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arXiv 2024年

作者： Gu, Qiwen Wang, Xufei Zhang, Fenglin Zhao, Junqiao Tao, Siyue Ye, Chen Feng, Tiantian Jiang, Changjun Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The Shanghai Research Institute for Intelligent Autonomous System Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China School of Surveying and Geo-Informatics Tongji University Shanghai China

Visual Place Recognition (VPR) aims to robustly identify locations by leveraging image retrieval based on descriptors encoded from environmental images. However, drastic appearance changes of images captured from different viewpoints at the same location pose incoherent supervision signals for descriptor learning, which severely hinder the performance of VPR. Previous work proposes classifying images based on manually defined rules or ground truth labels for viewpoints, followed by descriptor training based on the classification results. However, not all datasets have ground truth labels of viewpoints and manually defined rules may be suboptimal, leading to degraded descriptor performance. To address these challenges, we introduce the mutual learning of viewpoint self-classification and VPR. Starting from coarse classification based on geographical coordinates, we progress to finer classification of viewpoints using simple clustering techniques. The dataset is partitioned in an unsupervised manner while simultaneously training a descriptor extractor for place recognition. Experimental results show that this approach almost perfectly partitions the dataset based on viewpoints, thus achieving mutually reinforcing effects. Our method even excels state-of-the-art (SOTA) methods that partition datasets using ground truth labels. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points

arXiv

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arXiv 2025年

作者： Zhang, Qianyi Luo, Wentao Liu, Boyi Zhang, Ziyang Wang, Yaoyuan Liu, Jingtai Institute of Robotics and Automatic Information System Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin China Advanced Computing and Storage Lab Huawei 2012 Lab China Department of Electronic and Computer Engineering The Kong Kong University of Science and Technology Hong Kong

Motion planning in navigation systems is highly susceptible to upstream perceptual errors, particularly in human detection and tracking. To mitigate this issue, the concept of guidance points—a novel directional cue within a reinforcement learning-based framework—is introduced. A structured method for identifying guidance points is developed, consisting of obstacle boundary extraction, potential guidance point detection, and redundancy elimination. To integrate guidance points into the navigation pipeline, a perception-to-planning mapping strategy is proposed, unifying guidance points with other perceptual inputs and enabling the RL agent to effectively leverage the complementary relationships among raw laser data, human detection and tracking, and guidance points. Qualitative and quantitative simulations demonstrate that the proposed approach achieves the highest success rate and near-optimal travel times, greatly improving both safety and efficiency. Furthermore, real-world experiments in dynamic corridors and lobbies validate the robot’s ability to confidently navigate around obstacles and robustly avoid pedestrians. © 2025, CC BY.

关键词： Travel time

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Analysis of Deep Learning Algorithms for Prawn Aquaculture in a Challenging Environment 6

Analysis of Deep Learning Algorithms for Prawn Aquaculture i...

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6th International Conference on Applied Computational Intelligence in Information systems, ACIIS 2023

作者： Zariful, Wafiq Tashim, Najeebah Az-Zahra Lim, Tiong Hoo Basri, Aida Maryam Chuprat, Suriayati Putra, Seno Adi Liu, Pengcheng Fakhrurroja, Hanif Universiti Teknologi Brunei Electrical and Electronic Engineering Bandar Seri Begawan Brunei Universiti Teknologi Brunei School of Applied Sciences and Mathematics Bandar Seri Begawan Brunei Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Johor Bahru Malaysia Telkom Universiti School of Industrial and System Engineering Bandung Indonesia University of York Department of Computer Science York United Kingdom Telkom University Research Center for Smart Mechatronics National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798350317428

Deep Learning plays a vital role in various domains, spanning surveillance, agriculture, and healthcare. However, the current landscape lacks comprehensive comparative studies, underscoring the needs to explore various convolutional neural networks (CNNs) for aquaculture industries. This study conducts a comprehensive comparative analysis on state-of-the-art object detection models to identify the most suitable approach for detecting freshwater prawns, specifically in challenging turbid and murky water conditions as there is a lack of comparison studies for underwater images of freshwater prawns. The evaluated models, including YOLOv8, Faster R-CNN (FRCNN), Single Shot Detector (SSD), and EfficientDet, are assessed based on the following metrics namely inference time, accuracy, image cropping size, and model memory footprint. Using a dataset comprising 2976 manually collected images of freshwater prawns, augmented to a total of 7148 images, our results demonstrate that YOLOv8 outperforms its counterparts, achieving an impressive mean Average Precision (mAP) accuracy of 83.71% with an inference time of 32.9 milliseconds. This positions YOLOv8 as a dependable choice for real-time object detection in challenging underwater environments and contributes to addressing the limited research comparing YOLOv8 with other popular object detection algorithms, thereby aiding model selection for similar object detection applications. © 2023 IEEE.

关键词： Object detection

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Efficient and Resilient Placement Strategy for Electric Vehicle Charging Stations, Incorporating Renewable Energy and Storage Equipment in Distribution Networks

Efficient and Resilient Placement Strategy for Electric Vehi...

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Technological engineering and its Applications in Sustainable Development (ICTEASD), International Conference for

作者： Rakesh Kumar S. Shajathi Begam Dev Singh Tuychiev Komiljon Lazizovich S. Muthubalaji Mohammed Al-Farouni Srinivas Varanasi Department of Computer Engineering & Applications GLA University Mathura Department of Computer Science and Business System E.G.S. Pillay Engineering College Nagapattinam Tamilnadu India Mathematics Department Allenhouse Institute of Technology Kanpur Department of Marketing Tashkent State University of Economics Uzbekistan Department of Electrical and Electronics Engineering CMR College of Engineering & Technology Hyderabad Telangana India The Islamic University Najaf Iraq GRIET Hyderabad Telangana India

ISBN: (数字)9798350336474

ISBN: (纸本)9798350336481

The placement of Electric Vehicle Charging Stations (EVCS) is a significant obstacle to the widespread adoption of Electric Vehicles (EVs). However, integrating EVCS with distributed Renewable Energy (RE) sources may help mitigate the variability of RE production. This article focuses on studying a Microgrid (MG) that consists of power distribution sources such as Wind Turbines (WT) and photovoltaic (PV), as well as EVCSs and Energy Storage Structures (ESS). The analysis considers the ambiguities associated with the charging needs of electric vehicles and the production of distributed Renewable Energy Sources (RES). A comprehensive optimization model is provided for computing the best Charging Stations (CS) locations equipped with distributed electricity. This model combines the electrical network and the grid to ensure efficiency and reliability. The usage variation ratio is employed to assess the level of compatibility among the uncertain production curve of RE and the charging request curves to ascertain the suitable size of the WT and photovoltaic-producing method and energy storage system. The kernel prediction function is utilized to address the problem of excessive caution in adaptable optimizing methods. A simulation is conducted on an IEEE 33-nodal energy distributing system and a 50-nodal transit network. The findings from simulations provide evidence of the model's resilience and cost-effectiveness.

关键词： Photovoltaic systems Renewable energy sources Power distribution Production Electric vehicle charging Wind turbines Reliability

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Integrating modalities into context aware eLearning system using cloud computing

Integrating modalities into context aware eLearning system u...

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2021 International Conference of Women in Data science at Taif University, WiDSTaif 2021

作者： Zaguia, Atef Ameyed, Darine Daadaa, Yassine Computer Science Department College of Computers and Information Technology Taif University Taif Saudi Arabia System Engineering Department École de Technologie Supérieure University of Quebec Montreal Canada Riyadh Saudi Arabia

ISBN: (纸本)9781665449489

ICT and digital Technologies have influenced the everyday life and the way how people learn and work. Nowadays, there is a variety of learning systems supporting innovative digital approaches and many schools, universities and organizations worldwide are investing in building their own e-Learning platforms. However, learners' performance still a concern that requests focus for improvement. The learning processes can be adapted to suit the needs of particular users when the proposed e-learning systems include context-awareness computing. We believe that instead of designing an e-Learning environment with hidden contextual information, e.g.: user's activity, location or nearby devices, we must consider a new e-Learning application approach including context-awareness capabilities. Such a system will be more efficient and adaptive to users' needs. Existing eLearning systems suffer a lack of dynamic scalability. It's also complexes to extend, and expensive to integrate with other eLearning platforms. In particular students who have visual processing disorder or hearing impairments can manipulate digital text or use adaptive modules like Subtitles which help them in processing the information effectively. This paper focus on the development of a hybrid e-Learning cloud system that combines the National Institute of Standards and Technology (NIST) architecture and various contexts aware computing such as User context, system context, and environment context that can quickly adjust to any environment change is expected. Were viewed traditional eLearning architectures and their limitations and then proposed a hybrid cloud computing architecture to offer resources efficiently to all learning stakeholders and improve the education system quality at an affordable cost. To demonstrate the usefulness of the proposed system, we implemented and evaluated a simulation scenario. Based on our results, we highlight facts for increasing the cooperation and communication levels among le

关键词： Learning systems

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DUCG-based Spacecraft Fault Diagnosis Methodology

DUCG-based Spacecraft Fault Diagnosis Methodology

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Communication Technology and Information Technology (ICCTIT), International Conference on

作者： Hongfei Li Quanying Yao Peng Liu Zhan Zhang Rui Qiu Mingjiang Zhang Spacecraft On-orbit Operation Center Beijing Institute of Spacecraft System Engineering Beijing China Technology Department Yutong Intelligence Technology Co. Ltd Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

In order to solve the problems of intelligent fault diagnosis of spacecraft, this paper builds a diagnostic model based on Dynamic Uncertain Causality Graph (DUCG). This model solves the issues with data-driven and model-based methods, such as poor interpretability, high data dependence, and low diagnostic correctness. The DUCG methodology relies on the expertise of the domain experts to demonstrate uncertainties between the spacecraft telemetry parameters and possible faults in a graphic way. Notably, DUCG exhibits high diagnostic accuracy and interpretability, even in the absence of existing fault data. The diagnostic model developed utilizing the DUCG contains a total of 29 faults and 110 telemetry parameters. Empirical findings indicate that the model achieves a remarkable accuracy rate of 100%. The research results in this paper are of great significance for the development and promotion of spacecraft fault diagnosis technology.

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Optimization and Application of a Pathology computer Assisted Microscope (PathCAM) for Real-Time Slide Digitization and Analysis

Optimization and Application of a Pathology Computer Assiste...

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Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics

作者： Cooper, Max S. Ashman, Kimberly Maira, Cooper Halat, Shams Sholl, Andrew Wenk, Carola Fox, Sharon Summa, Brian Brown, J. Quincy Department of Biomedical Engineering Tulane University New OrleansLA70118 United States Department of Computer Science Tulane University New OrleansLA70118 United States Department of Pathology and Lab Medicine Tulane University New OrleansLA70112 United States Southeast Louisiana Veterans Healthcare System Pathology and Laboratory Medicine Service New OrleansLA70119 United States LSU Health Sciences Center Department of Pathology New OrleansLA70112 United States Delta Pathology Group Touro Infirmary New OrleansLA70115 United States

We describe the development and optimization of PathCAM, a system for real-time slide digitization during clinical glass slide review, and initial real-time "expert-in-the-loop" applications including assess... 详细信息

关键词： Real time systems

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HierArch: A Cluster-Based DNN Accelerator with Hierarchical Buses for Design Space Exploration

HierArch: A Cluster-Based DNN Accelerator with Hierarchical ...

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IEEE International SOC Conference

作者： Jyun-Siou Huang Ting-Han Chou Juin-Ming Lu Chih-Tsun Huang Jing-Jia Liou Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan Electronic and Optoelectronic System Research Lab Industrial Technology Research Institute Hsinchu Taiwan

With the increasing intensity of deep neural network (DNN) workloads, developing various hardware accelerators and their estimation tools have become vital to tackle this challenge. Modern DNN accelerators often develop a cluster that involves multiple processing arrays, therefore, the design of interconnections for connecting processing arrays has emerged as a crucial component in achieving high performance. This paper proposes a novel architecture of cluster-based accelerator architecture with high-level synthesis, enabling rapid exploration of the design space for scalable interconnection bandwidth, cluster size, and PE array size. Additionally, we leverage tag data and work modes to introduce flexibility in data communication without restricting the type of dataflow and data mapping. Through the evaluation, our proposed architecture demonstrates performance improvements ranging from 14% to 67.8% for different neural network models, achieved simply by adjusting the cluster size.

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A Cooperative Co-Evolution Algorithm with Variable-Importance Grouping for Large-Scale Optimization

A Cooperative Co-Evolution Algorithm with Variable-Importanc...

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Congress on Evolutionary Computation

作者： Yongfeng Li Yuze Zhang Lijia Ma Junkai Ji Dugang Liu Victor C. M. Leung Jianqiang Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Cooperative co-evolution (CC) is a promising direction in solving large-scale multiobjective optimization problems (LMOPs). However, most existing methods of grouping decision variables face some difficulties when searching in the huge search space. Specifically, the methods of grouping decision variables can be classified into two types, i.e., high-consumption grouping methods and non-consumption grouping methods. On the one hand, the former ones divide the decision variables into different groups based on the correlation analysis between variables, which consume much evaluation. This way may lead to premature convergence within limited computational resources. On the other hand, the later ones allocate the decision variables into sub-groups based on some metrics, e.g., order and size, which consume no evaluation while may cause the search fall into local optima. To alleviate the above issues, this paper proposes a CC-based algorithm with a variable-importance grouping (VIG) method, called VICCA. Firstly, the decision variables are classified into several subgroups according to their importance quantified by a meta-gene construction method. Secondly, a CC strategy is designed to simultaneously optimize all subgroups of decision variables formed by VIG using the differential evolution operator, which aims to accelerate the convergence speed. Thirdly, a global evolutionary strategy is proposed to optimize original decision variable space by the competitive swarm optimizer, aiming to maintain the diversity. Finally, the experiments demonstrate that our proposed VICCA has the significant advantage in solving LMOPs when compared with state-of-the-art evolutionary algorithms.

关键词： Measurement Correlation Sociology Evolutionary computation Reinforcement learning Germanium Computational efficiency

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Optical coherent dot-product chip for sophisticated deep learning regression

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Light(science & Applications) 2021年第12期10卷 2330-2341页

作者： Shaofu Xu Jing Wang Haowen Shu Zhike Zhang Sicheng Yi Bowen Bai Xingjun Wang Jianguo Liu Weiwen Zou State Key Laboratory of Advanced Optical Communication Systems and Networks Intelligent Microwave Lightwave Integration Innovation Center(imLic)Department of Electronic EngineeringShanghai Jiao Tong University800 Dongchuan RoadShanghai 200240China State Key Laboratory of Advanced Optical Communications System and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Institution of Semiconductors Chinese Academy of SciencesBeijing 100083China

Optical implementations of neural networks(ONNs)herald the next-generation high-speed and energy-efficient deep learning computing by harnessing the technical advantages of large bandwidth and high parallelism of ***,due to the problems of the incomplete numerical domain,limited hardware scale,or inadequate numerical accuracy,the majority of existing ONNs were studied for basic classification *** that regression is a fundamental form of deep learning and accounts for a large part of current artificial intelligence applications,it is necessary to master deep learning regression for further development and deployment of ***,we demonstrate a silicon-based optical coherent dot-product chip(OCDC)capable of completing deep learning regression *** OCDC adopts optical fields to carry out operations in the complete real-value domain instead of in only the positive *** reusing,a single chip conducts matrix multiplications and convolutions in neural networks of any ***,hardware deviations are compensated via in-situ backpropagation control provided the simplicity of chip ***,the OCDC meets the requirements for sophisticated regression tasks and we successfully demonstrate a representative neural network,the AUTOMAP(a cutting-edge neural network model for image reconstruction).The quality of reconstructed images by the OCDC and a 32-bit digital computer is *** the best of our knowledge,there is no precedent of performing such state-of-the-art regression tasks on ONN *** is anticipated that the OCDC can promote the novel accomplishment of ONNs in modern AI applications including autonomous driving,natural language processing,and scientific study.

关键词： neural hardware coherent

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