disease presentation further exacerbate the controversy surrounding the diagnosis of psoriasis. Recent advances in artificial intelligence (AI), particularly deep learning (DL) techniques such as convolutional neural networks (CNNs) and transformers, have shown promising results in improving recognition of psoriasis. This comprehensive review aims to provide an in-depth examination of the current state of AI applications in the diagnosis of psoriasis, highlighting the strengths and limitations of existing approaches. We evaluate the performance of various AI models on publicly available datasets, including the Psoriasis Image Dataset (PID) and the International Skin Imaging Collaboration (ISIC) dataset, and compare their accuracy, sensitivity, and specificity with traditional diagnostic methods. Furthermore, we discuss the challenges and future directions of AI-powered psoriasis diagnosis, including the need for large, diverse datasets, addressing data quality and generalization issues, and developing explainable AI models. Our review highlights the potential of AI to revolutionize the diagnosis and management of psoriasis and provides a pathway for future research in this field. © The Author(s) 2025.

关键词： Convolutional neural networks

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Effect of geometric heterogeneity using an auxiliary device on the accuracy of complete arch implant scanning: An in vitro study of different clinical simulations

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Journal of Prosthetic Dentistry 2023年

作者： Abdelrehim, Aly Sulaiman, Eshamsul Sofian, Hazrina Salleh, Nosizana Mohd Graduate student Department of Restorative Dentistry Faculty of Dentistry Universiti Malaya (UM) Kuala Lumpur Malaysia Associate Professor Department of Restorative Dentistry Faculty of Dentistry Universiti Malaya (UM) Kuala Lumpur Malaysia Senior Lecturer Department of Software Engineering Faculty of Computer Science & Information Technology Universiti Malaya (UM) Kuala Lumpur Malaysia

Statement of problem: Intraoral scanning of implants supporting complete arch prostheses is limited because of the lack of geometric heterogeneity and unique reference points, creating inherent errors in the image stitching process by the scanner software program. Purpose: The purpose of this in vitro study was to evaluate the significance of geometric heterogeneity on complete arch implant scanning by using a novel auxiliary geometric device. Three different clinical simulations were tested to assess its significance. The study also assessed whether scans produced using the auxiliary device would meet a clinically acceptable threshold. Material and methods: A total of 60 scans (n=20) were performed using an intraoral scanner in 3 different clinical simulations: 2 parallel implants, 4 parallel implants, and 4 implants with a 30-degree posterior angulation of the distal implants. Scanning alternated between using the auxiliary geometric scanning device (test groups;4IP+, 4IA+, 2IP+) and not using the device (control groups;4IP-, 4IA-, 2IP-). A reference scan for each model was prepared from a high precision laboratory scanner. The scans were analyzed for accuracy in 3-dimensional deviation, interimplant distance deviation, and angular deviation by using an inspection software program. The effect of the auxiliary device was statistically analyzed by comparing scans of the same group using the paired t test for normally distributed data and the Wilcoxon Signed Rank test when data were not normally distributed (α=.05). Results: Significant effects of the auxiliary geometric device were found in 3-dimensional, distance and angular deviations (P<.05). Scans performed using the device were significantly more accurate in most implant positions (P<.05). Linear and angular deviations were clinically acceptable for all test groups. However, the deviations were above the clinically acceptable threshold for the control groups. Conclusions: Using an auxiliary geometric device sig

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Example based machine translation using fuzzy logic from English to Hindi

Example based machine translation using fuzzy logic from Eng...

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2015 International Conference on Artificial Intelligence, ICAI 2015 - WORLDCOMP 2015

作者： Rana, Manish Atique, Mohammad Post Graduate Department of Computer Science and Engineering Sant Gadge Baba Amravati University Amravati India Post Graduate Department of Computer Science Sant Gadge Baba Amravati University Amravati India

ISBN: (纸本)1601324073

Example based machine translation is one of the approaches in machine translation. The concept uses the corpus of two languages and then translates the input text to desired target text by proper matching. The different languages have different language structure of the subject-object-verb (SOV) alignment. The matching is then arranged to give proper meaning in target text language and to form proper structure. This paper, describes the Example Based Machine Translation using Natural Language Processing demo. The proposed EBMT framework can be used for automatic translation of text by reusing the examples of previous translations through the use of Fuzzy which is proposed work. This framework comprises of three phases, matching, alignment and recombination. Same type of machine translation is possible through use of soft computing tool (Fuzzy Logic). © 2019 ICAI 2015 - WORLDCOMP 2015. All rights reserved.

关键词： Learning algorithms

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Application of Internet of Things and Machine learning in improving supply chain financial risk management System

Application of Internet of Things and Machine learning in im...

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Industrial Electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： Kafila Nalla Bala Kalyan Kamal Ahmad Fakhruddin Rahi Chetan Shelke S Mahabub Basha School of Business SR University Warangal Telangana Department of Management Studies Sri Venkateswara College of Engineering (Autonomous) Tirupati A.P Department of Commerce HNB Garhwal (A Central University) Srinagar Garhwal Uttarakhand Department of Commerce Government Post Graduate College Bageshwar Uttarakhand Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Central Campus Chikkahagade Cross Chandapura Anekal Main Road Bengaluru Karnataka India Department of Commerce International Institute of Business Studies Bangalore India

The incorporation of the Internet of Things (IoT) and machine learning (ML) methods has attracted significant interest in a variety of sectors in previous years. This article investigates the use of IoT and ML to improve supply chain financial (SCF) threat management. The supply chain is a complicated network with many key players, and financial threat management is important to its sustainability and success. IoT and ML possess inherent advantages in SCF due to their technology properties. They also have significant opportunities to build trust to solve big challenges in SCF, which aids financial development in the Tonkin Gulf area. This paper focuses on introducing the study on using Machine learning innovation in SCF in the Tonkin Gulf area and aims to offer suggestions on how supply chain finance could evolve there using Machine learning. This paper suggests supply chain finance game applications for pertinent investigations as well as blockchain innovation, supply chain banking threats assessment on the IoT and machine learning, and supply chain finance implementation study methodologies in the Tonkin Gulf region.

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Model-centric volumetric point cloud attributes

arXiv

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arXiv 2021年

作者： de Queiroz, Ricardo L. Dorea, Camilo Freitas, Davi R. Krivokuća, Maja Sandri, Gustavo P. Computer Science Department Universidade de Brasilia Brasilia Brazil Post-Graduate Program at the Electrical Engineering Science Department Universidade de Brasilia Brasilia Brazil INRIA Rennes France Federal Institute of Brasilia Brasilia Brazil

Point clouds have recently gained interest, especially for real-time applications and for 3D-scanned material, such as is used in autonomous driving, architecture, and engineering, to model real estate for renovation or display. Point clouds are associated with geometry information and attributes such as color. Be the color unique or direction-dependent (in the case of plenoptic point clouds), it reflects the colors observed by cameras displaced around the object. Hence, not only are the viewing references assumed, but the illumination spectrum and illumination geometry is also implicit. We propose a model-centric description of the 3D object, that is independent of the illumination and of the position of the cameras. We want to be able to describe the objects themselves such that, at a later stage, the rendering of the model may decide where to place illumination, from which it may calculate the image viewed by a given camera. We want to be able to describe transparent or translucid objects, mirrors, fishbowls, fog and smoke. Volumetric clouds may allow us to describe the air, however "empty", and introduce air particles, in a manner independent of the viewer position. For that, we rely on some eletromagnetic properties to arrive at seven attributes per voxel that would describe the material and its color or transparency. Three attributes are for the transmissivity of each color, three are for the attenuation of each color, and another attribute is for diffuseness. These attributes give information about the object to the renderer, with whom lies the decision on how to render and depict each object. © 2021, CC BY.

关键词： Color

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Dynamic Hard Timeout based Flow Table Management in Openflow enabled SDN

Dynamic Hard Timeout based Flow Table Management in Openflow...

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2019 International Conference on Vision Towards Emerging Trends in Communication and Networking, ViTECoN 2019

作者： Panda, Abinas Samal, Siddharth Shankar Turuk, Ashok Kumar Panda, Aliva Venkatesh, Vummadi Chetty National Institute of Technology Computer Science and Engineering Rourkela India Center of Advanced Post Graduate Studies BPUT Computer Science and Engineering Rourkela India

ISBN: (纸本)9781538693537

Software Defined Networking is the innovation in the network which allows us to manage the network dynamically in an efficient way. Ternary Content Addressable Memory (TCAM) is being used by the OpenFlow switches because it is faster look-up. However, it is very costly, and also the number of flow entries accommodated in it is less due to its limited size. So to take full advantage of the TCAM, we need to maximize the flow table utilization. To improve the TCAM allocation, This paper emphasizes managing the Hard Timeout of the flow table entries. We are proposing a Dynamic way to allocate the flow entry timeout so that the flow table can be used optimally by assigning different dynamic timeout considering predictable and unpredictable flows. The results show that Dynamically allotted hard timeout along with LRU as the eviction method performs better than the Statically allotted hard timeout along with LRU. © 2019 IEEE.

关键词： Logic gates

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