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Method for Image-Based Preliminary Assessment of Car Park for the Disabled and the Elderly Using Convolutional Neural Networks and Transfer Learning 1

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15th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2022

作者： Hanpinitsak, Panawit Posawang, Pitiphum Phankaweerat, Sumate Pattara-atikom, Wasan Department of Computer Engineering Khon Kaen University Khon Kaen40002 Thailand Intelligent Transportation Systems Laboratory National Electronics and Computer Technology National Science and Technology Development Agency Pathumthani12120 Thailand Department of Computer Science Vongchavalitkul University Nakhon Ratchasima30000 Thailand

ISBN: (数字)9783031209925

ISBN: (纸本)9783031209918

It is critical to assess the standards of disabled facilities in order to ensure the comfort and safety of disabled individuals who use them. In this study, deep convolutional neural networks (CNNs) with multi-label classification capability are employed for a preliminary evaluation of the car park for the disabled and the elderly in accordance with ministerial regulations, reducing the burden of on-site inspection by specialists. Using a transfer learning technique, the weights of an Inception-V3, Xception, and EfficientNet-B2 architectures previously trained on the ImageNet dataset were updated with the disabled car park image dataset. We used 4,812 training images and 355 test images to train, evaluate, and compare the model. The results revealed that the EfficientNet-B2 model yielded the best performance for 5 out of 6 classes, with the F1-score between 79.8% and 95.6%. In contrast, the remaining one class was best predicted by the Xception model, where the F1-score was 83.33%. This implies that it is possible to apply CNNs to aid in the evaluation of handicap facilities. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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Automatic recognition of tweek atmospherics and plasma diagnostics in the lower ionosphere with the machine learning method

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Earth and Planetary Physics 2023年第3期7卷 407-413页

作者： Mao Zhang GaoPeng Lu HaiLiang Huang ZhengWei Cheng YaZhou Chen Steven A.Cummer JiaYi Zheng JiuHou Lei School of Earth and Space Sciences University of Science and Technology of ChinaHefei 230026China Key Laboratory of Atmospheric Optics Anhui Institute of Optics and Fine MechanicsHefei Institutes of Physical ScienceChinese Academy of SciencesHefei 230031China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China National Key Laboratory on Electromagnetic Environment Effects Army Engineering UniversityShijiazhuang CampusShijiazhuang 050003China Electrical and Computer Engineering Department Duke UniversityDurhamNC 27708USA

Tweek atmospherics are extremely low frequency and very low frequency pulse signals with frequency dispersion characteristics that originate from lightning discharges and that propagate in the Earth–ionosphere waveguide over long *** this study,we developed an automatic method to recognize tweek atmospherics and diagnose the lower ionosphere based on the machine learning *** differences(automatic−manual)in each ionosphere parameter between the automatic method and the manual method were−0.07±2.73 km,0.03±0.92 cm^(−3),and 91±1,068 km for the ionospheric reflection height(h),equivalent electron densities at reflection heights(Ne),and propagation distance(d),***,the automatic method is capable of recognizing higher harmonic tweek *** evaluation results of the model suggest that the automatic method is a powerful tool for investigating the long-term variations in the lower ionosphere.

关键词： machine learning method tweek atmospherics reflection height D-region ionosphere

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Energy Efficiency Prediction Model of Suction Hopper Dredger Based on Correlation Analysis and Convolutional Neural Network 20

Energy Efficiency Prediction Model of Suction Hopper Dredger...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Zhang, Jinyue Chen, Liheng Qin, Daqing Harbin Engineering University College of Intelligent Systems Science and Engineering Harbin150001 China University of Macau Faculty of Science and Technology Department of Electromechanical Engineering Macau China State Key Laboratory of Hydro-power Equipment Harbin150040 China Harbin Electric Machinery Company Limited Harbin150040 China

ISBN: (纸本)9798350320831

Suction hopper dredgers are the main type of ship used in dredging projects. Due to the complex dynamic characteristics of the dredging process and the fact that the power consumption characteristics of dredgers are affected by many factors such as soil, meteorological environment, and hydrology, it is difficult to obtain accurate prediction results using traditional methods. This paper proposes a data extraction method based on correlation analysis to filter out the most critical influencing features from a large amount of data that affects the working efficiency of dredgers. An energy efficiency prediction model is established using convolutional neural network method to estimate the actual production efficiency of bucket-wheel dredgers and applied to actual production cases. The results show that the energy efficiency prediction model established using this method can effectively predict the power consumption of suction hopper dredgers. © 2023 IEEE.

关键词： Energy efficiency

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Domain knowledge discovery from abstracts of scientific literature on Nickel-based single crystal superalloys

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science China(Technological sciences) 2023年第6期66卷 1815-1830页

作者： LIU Yue DING Lin YANG ZhengWei GE XianYuan LIU DaHui LIU Wei YU Tao AVDEEV Maxim SHI SiQi School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Shanghai Engineering Research Center of Intelligent Computing System Shanghai 200444China State Key Laboratory of Advanced Special Steel&Shanghai Key Laboratory of Advanced Ferrometallurgy&School of Materials Science and Engineering Shanghai UniversityShanghai 200444China Materials Genome Institute Shanghai UniversityShanghai 200444China Institute of Functional Materials Central Iron&Steel Research InstituteBeijing 100081China Australian Nuclear Science and Technology Organisation Locked Bag 2001Kirrawee DC NSW 2232Australia School of Chemistry The University of SydneySydney 2006Australia Zhejiang Laboratory Hangzhou 311100China

Despite the huge accumulation of scientific literature,it is inefficient and laborious to manually search it for useful information to investigate structure-activity ***,we propose an efficient text-mining framework for the discovery of credible and valuable domain knowledge from abstracts of scientific literature focusing on Nickel-based single crystal ***,the credibility of abstracts is quantified in terms of source timeliness,publication authority and author’s academic ***,eight entity types and domain dictionaries describing Nickel-based single crystal superalloys are predefined to realize the named entity recognition from the abstracts,achieving an accuracy of 85.10%.Thirdly,by formulating 12 naming rules for the alloy brands derived from the recognized entities,we extract the target entities and refine them as domain knowledge through the credibility *** this,we also map out the academic cooperative“Author-Literature-Institute”network,characterize the generations of Nickel-based single crystal superalloys,as well as obtain the fractions of the most important chemical elements in *** extracted rich and diverse knowledge of Nickel-based single crystal superalloys provides important insights toward understanding the structure-activity relationships for Nickel-based single crystal superalloys and is expected to accelerate the design and discovery of novel superalloys.

关键词： Nickel-based single crystal superalloys text mining named entity recognition credibility analysis domain knowledge

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ERDDCI: Exact Reversible Diffusion via Dual-Chain Inversion for High-Quality Image Editing

arXiv

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arXiv 2024年

作者： Dai, Jimin Zhang, Yingzhen Chen, Shuo Yang, Jian Luo, Lei The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China The RIKEN Japan

Diffusion models (DMs) have been successfully applied to real image editing. These models typically invert images into latent noise vectors used to reconstruct the original images (known as inversion), and then edit them during the inference process. However, recent popular DMs often rely on the assumption of local linearization, where the noise injected during the inversion process is expected to approximate the noise removed during the inference process. While DM efficiently generates images under this assumption, it can also accumulate errors during the diffusion process due to the assumption, ultimately negatively impacting the quality of real image reconstruction and editing. To address this issue, we propose a novel method, referred to as ERDDCI (Exact Reversible Diffusion via Dual-Chain Inversion). ERDDCI uses the new Dual-Chain Inversion (DCI) for joint inference to derive an exact reversible diffusion process. By using DCI, our method effectively avoids the cumbersome optimization process in existing inversion approaches and achieves high-quality image editing. Additionally, to accommodate image operations under high guidance scales, we introduce a dynamic control strategy that enables more refined image reconstruction and editing. Our experiments demonstrate that ERDDCI significantly outperforms state-of-the-art methods in a 50-step diffusion process. It achieves rapid and precise image reconstruction with an SSIM of 0.999 and an LPIPS of 0.001, and also delivers competitive results in image editing. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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Preface to Special Issue on Advanced Optoelectronic and Electronic Devices toward Future Displays--Celebration of the 10th anniversary of the state key laboratory of Advanced Displays and Optoelectronics Technologies at HKUST

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Journal of Semiconductors 2023年第9期44卷 1-2页

作者： Hoi-Sing Kwok Zhiyong Fan Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong Kong SARChina State Key Laboratory of Advanced Displays and Optoelectronics Technologies HKUSTClear Water BayKowloonHong Kong SARChina

This year marks the tenth anniversary of the state key laboratory of Advanced Displays and Optoelectronics Technologies(SKLADOT)at the Hong Kong University of science and technology(HKUST).The predecessor of SKLADOT w... 详细信息

关键词： ADO key UST

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The ChatGPT After: Building Knowledge Factories for Knowledge Workers with Knowledge Automation

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2041-2044页

作者： Yutong Wang Xiao Wang Xingxia Wang Jing Yang Oliver Kwan Lingxi Li Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence Anhui UniversityHefei 230039China Motion G Inc068898Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndiana 46202-5132 USA IEEE

Briefing: This perspective introduces the concept and framework of knowledge factories with knowledge machines for knowledge workers to achieve knowledge automation for Industry 5.0 and intelligent *** The big hit of Chat GPT makes it imperative to contemplate the practical applications of big or foundation models [1]-[5]. However, as compared to conventional models, there is now an increasingly urgent need for foundation intelligence of foundation models for real-world industrial applications.

关键词： Automation template Chat GPT

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A Multi-Stage Expensive Constrained Multi-Objective Optimization Algorithm Based on Ensemble Infill Criterion

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Wu, Haofeng Chen, Qingda Chen, Jiaxin Jin, Yaochu Ding, Jinliang Zhang, Xingyi Chai, Tianyou State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Computer Science and Technology Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China

Surrogate-assisted evolutionary algorithms (SAEAs) rely on the infill criterion to select candidate solutions for expensive evaluations. However, in the context of expensive constrained multi-objective optimization problems (ECMOPs) with complex feasible regions, guiding the optimization algorithm towards the constrained Pareto optimal front and achieving a balance between feasibility, convergence, diversity, exploration, and exploitation using a single infill criterion pose significant challenges. We propose an ensemble infill criterion-based multi-stage SAEA (EIC-MSSAEA) to tackle these challenges. Specifically, EIC-MSSAEA comprises three stages. In the first stage, we ignore constraints to facilitate the rapid traversal of infeasible obstacles. In the second stage, only one constraint is activated at a time to increase algorithm diversity. Finally, in the last stage, we activate all constraints to improve overall feasibility. In each stage, EIC-MSSAEA first employs NSGA-III as the underlying baseline solver to explore the search space, in which promising solutions are then selected by an ensemble infill criterion that incorporates multiple base-infill criteria to measure the feasibility, convergence, diversity, and uncertainty of candidate solutions. Experimental results demonstrate the competitiveness of EIC-MSSAEA against state-of-the-art SAEAs for ECMOPs. IEEE

关键词： Linear programming

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GaitCycFormer: Leveraging Gait Cycles and Transformers for Gait Emotion Recognition 39

GaitCycFormer: Leveraging Gait Cycles and Transformers for G...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zeng, Qingyang Shang, Lin State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)157735897X

Gait Emotion Recognition (GER) is an emerging task within Human Emotion Recognition. Skeleton-based GER requires discriminative spatial and temporal features. However, current methods primarily focus on capturing spatial topology information but fail to effectively learn temporal features from long-distance frames. Moreover, these methods are mostly sensitive to the order of sampled sequences, resulting in significant accuracy drops when sequences are randomly sampled. In order to obtain a more robust and comprehensive spatial-temporal representation of gait, we introduce the Graph-Transformer architecture into GER for the first time, proposing a novel framework named GaitCycFormer. Specifically, we designed a Cycle Position Encoding (CPE) based on the gait cycle, which explicitly segments any gait sequence into more manageable periodic units, to enhance temporal feature modeling. Additionally, we incorporate a bi-level Transformer, consisting of an Intra-cycle Transformer and an Inter-cycle Transformer to capture local and global temporal information within each gait cycle and between gait cycles respectively. Experiments demonstrate that our GaitCycFormer achieves state-of-the-art performance on popular datasets, and proves to be more reliable and robust. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Using Lock-Free Design for Throughput-Optimized Cache Eviction

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Proceedings of the ACM on Measurement and Analysis of Computing systems 2025年第2期9卷 1-28页

作者： Dong, Chao Wang, Fang Jiang, Hong Feng, Dan Huazhong University of Science and Technology Key Laboratory of Information Storage System Wuhan National Laboratory for Optoelectronics Engineering Research Center of data storage systems and Technology School of Computer Science and Technology Ministry of Education of China Hubei Wuhan China University of Texas at Arlington Department of Computer Science and Engineering ArlingtonTX United States

In large-scale information systems, storage device performance continues to improve while workloads expand in size and access characteristics. This growth puts tremendous pressure on caches and storage hierarchy in terms of concurrent throughput. However, existing cache eviction policies often struggle to provide adequate concurrent throughput due to their reliance on coarse-grained locking mechanisms and complex data structures. This paper presents a practical approach to cache eviction algorithm design, called Mobius, that optimizes the concurrent throughput of caches and reduces cache operation latency by utilizing lock-free data structures, while maintaining comparable hit ratios. Mobius includes two key designs. First, Mobius employs two lock-free FIFO queues to manage cache items, ensuring that all cache operations are executed efficiently in parallel. Second, Mobius integrates a consecutive detection mechanism that merges multiple modifications during eviction into a single operation, thereby reducing data races. Extensive evaluations using both synthetic and real-world workloads from high-concurrency clusters demonstrate that Mobius achieves a concurrent-throughput improvement ranging from 1.2× to 8.5× over state-of-the-art methods, while also maintaining lower latency and comparable cache hit ratios. The implementation of Mobius in CacheLib and RocksDB highlights its effectiveness in enhancing cache performance in practical scenarios. © 2025 ACM.

关键词： keyways

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