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A New Approach to the Position of Acupoints on the Back of Humans

Recent Patents on Mechanical Engineering 2023年第2期16卷 150-162页

作者： Xie, Shenglong Liu, Zijing Zhang, Liangan Song, Fengfeng Key Laboratory of Intelligent Manufacturing Quality Big Data Tracking and Analysis of Zhejiang Province China Jiliang University Zhejiang Hangzhou310018 China Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma’anshan243032 China Hangzhou Qiandaohu Ruichun Robot Research Institute Co. Ltd Zhejiang Hangzhou310018 China Zhejiang Shusi Robot Co. Ltd Zhejiang Huzhou313000 China

Aim: In order to realize the position of acupoints on the back of humans, a new acupoints positioning approach combined with machine vision technology is proposed in this paper. Background: The acupuncture point (acupoint) is an important reference in the treatment of Traditional Chinese Medicine (TCM). However, the traditional acupoints positioning method mainly depends on manpower, which has the disadvantages of low efficiency and relying on practical experience heavily. Objective: In order to realize the position of acupoints on the back of humans, a new acupoints positioning approach combined with machine vision technology is proposed in this paper. Methods: Firstly, the distribution of acupoints on the back of humans are determined according to the meridian theory, and the coordinate of each acupoint are established based on the approach of fingercun measurement, after review of previous literature and patents. Secondly, the principle of the proposed acupoints positioning method is introduced based on the skeleton model of humans. Finally, the effec-tiveness of the proposed approach is validated by experimental testing. Results: The value of emax is around 3.5mm and those of emae and erms are smaller than 0.25mm and 0.45mm, respectively. Conclusion: The experimental results prove that the proposed approach possesses the capability of good adaptive capability and high. © 2023 Bentham Science Publishers.

关键词： Computer vision

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Surface Defect Detection of Hot-Rolled Strip Based on Improved YOLOv7

Surface Defect Detection of Hot-Rolled Strip Based on Improv...

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2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023

作者： Jin, Yiting Li, Junqiu Zhang, Qinghui School of Big Data and Intelligent Engineering Southwest Forestry University Kunming China Southwest Forestry University Key Laboratory of Forest Ecological Big Data of National Forestry and Grassland Administration Kunming China

ISBN: (纸本)9798350314670

In order to increase the detecting precision of surface defects in industrial hot-rolled strip steel, this paper improves the network model for target detection of strip steel surface defects based on the YOLOv7 algorithm. First, to minimize information loss and enhance global dimensional interaction characteristics, the Global Attention Mechanism (GAM) is applied. Second, the YOLOv7 model incorporates the adaptive spatial feature fusion technique to achieve the fusion of top-level features with bottom-level features. Finally, the regression loss function used in the original model is substituted for SIoU Loss to increase the speed and accuracy of the frame regression. The testing outcomes reveal that the method in this paper has an average accuracy improvement of 3.1% compared to the YOLOv7 algorithm before improvements on the industrial hot-rolled strip data set (NEU-DET). © 2023 IEEE.

关键词： Surface defects

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Improved Demonstration-Knowledge Utilization in Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2139-2150页

作者： Liu, Yanyu Zeng, Yifeng Ma, Biyang Pan, Yinghui Gao, Huifan Zhang, Yuting Xiamen University Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen361102 China Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Minnan Normal University Department of Computer Science Zhangzhou363000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Reinforcement learning (RL) has made great success in recent years. Generally, the learning process requires a huge amount of interaction with the environment before an agent can achieve acceptable performance. This motivates many techniques, such as incorporating prior knowledge which is usually presented as experts' demonstration, and using a probability distribution to represent state-and-action values, to accelerate the learning process. The methods perform well when the prior knowledge is genuinely correct and no much change occurs to the learning environment. However, the requirement is not perfectly realistic in many complex applications. The demonstration knowledge may not reflect the true environment and even be full of noise. In this article, we introduce a dynamic distribution merging method to improve knowledge utilization in a general RL algorithm, namely Q-learning. The new method adapts a normal distribution to represent state-action values and merges the prior and learned knowledge in a discriminative way. We theoretically analyze the new learning method and demonstrate its empirical performance over multiple problem domains. © 2020 IEEE.

关键词： Merging

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Scene Graph Enhanced Pseudo-Labeling for Referring Expression Comprehension

Scene Graph Enhanced Pseudo-Labeling for Referring Expressio...

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Conference on Empirical Methods in Natural Language Processing (EMNLP)

作者： Wu, Cantao Cai, Yi Li, Liuwu Wang, Jiexin South China Univ Technol Sch Software Engn Guangzhou Peoples R China South China Univ Technol Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou Peoples R China

ISBN: (纸本)9798891760615

Referring Expression Comprehension (ReC) is a task that involves localizing objects in images based on natural language expressions. Most ReC methods typically approach the task as a supervised learning problem. However, the need for costly annotations, such as clear image-text pairs or region-text pairs, hinders the scalability of existing approaches. In this work, we propose a novel scene graph-based framework that automatically generates highquality pseudo region-query pairs. Our method harnesses scene graphs to capture the relationships between objects in images and generate expressions enriched with relation information. To ensure accurate mapping between visual regions and text, we introduce an external module that employs a calibration algorithm to filter out ambiguous queries. Additionally, we employ a rewriter module to enhance the diversity of our generated pseudo queries through rewriting. Extensive experiments demonstrate that our method outperforms previous pseudolabeling methods by about 10%, 12%, and 11% on RefCOCO, RefCOCO+, and RefCOCOg, respectively. Furthermore, it surpasses the stateof-the-art unsupervised approach by more than 15% on the RefCOCO dataset.

关键词： Graphic methods

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Updating Large Language Models' Memories with Time Constraints

Updating Large Language Models' Memories with Time Constrain...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Wu, Xin Bu, Yuqi Cai, Yi Wang, Tao School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China Department of Biostatistics and Health Informatics King's College London United Kingdom

ISBN: (纸本)9798891761681

By incorporating the latest external knowledge, large language models (LLMs) can modify their internal memory. However, in practical applications, LLMs may encounter outdated information, necessitating the filtering of such data and updating of knowledge beyond internal memory. This paper explores whether LLMs can selectively update their memories based on the time constraints between internal memory and external knowledge. We evaluate existing LLMs using three types of data that exhibit different time constraints. Our experimental results reveal the challenges most LLMs face with time-constrained knowledge and highlight the differences in how various LLMs handle such information. Additionally, to address the difficulties LLMs encounter in understanding time constraints, we propose a two-stage decoupling framework that separates the identification and computation of time constraint into a symbolic system. Experimental results demonstrate that the proposed framework yields an improvement of over 60% in ChatGPT's performance, and achieves a 12-24% enhancement in state-of-the-art LLM GPT-4. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Personalized Review Recommendation based on Implicit dimension mining

Personalized Review Recommendation based on Implicit dimensi...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Xu, Bei Xu, Yifan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China

Users usually browse product reviews before buying products from e-commerce websites. Lots of e-commerce websites can recommend reviews. However, existing research on review recommendation mainly focuses on the general usefulness of reviews and ignores personalized and implicit requirements. To address the issue, we propose a Large language model driven Personalized Review Recommendation model based on Implicit dimension mining (PRR-LI). The model mines implicit dimensions from reviews and requirements, and encodes them in the form of "text + dimension". The experiments show that our model significantly outperforms other state-of-the-art textual models on the Amazon-MRHP dataset, with some of the metrics outperforming the state-of-the-art multimodal models. And we prove that encoding "text + dimension" is better than encoding "text" and "dimension" separately in review recommendation. © 2024 Association for Computational Linguistics.

关键词： Signal encoding

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IGED:Towards intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

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Computers, Materials & Continua 2024年第8期80卷 1851-1866页

作者： Yanhua Liu Yuting Han HuiChen Baokang Zhao XiaofengWang Ximeng Liu College of Computer and Data Science Fuzhou UniversityFuzhou350108China Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou350108China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350108China College of Computer National University of Defense TechnologyChangsha410073China

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly *** propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data *** the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization *** results show that the proposed method can efficiently distinguish normal traffic from DDoS *** with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

关键词： DDoS real-time improved generalized entropy DNN

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Influencing Factors of Healthy Aging Risk Assessed Using Biomarkers:A Life Course Perspective

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China CDC weekly 2024年第11期6卷 219-224,I0014-I0017页

作者： Cedric Zhang Bo Lua Yajie Gao Jinming Li Xingqi Cao Xinwei Lyu Yinuo Tu Shuyi Jin Zuyun Liu Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Healththe Key Laboratory of Intelligent Preventive Medicine of Zhejiang ProvinceZhejiang University School of MedicineHangzhou CityZhejiang ProvinceChina Institute of Epidemiology and Health Care University College LondonLondonUK College of Chemical and Biological Engineering Zhejiang UniversityHangzhou CityZhejiang ProvinceChina

Assessing individual risks of healthy aging using biomarkers and identifying associated factors have become important areas of *** this study,we conducted a literature review of relevant publications between 2018 and 2023 in both Chinese and English *** studies have predominantly used single biomarkers,such as C-reactive protein,or focused on specific life course stages and factors such as socioeconomic status,mental health,educational levels,and unhealthy *** summarizing the progress in this field,our study provides valuable insights and future directions for promoting healthy aging from a life course perspective.

关键词： such insight directions

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Unsigned Road Incidents Detection Using Improved RESNET From Driver-View Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1203-1216页

作者： Li, Changping Wang, Bingshu Zheng, Jiangbin Zhang, Yongjun Chen, C.L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature of traffic introduces significant challenges, particularly in detecting sudden and temporary incidents. In this article, we propose a novel detection framework, multihead attention ResNet with dynamic bottleneck (DB_RESNET_MHA), to identify physical unsigned road incidents. Our approach introduces three key innovations. First, we develop a tailored data augmentation strategy to create images that closely mimic the complex variations found in real-world road environments. Second, we enhance model expressiveness by employing attention mechanisms to nonlinearly integrate convolutional kernels within the residual network. Furthermore, we refine the prediction head by applying spatially distinct attention weights, enabling the model to capture intricate correlations between different features more effectively. To demonstrate the effectiveness of our method, we create a dataset for unsigned road incidents (UnsignRI), comprising a total of 16 323 images that capture 12 distinct types of incidents. It stands out as the most comprehensive dataset in the field, encompassing a wide range of geographical features and incident categories. Experimental results show that DB_RESNET_MHA achieves an average accuracy of 96.2% and a f1-score of 0.955 across various categories of unsigned incidents, surpassing other models. © 2020 IEEE.

关键词： Highway accidents

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Homomorphic Encryption based stem cell assay data sharing study 4

Homomorphic Encryption based stem cell assay data sharing st...

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4th IEEE International Conference on Information Technology, big data and Artificial Intelligence, ICIBA 2024

作者： Xiao, Hengjiang Liang, Zhihong Huang, Yuxiang Qin, Mingming Xiong, Fei Liu, Mengxin Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China Southwest Forestry University Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data China Southwest Forestry University School of Economics and Management Kunming China

ISBN: (纸本)9798350363586

With the rapid development of the biomedical field, stem cell therapy has received widespread attention as an innovative therapy with great potential. However, privacy protection and secure sharing of stem cell testing data are still major challenges. In this paper, we propose a homomorphic encryption-based data sharing scheme for stem cell testing, which achieves efficient and secure data sharing with data privacy by designing a system architecture covering data acquisition, encryption, transmission, decryption and analysis. Experimental results show that the scheme can not only effectively protect data privacy, but also support efficient data processing with less impact on system performance. The main contributions of this paper include designing and implementing a stem cell detection data sharing system based on homomorphic encryption, proposing a homomorphic encryption algorithm optimised for the high-dimensional characteristics of stem cell data, and experimentally verifying the security and effectiveness of the system. Future research directions include optimising homomorphic encryption algorithms, expanding application scenarios, integrating multiple privacy protection techniques, and optimising system performance. © 2024 IEEE.

关键词： data assimilation

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