检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Al-Badri, Ahmed Husham Ismail, Nor Azman Al-Dulaimi, Khamael Salman, Ghalib Ahmed Salam, Md Sah Bin Haji Al-Sabaawi, Aiman School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia Computer Science Department College of Science Al-Nahrain University Baghdad Iraq School of Electrical Engineering and Robotics Queensland University of Technology BrisbaneQLD4000 Australia Department of Computer Science Middle Technical University Baghdad Iraq School of Computer Science Queensland University of Technology BrisbaneQLD4000 Australia

Weed detection methods based on Region-Convolutional Neural Network (R-CNN) frameworks still suffer from a lack of detection rate due to overlapping and occlusion leaves issues. This paper presents an Ensemble-Region Convolutional Neural Networks (E-RCNN) model of three state-of-the-art networks to detect Rumex obtusifolius L. ( R. obtu .) weeds under various condition especially overlapping. The proposed E-RCNN model is used due to its novelty of using ensemble classifiers with the combination of three extractors at its backbone. The Adaptive Non-Maximum Suppression (ANMS) is proposed with the Region Proposal Network (RPN) to enhance the detection performance of overlapping and occluded objects by overcoming the drawbacks of the conventional Non-Maximum Suppression (NMS). Three main stages are proposed as a framework to achieve the classification and detection goals, including pre-processing stage, classification stage, and detection stage. A hybrid model of three CNN extractor networks is used as the backbone in the classification stage. Thus, integrating three networks into one robust model increases the recognition capability by extracting additional useful features more efficiently than those from an individual network. For detection, RPN is used to generate multi-proposed boxes, whereas ANMS is used to select the best box that has a high score rate to match the target object. Our proposed model has trained and tested two standard benchmarking datasets of Rumex weeds under real-world data. The proposed model tested each dataset separately to evaluate the detection rate in terms of Intersection over Union (IoU). To evaluate the detection rate, AlexNet, Single-Shot Detector (SSD), DetectNet and Faster R-CNN with conventional NMS models are used to compare the results. The evaluation performances of AlexNet, SSD, DetectNet, and Faster R-CNN on the IoU metric are 0.81, 0.83, 0.84, and 0.88, respectively. The proposed model achieves the highest score in localization

关键词： Convolutional neural networks

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Dermatological Diagnostics: A Unified Deep Learning Framework for Skin Lesion and Cancer Classification

Dermatological Diagnostics: A Unified Deep Learning Framewor...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Vinod Kumar Isha Jain Durga Sritha Dongla Shivangi Rai Vani Rastogi Eva Kaushik Rohit Kaushik School of Engineering & Applied Science The George Washington University Washington D.C USA Computer Science & Engineering Department Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Computer Science & Engineering Department Gandhi Institute of Technology and Management Visakhapatnam Andhra Pradesh Data Scientist DXC Technology Noida India CS and Mathematics Department Data Analytics Program University of Illinois Springfield USA

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) for the classification of dermatological anomalies, benchmarking CNN diagnostic fidelity against dermatological expert evaluations. The study underscores the efficacy of CNN classifiers in expediting the diagnostic workflow for various cutaneous disorders. Advocating for an automated diagnostic framework, the research introduces a CNN-based system aimed at reducing human diagnostic load, accelerating diagnostic timelines, and enhancing survival outcomes. Utilizing advanced image processing algorithms and deep learning architectures, the research presents an automated classification system for skin pathologies, addressing both benign and malignant presentations. The classification matrix includes nine dermatological conditions: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, and vascular lesions. The objective is to engineer a CNN model with robust diagnostic performance across a diverse lesion dataset, ensuring accurate identification and categorization of dermatological conditions.

关键词： Deep learning Pathology Accuracy Melanoma computer architecture Benchmark testing Skin Convolutional neural networks Lesions Medical diagnosis

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Towards End-to-End Unsupervised Saliency Detection with Self-Supervised Top-Down Context

arXiv

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arXiv 2023年

作者： Song, Yicheng Gao, Shuyong Xing, Haozhe Cheng, Yiting Wang, Yan Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Keenon Robotics Co. Ltd. Shanghai China Academy for Engineering & Technology Fudan University Shanghai China

Unsupervised salient object detection aims to detect salient objects without using supervision signals eliminating the tedious task of manually labeling salient objects. To improve training efficiency, end-to-end methods for USOD have been proposed as a promising alternative. However, current solutions rely heavily on noisy handcraft labels and fail to mine rich semantic information from deep features. In this paper, we propose a self-supervised end-to-end salient object detection framework via top-down context. Specifically, motivated by contrastive learning, we exploit the self-localization from the deepest feature to construct the location maps which are then leveraged to learn the most instructive segmentation guidance. Further considering the lack of detailed information in deepest features, we exploit the detail-boosting refiner module to enrich the location labels with details. Moreover, we observe that due to lack of supervision, current unsupervised saliency models tend to detect non-salient objects that are salient in some other samples of corresponding scenarios. To address this widespread issue, we design a novel Unsupervised Non-Salient Suppression (UNSS) method developing the ability to ignore non-salient objects. Extensive experiments on benchmark datasets demonstrate that our method achieves leading performance among the recent end-to-end methods and most of the multi-stage solutions. The code is available. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Reconfigurable Intelligent Surface-Aided Joint Radar and Covert Communications: Fundamentals, Optimization, and Challenges

arXiv

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arXiv 2022年

作者： Du, Hongyang Kang, Jiawen Niyato, Dusit Zhang, Jiayi Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Future wireless communication systems will evolve toward multi-functional integrated systems to improve spectrum utilization and reduce equipment sizes. A joint radar and communication (JRC) system, which can support simultaneous information transmission and target detection, has been regarded as a promising solution for emerging applications such as autonomous vehicles. In JRC, data security and privacy protection are critical issues. Thus, we first apply covert communication into JRC and propose a joint radar and covert communication (JRCC) system to achieve high spectrum utilization and secure data transmission simultaneously. In the JRCC system, an existence of sensitive data transmission is hidden from a maliciously observant warden. However, the performance of JRCC is restricted by severe signal propagation environment and hardware devices. Fortunately, reconfigurable intelligent surfaces (RISs) can change the signal propagation smartly to improve the networks performance with low cost. We first overview fundamental concepts of JRCC and RIS and then propose the RIS-aided JRCC system design. Furthermore, both covert communication and radar performance metrics are investigated and a game theory-based covert rate optimization scheme is designed to achieve secure communication. Finally, we present several promising applications and future directions of RIS-aided JRCC systems. © 2022, CC BY.

关键词： Data transfer

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DTIAM: A unified framework for predicting drug-target interactions, binding affinities and activation/inhibition mechanisms

arXiv

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arXiv 2023年

作者： Lu, Zhangli Lei, Chuqi Wang, Kaili Qin, Libo Tang, Jing Li, Min School of Computer Science and Engineering Central South University Changsha410083 China Research Program in Systems Oncology Faculty of Medicine University of Helsinki HelsinkiFI00014 Finland

Accurate and robust prediction of drug-target interactions (DTIs) plays a vital role in drug discovery. Despite extensive efforts have been invested in predicting novel DTIs, existing approaches still suffer from insufficient labeled data and cold start problems. More importantly, there is currently a lack of studies focusing on elucidating the mechanism of action (MoA) between drugs and targets. Distinguishing the activation and inhibition mechanisms is critical and challenging in drug development. Here, we introduce a unified framework called DTIAM, which aims to predict interactions, binding affinities, and activation/inhibition mechanisms between drugs and targets. DTIAM learns drug and target representations from large amounts of label-free data through self-supervised pre-training, which accurately extracts the substructure and contextual information of drugs and targets, and thus benefits the downstream prediction based on these representations. DTIAM achieves substantial performance improvement over other state-of-the-art methods in all tasks, particularly in the cold start scenario. Moreover, independent validation demonstrates the strong generalization ability of DTIAM. All these results suggested that DTIAM can provide a practically useful tool for predicting novel DTIs and further distinguishing the MoA of candidate drugs. DTIAM, for the first time, provides a unified framework for accurate and robust prediction of drug-target interactions, binding affinities, and activation/inhibition mechanisms. Copyright © 2023, The Authors. All rights reserved.

关键词： Chemical activation

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Participatory Design and Early Deployment of DarumaTO-3 Social Robot 14th

Participatory Design and Early Deployment of DarumaTO-3 So...

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14th International Conference on Social robotics, ICSR 2022

作者： Shen, Zhihao Urano, Nanaka Chen, Chih-Pu Feng, Shi Mitchell, Scean Katagiri, Masao Du, Yegang Trevejo, Franco Pariasca Tomo, Tito P. Schmitz, Alexander Browne, Ryan Ogawa, Toshimi Taki, Yasuyuki Trovato, Gabriele Future Robotics Organization Waseda University Tokyo Japan School of International Liberal Studies Waseda University Tokyo Japan Graduate School of Science and Engineering Waseda University Tokyo Japan Pontifical Catholic University of Peru Lima Peru Smart Aging Research Center Tohoku University Miyagi Sendai Japan Innovative Global Program Shibaura Institute of Technology Tokyo Japan

ISBN: (纸本)9783031246692

With the problem of ageing population increasingly prominent, the burden of families, caregivers and medical workers to take care of older adults will be heavier. Social exclusion and cognitive dysfunctions make things worse, especially in times of a pandemic. One of the most effective approaches to solve these problems can be technology, which application is often limited by the acceptance of the end user. We introduce a social robot, DarumaTO-3, whose appearance is inspired by a traditional Buddhist and Shinto doll called Daruma, and perform a preliminary test in which we hear the response from older adults. This paper describes this new robot prototype, and reports the feedback received from the early deployment with 44 Japanese older adults. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine design

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Pallet and Pocket Detection Based on Deep Learning Techniques

Pallet and Pocket Detection Based on Deep Learning Technique...

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Iberian robotics Conference (ROBOT)

作者： Daniele Caldana Artur Cordeiro João Pedro Souza Ricardo B. Sousa Paulo M. Rebelo António Joaquim Silva Manuel F. Silva CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Portugal Faculty of Engineering University of Porto (FEUP) Portugal School of Science and Technology University of Trás-os-Montes e Alto Douro (UTAD) Portugal Seúibal Industrial Complex The Navigator Company Portugal ISEP Polytechnic of Porto Portugal

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

The high level of precision and consistency required for pallet detection in industrial environments and logistics tasks is a critical challenge that has been the subject of extensive research. This paper proposes a system for detecting pallets and its pockets using the You Only Look Once (YOLO) v8 Open Neural Network Exchange (ONNX) model, followed by the segmentation of the pallet surface. On the basis of the system a pipeline built on the ROS Action Server whose structure promotes modularity and ease of implementation of heuristics. Additionally, is presented a comparison between the YOLOv5 and YOLOv8 models in the detection task, trained with a customised dataset from a factory environment. The results demonstrate that the pipeline can consistently perform pallet and pocket detection, even when tested in the laboratory and with successive 3D pallet segmentation. When comparing the models, YOLOv8 achieved higher average metric values, with YOLOv8m providing better detection performance in the laboratory setting.

关键词： YOLO Measurement Three-dimensional displays Pipelines Neural networks Production facilities Servers Pallets Robots Logistics

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Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning

arXiv

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arXiv 2023年

作者： Li, Wenhao Qiao, Dan Wang, Baoxiang Wang, Xiangfeng Jin, Bo Zha, Hongyuan School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen518172 China School of Computer Science and Technology East China Normal University Shanghai200062 China School of Software Engineering Tongji University Shanghai200062 China

The difficulty of appropriately assigning credit is particularly heightened in cooperative MARL with sparse reward, due to the concurrent time and structural scales involved. Automatic subgoal generation (ASG) has recently emerged as a viable MARL approach inspired by utilizing sub-goals in intrinsically motivated reinforcement learning. However, end-to-end learning of complex task planning from sparse rewards without prior knowledge, undoubtedly requires massive training samples. Moreover, the diversity-promoting nature of existing ASG methods can lead to the "over-representation" of sub-goals, generating numerous spurious sub-goals of limited relevance to the actual task reward and thus decreasing the sample efficiency of the algorithm. To address this problem and inspired by the disentangled representation learning, we propose a novel "disentangled" decision-making method, Semantically Aligned task decomposition in MARL (SAMA), that prompts pretrained language models with chain-of-thought that can suggest potential goals, provide suitable goal decomposition and subgoal allocation as well as self-reflection-based replanning. Additionally, SAMA incorporates language-grounded RL to train each agent's subgoal-conditioned policy. SAMA demonstrates considerable advantages in sample efficiency compared to state-of-the-art ASG methods, as evidenced by its performance on two challenging sparse-reward tasks, Overcooked and MiniRTS. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Image Anomaly Detection

arXiv

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arXiv 2024年

作者： Lin, Yuxuan Chang, Yang Tong, Xuan Yu, Jiawen Liotta, Antonio Huang, Guofan Song, Wei Zeng, Deyu Wu, Zongze Wang, Yan Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200438 China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai200438 China Faculty of Computer Science Free University of Bozen-Bolzano Bolzano39100 Italy College of Information Technology Shanghai Ocean University Shanghai201306 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China

In the advancement of industrial informatization, unsupervised anomaly detection technology effectively overcomes the scarcity of abnormal samples and significantly enhances the automation and reliability of smart manufacturing. As an important branch, industrial image anomaly detection focuses on automatically identifying visual anomalies in industrial scenarios (such as product surface defects, assembly errors, and equipment appearance anomalies) through computer vision techniques. With the rapid development of Unsupervised industrial Image Anomaly Detection (UIAD), excellent detection performance has been achieved not only in RGB setting but also in 3D and multimodal (RGB and 3D) settings. However, existing surveys primarily focus on UIAD tasks in RGB setting, with little discussion in 3D and multimodal settings. To address this gap, this artical provides a comprehensive review of UIAD tasks in the three modal settings. Specifically, we first introduce the task concept and process of UIAD. We then overview the research on UIAD in three modal settings (RGB, 3D, and multimodal), including datasets and methods, and review multimodal feature fusion strategies in multimodal setting. Finally, we summarize the main challenges faced by UIAD tasks in the three modal settings, and offer insights into future development directions, aiming to provide researchers with a comprehensive reference and offer new perspectives for the advancement of industrial informatization. Corresponding resources are available at https://***/Sunny5250/Awesome-Multi-Setting-UIAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Smart manufacturing

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Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile Robot

arXiv

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arXiv 2022年

作者： Yuhas, Michael Easwaran, Arvind School of Computer Science and Engineering Singapore Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore

In a cyber-physical system such as an autonomous vehicle (AV), machine learning (ML) models can be used to navigate and identify objects that may interfere with the vehicle’s operation. However, ML models are unlikely to make accurate decisions when presented with data outside their training distribution. Out-of-distribution (OOD) detection can act as a safety monitor for ML models by identifying such samples at run time. However, in safety critical systems like AVs, OOD detection needs to satisfy real-time constraints in addition to functional requirements. In this demonstration, we use a mobile robot as a surrogate for an AV and use an OOD detector to identify potentially hazardous samples. The robot navigates a miniature town using image data and a YOLO object detection network. We show that our OOD detector is capable of identifying OOD images in real-time on an embedded platform concurrently performing object detection and lane following. We also show that it can be used to successfully stop the vehicle in the presence of unknown, novel samples. Copyright © 2022, The Authors. All rights reserved.

关键词： Mobile robots

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