作者机构：
[Chaofan Wu; Tao He; Kun Yang] School of Computer Science, University of South China, Hengyang, China
会议名称：
2025 IEEE 5th International Conference on Power, Electronics and Computer Applications (ICPECA)
会议时间：
17 January 2025
会议地点：
Shenyang, China
会议论文集名称：
2025 IEEE 5th International Conference on Power, Electronics and Computer Applications (ICPECA)
关键词：
intensive pedestrian detection;RT-DETR;attention mechanism;GELAN modules;loss function
摘要：
Pedestrian detection is a vital study field within object detection. With the development of deep learning, models perform exceptionally well in pedestrian detection for sparse scenarios. However, their performance often fails in complex and crowded dense pedestrian scenarios, particularly when there is a concentration of small objects. Challenges such as low detection accuracy, high miss rates, and high false-positive rates persist in these scenarios. To enhance dense pedestrian detection performance, we propose a model named RT-DETR-LKG, based on RT-DETR. The improved model incorporates LSKA (Large Separable Kernel Attention) and the GELAN (Generalized Efficient Layer Aggregation Network) modules (LKG). The backbone network integrates the LSKA, which generates spatial attention weights for feature maps via multiple convolutional layers. This process enables feature extraction across various receptive fields and applies weighting to input channels, resulting in more refined feature extraction and enhancement. The hybrid encoder network applies the GENLAN module. It achieves rich feature extraction with minimal computational cost by combining channel partitioning and repeated convolutions. The network's feature extraction ability is also kept. In bounding box regression, the WIoU loss function is used instead of the GIoU loss function to improve anchor selection for occluded and small object prediction boxes. Experimental results indicate that the new RT-DETR algorithm performs better in crowded pedestrian scenarios. It has a mean average precision of 86.1%, which is 1.2% higher than the original model.
作者机构：
Key Laboratory of Medical Imaging Precision Theranostics and Radiation Protection, College of Hunan Province, University of South China, Changsha, China;Department of Medical Imaging, the Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China;[Xiaoxia Xing; Lu Yao] School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China;[Xiaowen Liang; Zhili Guo; Qing Zhang; Zhiyi Chen] Key Laboratory of Medical Imaging Precision Theranostics and Radiation Protection, College of Hunan Province, University of South China, Changsha, China<&wdkj&>Department of Medical Imaging, the Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China
会议名称：
2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)
会议时间：
14 April 2025
会议地点：
Houston, TX, USA
会议论文集名称：
2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)
摘要：
Accurate localization of standard planes is crucial for sonog-raphers diagnosing and evaluating endometrial function from ultrasound videos. Traditionally, this process is performed manually, resulting in significant inter- and intra-observer variability. To address these challenges and enhance diagnos-tic consistency, we propose an automated model that localizes endometrial standard planes by detecting key anatomical structures. Specifically, our model segments the endometrium and identifies uterine cavity lines, facilitating standard plane localization based on clinical prior knowledge. Experimen-tal results demonstrate that our model outperforms existing methods, achieving an AUC of 0.922 and an accuracy of 0.869. The model's predictions provide consistent and in-terpretable results, supporting sonographers in their assessments.
作者机构：
[Huifang Tang] The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, China [email protected];[Jiaqi Liu] The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, Hunan, China [email protected];[Jin Liu] School of Computer, University of South China, Hengyang, Hunan, China [email protected]
会议名称：
BIC '25: Proceedings of the 2025 5th International Conference on Bioinformatics and Intelligent Computing
摘要：
3D medical images can visualize the internal structure of human organs, which have significant advantages over 2D medical images. However, the annotations of 3D medical images are more difficult to obtain compared to 2D medical images, which makes it challenging to capture complete spatial information in 3D medical images. To address this challenge, we propose a simple but effective self-supervised learning method that utilizes the similarity between medical images for region comparison and learns the organ correspondence information from different images but between the same regions. We pre-trained our method on the UK Biobank's cardiac short-axis nuclear magnetic resonance dataset. Our method performs well on three downstream task-related datasets.
期刊：
Proceedings of the 2024 5th International Symposium on Artificial Intelligence for Medicine Science,2025年:421-425
作者机构：
[Dongping Xiong; Lijun Ouyang] School of Computing/Software, University of South China, Hengyang, Hunan, China [email protected];[Yudan Li; Xiaozhi Zhang] School of Electrical Engineering, University of South China, Hengyang, Hunan, China [email protected]
会议名称：
ISAIMS '24: Proceedings of the 2024 5th International Symposium on Artificial Intelligence for Medicine Science
会议论文集名称：
Artificial Intelligence for Medicine Science
摘要：
Accurately segmenting brain tumors from multimodal MRI sequences is a key prerequisite for brain tumor diagnosis, prognosis assessment, and surgical treatment. However, in practical applications, one or more modal data is often missing due to image corruption, different acquisition protocols, artifacts, contrast agent allergies, or cost considerations. To address the challenges of brain tumor segmentation under modality loss, this paper proposes an innovative tumor feature perception strategy. The core of this strategy is to introduce a Mamba-based Encoder (MBE) architecture, which effectively improves the feature expression ability of each modality encoder under limited data conditions. In view of the irregularity of tumor morphology, a Modulation and Demodulation Fusion Block (MDFB) is designed to accurately capture the semantic features of the tumor from the missing multimodal image data, providing strong guidance for the network to locate the tumor area. Experimental results on the widely used BraTS2020 dataset demonstrate the effectiveness of MMITS, and the brain tumor segmentation effect under various incomplete modalities is better than the state-of-the-art methods.
作者机构：
[Sen Yang] Southern University of Science and Technology, Shenzhen, China;[Linchao Li] University of South China, Hengyang, China;[Zhihan Liu] Wuhan University of Science and Technology, Wuhan, China;[Shiwei Liu; Kun Zhang] Beijing University of Posts and Telecommunications, Peking, China
会议名称：
2025 International Conference on Digital Analysis and Processing, Intelligent Computation (DAPIC)
会议时间：
26 February 2025
会议地点：
Incheon, Korea, Republic of
会议论文集名称：
2025 International Conference on Digital Analysis and Processing, Intelligent Computation (DAPIC)
关键词：
Tar Yield Prediction;Cubic Spline Interpolation;Correlation Analysis;Machine Learning;Regression Model
摘要：
This paper investigates the prediction method for tar yield based on correlation analysis and machine learning. Initially, to address the issue of missing values in the data, this study employs cubic spline interpolation for imputation. By constructing the interpolation function and setting constraint conditions, the data imputation process was successfully completed. Subsequently, this paper conducts Pearson and Spearman correlation analyses to assess the linear and nonlinear relationships between different variables, and the results of the correlation analysis are displayed through heatmaps. The findings reveal that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex>-hexane insoluble material (INS) is positively correlated with tar yield, while it is negatively correlated with water yield and char yield. To further predict the tar yield, this study constructs regression models based on machine learning, including Random Forest (RF), Support Vector Regression (SVR), and Backpropagation Neural Network (BPNN). Through MATLAB programming, these models were trained and tested, and the model performance was evaluated using MSE, RMSE, MAE, and MAPE as evaluation metrics. The results indicate that the BPNN model demonstrates superior performance across all evaluation metrics.
作者机构：
[Xig, Jiaojiao; Li, Wenjun; Ma, Wanjun; Peng, Huan] Changsha Univ Sci & Technol, Hunan Prov Key Lab Intelligent Proc Big Data Tran, Changsha, Hunan, Peoples R China.;[Liang, Weijun] Univ South China, Affiliated Changsha Cent Hosp, Hengyang Med Sch, Changsha, Hunan, Peoples R China.
会议名称：
7th Chinese Conference on Pattern Recognition and Computer Vision
会议时间：
OCT 18-20, 2024
会议地点：
Urumqi, PEOPLES R CHINA
会议主办单位：
[Li, Wenjun;Xig, Jiaojiao;Peng, Huan;Ma, Wanjun] Changsha Univ Sci & Technol, Hunan Prov Key Lab Intelligent Proc Big Data Tran, Changsha, Hunan, Peoples R China.^[Liang, Weijun] Univ South China, Affiliated Changsha Cent Hosp, Hengyang Med Sch, Changsha, Hunan, Peoples R China.
会议论文集名称：
Lecture Notes in Computer Science
关键词：
Embedded Deep Learning;Rifampicin;Drug Resistance;Tuberculosis;CT Images;Diagnostic Application
摘要：
In the treatment of tuberculosis (TB), drug-resistant tuberculosis arises when Mycobacterium tuberculosis undergoes genetic mutations or acquires resistance through horizontal gene transfer. Identifying the treatment response of TB patients to Rifampicin, a principal medication for TB treatment, is essential for healthcare professionals to make timely and accurate diagnoses. Not only can this approach save on the costs and duration of TB treatment, but it also helps prevent the disease's spread and fatalities. Traditional methods for diagnosing Rifampicin-resistant TB involve molecular biology tests and drug susceptibility testing, which are time-consuming, expensive, and labor-intensive. To assist physicians in diagnosing the treatment response of TB patients to Rifampicin more rapidly and efficiently, this study introduces a computer-aided diagnostic algorithm based on Embedded Deep Learning (EDL). Initially, CT images from target patients at two imaging centers were collected. The classifier model used in this research combines image preprocessing techniques, three convolutional neural networks, and decision fusion technology to enhance the model's classification efficiency and reduce overfitting. Additionally, the Grad-CAM model was utilized for visualizing the areas of lesions. In the test sets from both centers, the Embedded Deep Learning Model (EDL Model) demonstrated superior performance over other models by combining hard voting or soft voting mechanisms, with an average accuracy improvement of 3.16-16.87%, AUC increase of 3.05-12.66%, and F1-score enhancement of 6.38-22.49%. The diagnostic tool developed in this research for assisting in the diagnosis of TB patients' response to Rifampicin treatment has significant clinical potential, particularly in settings lacking specialized radiological expertise.
作者机构：
[Zhicheng Bi; Jinfeng Xiao; Chaofeng Wang] School of Electrical Engineering, University of South China, Hengyang, Hunan, China [email protected]
会议名称：
AISNS '24: Proceedings of the 2024 2nd International Conference on Artificial Intelligence, Systems and Network Security
摘要：
Underwater acoustic communication networks can satisfy the need for long-distance reliable communication for underwater node, serving as a key technology for the informatization and intelligentization of such communication. However, due to the use of sound waves as a carrier for information, underwater acoustic networks face complex challenges such as long propagation delays, severe channel attenuation, multipath effects, and environmental noise. To enhance the communication efficiency and reduce network congestion of underwater acoustic networks, this study proposes a MAC protocol based on deep reinforcement learning to adaptively adjust the transmission strategy of network nodes. Each network node collaboratively decides the optimal transmission slot based on its own data buffer status and the history of transmission conflicts, to suppress transmission conflicts and thereby maximize transmission rates. Simulation results show that the designed protocol significantly improves the network throughput while ensuring the fairness of transmission among nodes, providing an important foundation for the rapid exchange of information between underwater nodes.
会议名称：
13th International Conference on Natural Language Processing and Chinese Computing
会议时间：
NOV 01-03, 2024
会议地点：
Westlake University, Hangzhou, PEOPLES R CHINA
会议主办单位：
Westlake University
会议论文集名称：
Lecture Notes in Artificial Intelligence
关键词：
large language models;group process;natural language process
摘要：
Exploring the alignment of multi-agent systems with human values during group process is an essential step towards the development of artificial general intelligence. In this work, we present a novel approach to systematically evaluate factors that influence the value orientation of large language models (LLMs) in simulating human group process. Our proposed framework, which requires neither fine-tuning nor pre-training, enables LLMs to simulate debates among personas on a given topic, autonomously assess response confidence, and retrieve external information to enhance low-confidence responses. We conduct comparative experiments using the same framework with human participants. Additionally, we introduce a comprehensive set of evaluation metrics to reveal discrepancies in value orientation alignment between LLM systems and human systems in group process.
摘要：
Contemporary makeup approaches primarily hinge on unpaired learning paradigms, yet they grapple with the challenges of inaccurate supervision (e.g., face misalignment) and sophisticated facial prompts (including face parsing, and landmark detection). These challenges prohibit low-cost deployment of facial makeup models, especially on mobile devices. To solve above problems, we propose a brand-new learning paradigm, termed "Data Amplify Learning (DAL)," alongside a compact makeup model named "TinyBeauty." The core idea of DAL lies in employing a Diffusion-based Data Amplifier (DDA) to "amplify" limited images for the model training, thereby enabling accurate pixel-to-pixel supervision with merely a handful of annotations. Two pivotal innovations in DDA facilitate the above training approach: (1) A Residual Diffusion Model (RDM) is designed to generate high-fidelity detail and circumvent the detail vanishing problem in the vanilla diffusion models; (2) A Fine-Grained Makeup Module (FGMM) is proposed to achieve precise makeup control and combination while retaining face identity. Coupled with DAL, TinyBeauty necessitates merely 80K parameters to achieve a state-of-the-art performance without intricate face prompts. Meanwhile, TinyBeauty achieves a remarkable inference speed of up to 460 fps on the iPhone 13. Extensive experiments show that DAL can produce highly competitive makeup models using only 5 image pairs. Please visit https://github.com/TinyBeauty for code and demos.
作者机构：
[Zhaomeng Liu] Insititute of of Biochemistry and Molecular Biology, Hengyang Medical College, University of South China, Hengyang, Hunan, China [email protected];[Fujuan Dong; Zhengmin Yu; Fuhong Cai; Lidong Li] School of Computer, University of South China, Hengyang, Hunan, China [email protected];School of Computer, University of South China, Hengyang, Hunan, China;Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, China [email protected];[Ye Li] Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hengyang, Hunan, China [email protected]
会议名称：
BIC '25: Proceedings of the 2025 5th International Conference on Bioinformatics and Intelligent Computing
摘要：
There are excellently annotated cell atlases for scRNA-seq currently, and there have been many works on cell type annotation of scRNA-seq data, and many methods have achieved good achievements. However, it has been noted that the extreme sparsity of scATAC-seq data often limits its power in cell-type identification. There are still few related algorithms available, and online cell type annotation tools are still lacking. The existing methods for annotating scATAC-seq rely too much on the scRNA-seq reference set, and the cell-type label accuracy needs to be improved. Therefore, we propose to use the genomic region sets enrichment analysis to specifically cell-type identification and analysis in scATAC-seq data using the online tool scEnrich (https://bio.liclab.net/scEnrich/). We have collected a large amount of genomic region sets data related to epigenetic regulation on this website, such as transcription factors, enhancers, super enhancers, etc. In addition, we have also collected part of the single-cell data set as background. At present, scEnrich supports over 13800 genomic region reference sets, covering regions of 7 different data types. This article is based on the method of genomic region sets enrichment analysis to perform LOLA analysis on the query set and background set, determine the cell types of scATAC-seq data, and provide detailed annotations on epigenetic regulation. scEnrich provides a user-friendly interface for querying and browsing interested cell types, facilitating researchers to annotate scATAC-seq data and greatly simplifying their research work, providing valuable resources for further exploration of single-cell epigenetic regulation.
摘要：
The popularity of Connected Autonomous Vehicles (CAVs) has led to improvements in the efficiency of the transportation system. Controller Area Network (CAN) is the standard communication protocol used in CAVs. However, the absence of effective security measures within CAN has resulted in vulnerabilities that can be exploited by attackers. To address this issue, we propose an Intrusion Detection System (IDS) using Time series Imaging and Deep Learning called TIDL-IDS. First, the CAN ID in the CAN frame is encoded as the Markov Transition Field (MTF) images to take into account the temporal characteristics of the CAN time series. Given the limited resources in the vehicle network environment, a simple four-layer deep convolutional neural network is designed to classify the converted images. A comprehensive evaluation of TIDL-IDS on real datasets demonstrates that the proposed method outperforms the other two baseline methods in terms of F1 score and accuracy. Furthermore, the model parameters are also superior to those of the other methods.
作者机构：
[Wang, Jincan; Song, Yucheng; Lan, Peng; Ge, Yifan; Liao, Zhifang] Cent South Univ, Sch Comp Sci & Engn, Changsha, Peoples R China.;[Guo, Jia] Cent South Univ, Xiangya Sch Nursing, Changsha, Peoples R China.;[Li, Lifeng] Univ South China, Affiliated Changsha Cent Hosp, Hengyang Med Sch, Dept Radiol, Changsha, Hunan, Peoples R China.;[Li, Lifeng] Nanchang Univ, Med Imaging Ctr, Affiliated Hosp 1, Nanchang, Jiangxi, Peoples R China.
会议名称：
7th Chinese Conference on Pattern Recognition and Computer Vision
会议时间：
OCT 18-20, 2024
会议地点：
Urumqi, PEOPLES R CHINA
会议主办单位：
[Song, Yucheng;Wang, Jincan;Ge, Yifan;Liao, Zhifang;Lan, Peng] Cent South Univ, Sch Comp Sci & Engn, Changsha, Peoples R China.^[Guo, Jia] Cent South Univ, Xiangya Sch Nursing, Changsha, Peoples R China.^[Li, Lifeng] Univ South China, Affiliated Changsha Cent Hosp, Hengyang Med Sch, Dept Radiol, Changsha, Hunan, Peoples R China.^[Li, Lifeng] Nanchang Univ, Med Imaging Ctr, Affiliated Hosp 1, Nanchang, Jiangxi, Peoples R China.
会议论文集名称：
Lecture Notes in Computer Science
关键词：
Medical image classification;Noisy label;Knowledge distillation with multiple teachers;Deep learning;Point-of-care
摘要：
In recent years, the development of medical imaging technology has transformed imaging solutions from laboratory-based to point-of-care imaging with real-time capabilities. However, these point-of-care devices are often constrained by environmental factors such as ambient light and noise, leading to poor image quality and consequently affecting the diagnostic accuracy of point-of-care devices. Furthermore, due to the need for lightweight models in point-of-care devices, traditional models fail to meet requirements in terms of computational resources, model parameters, and inference time. Therefore, to address the aforementioned issues, this paper proposes an optimized lightweight student model that focuses on residual information. A lightweight structure based on Shift MLP is designed on the residual branch of the model to enhance the model's capability to acquire spatial feature information at multiple scales. Simultaneously, we propose a multi-teacher distillation strategy to improve the accuracy and noise-resistance of the student model. Firstly, we introduce an adaptive learning approach based on auxiliary teachers, leveraging unlabeled and noisy data for adaptive learning to enhance the model's robustness. Then, we design a global teacher model to enhance the accuracy of the student model and indirectly improve the teaching ability of auxiliary teacher model, thereby achieving knowledge transfer at a global level. We evaluate our approach on two public medical image classification datasets, and the results demonstrate that while almost maintaining accuracy, we reduce the number of parameters by 38 times, decrease computational complexity by 11 times, and achieve an inference time of only 18.94ms on CPU.
作者机构：
[Zhixiong Xie; Wenlong Tian] University of South China, Hengyang, Hunan, China;[Zhiyong Xu] Suffolk University, Boston, Massachusetts, USA;[Weijun Xiao] Virginia Commonwealth University, Richmond, Virginia, USA;[Jianfeng Lu] Wuhan University of Science and Technology, Wuhan, Hubei, China
会议名称：
WWW '25: Companion Proceedings of the ACM on Web Conference 2025
摘要：
Detecting similar data is crucial for optimizing file storage and transmission in HTTP protocols and Content Delivery Networks. Traditional MinHash methods encounter significant efficiency challenges due to their reliance on K-shingle structures, resulting in high computational costs and storage requirements. Additionally, these methods expose privacy risks in cloud environments, where sensitive information can be inferred from MinHash signatures. To address both efficiency and security concerns, we propose Horse-MinHash, which integrates a fast, content-defined feature extraction scheme with a non-interactive zero-knowledge proof-based similarity estimation method. Our approach significantly enhances computational efficiency while ensuring robust privacy protection by preventing plaintext exposure. Experimental results demonstrate that Horse-MinHash achieves lower mean squared error in Jaccard similarity estimation and reduces time overhead for average block sizes of 16KB or more, outperforming state-of-the-art methods.
期刊：
Proceedings of the 2024 8th International Conference on Electronic Information Technology and Computer Engineering,2025年:1288-1298
作者机构：
[Yehong Zhao; Jinmei Li] College of Electrical and Information Engineering, Hunan Institute of Traffic Engineering, Hengyang, Hunan, China [email protected];[Junwen Deng; Mingyue Zhang; Huan Liu; Mingtao Liu] School of Computing / Software, University of South China, Hengyang, Hunan, China [email protected]
会议名称：
EITCE '24: Proceedings of the 2024 8th International Conference on Electronic Information Technology and Computer Engineering
会议论文集名称：
Electronic Information Technology and Computer Engineering
摘要：
The aging population has led to an increased need for reliable fall detection systems that can provide timely assistance to individuals who have fallen. This paper presents a multi-sensor fusion system designed to detect falls and issue early warnings, overcoming the limitations of camera-based systems, particularly in areas where camera surveillance is not feasible. We utilize a combination of an MPU6050 six-axis pose sensor and a MAX30102 heart rate and blood oxygen sensor to achieve high accuracy in fall detection. The system is designed to differentiate between falling and normal physical activities, reducing false positives. When a fall is detected, the system triggers an alert and sends real-time health data to a TCP server for monitoring on a mobile terminal. Our approach addresses camera blind spots and provides a comprehensive solution for fall detection in non-visual environments.
会议名称：
13th International Conference on Natural Language Processing and Chinese Computing
会议时间：
NOV 01-03, 2024
会议地点：
Westlake University, Hangzhou, PEOPLES R CHINA
会议主办单位：
Westlake University
会议论文集名称：
Lecture Notes in Artificial Intelligence
关键词：
Biomedical Relation Extraction;Natural Language Inference;Counterfactual Inference;Debias
摘要：
Biomedical relation extraction is a core problem in biomedical natural language processing, whose goal is to classify the relations between entity mentions within a classified given text, being modeled as a classification method. There has been some recent work on converting biomedical RE to other auxiliary tasks to deal with it. However, they have all neglected the fact that this form of RE is subject to the inherent bias of the auxiliary task, resulting in the inability to make truly reasonable predictions. In this paper, we propose the CouBRE method, which excludes the direct influence of premise-only and hypothesis-only branches in the NLI task when converting biomedical RE to NLI task from a causal perspective on the RE results, allowing the NLI model to make valid predictions based on the combined information of premise and hypothesis rather than based on shortcut paths. Also by removing sample selection bias and label bias in the dataset, CouBRE can make unbiased prediction results more clearly. Comprehensive experiments demonstrate the effectiveness of our approach in low-resource scenarios, outperforming previous state-of-the-art models and remaining competitive even in full-shot scenarios.
摘要：
This study proposes a photovoltaic substation engineering site selection and line planning method based on intelligent algorithms to improve complex site selection areas' planning efficiency and scientificity. This method comprehensively utilizes unmanned aerial vehicle oblique photography, multi-objective genetic algorithm, differential evolution algorithm, and adaptive algorithm. Integrating advanced technology and optimization algorithms achieves efficient and accurate substation site selection and line planning. A multi-objective optimization model is proposed by collecting geographic information through drone technology and establishing a three-dimensional model, considering environmental impact, power supply reliability, and construction costs. This method was comprehensively compared with traditional genetic algorithms and other methods in the experimental stage. The experimental results show that the method proposed in this paper performs well in multiple indicators. Compared to traditional methods, this approach effectively reduces the total cost and significantly improves power supply reliability, achieving a 9% improvement. At the same time, the convergence speed has also been accelerated by 600 generations, reflecting the efficiency and superiority of the algorithm in solving complex problems. The method proposed in this paper improves the efficiency of substation site selection and line planning. It provides new ideas and technical means for research in related fields, verifying intelligent algorithms' practical application value and potential in power engineering.
期刊：
2024 4th International Conference on Energy Engineering and Power Systems (EEPS),2024年:1147-1151
作者机构：
[Yankai Zhang; Youwei He] School of Mechanical Engineering, University of South China, Hunan, Hengyang, China
会议名称：
2024 4th International Conference on Energy Engineering and Power Systems (EEPS)
会议时间：
09 August 2024
会议地点：
Hangzhou, China
会议论文集名称：
2024 4th International Conference on Energy Engineering and Power Systems (EEPS)
关键词：
Thermal management;Phase-change direct cooling system;Thermal-fluid-solid coupling simulation
摘要：
Cooling high heat flux electronic devices has always been a significant challenge in the field of electronics. With the continuous increase in laser power density, the demand for effective cooling has become increasingly urgent. This study focuses on a phase-change direct cooling system designed for a 2kW laser. To analyze the heat dissipation performance of the cold plate, the key component of the cooling system, the thermal-fluid-structural coupled simulations were conducted using Fluent software. The simulations covered the temperature distribution of the cold plate under design conditions and phase-change analysis within the flow channels. The highest temperature on the cold plate is 36.69°C. Additionally, simulations under non-design conditions were performed to obtain data such as the maximum temperature of the cold plate. Under 50% load, the highest temperature decreases by 5°C compared to the design conditions.
期刊：
IET Conference Proceedings,2024年2024(17):81-97
作者机构：
[Youwei He; Linzhi Liu; Jinliang Luo] School of Mechanical Engineering, University of South China, Hengyang, People's Republic of China
会议名称：
6th Chinese International Turbomachinery Conference (CITC 2024)
会议时间：
2024
会议地点：
Sanya, China
会议论文集名称：
6th Chinese International Turbomachinery Conference (CITC 2024)
摘要：
Recently, multi-fidelity multi-output Kriging have been proposed to take the advantages of both the multi-fidelity and multi-output modelling methods. The predictive performance of multi-fidelity multi-output Kriging model has been evaluated with the modelling efficiency being neglected. In this paper, a multi-fidelity multi-output Kriging model is developed and compared with Kriging, multi-fidelity Kriging, and multi-output Kriging w.r.t. both the modelling accuracy and efficiency. Furthermore, the efficient modelling method for high-dimensional problems is incorporated into the various Kriging models. A 144-dimension engineering problem is established and collected with analytic problems to investigate the modelling accuracy and efficiency. Results show that the multi-output Kriging or multi-fidelity multi-output Kriging model can provide more accurate predictions than the single- or multi-fidelity single-output models on problems with low-dimensional inputs. While, for problems with high-dimensional inputs, the predictive capability of the multi-output Kriging or multi-fidelity multi-output Kriging degenerates significantly with the explosive growth of the time for model construction. With the efficient modelling method, the predictive capability and the modelling efficiency of the multi-output Kriging or multi-fidelity multi-output Kriging model are improved remarkably. However, their performance is still outperformed by single-output multi-fidelity Kriging model throughout the empirical experiments for problems with more than 10 inputs.
作者机构：
[Xun Peng; Tengkai Tan; Teng Fan] School of Computer Science, University of South China, Hengyang, China
会议名称：
2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT)
会议时间：
26 April 2024
会议地点：
Jilin, China
会议论文集名称：
2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT)
关键词：
ICD coding;bias removal;deep learning
摘要：
The automated coding task aims to match medical text records with the corresponding International Classification of Diseases (ICD) codes to improve the efficiency and accuracy of medical record management. In the automatic coding task, existing methods often face the challenge of label bias, a problem that affects the overall performance of the model. To address this challenge, we propose a new bias removal method that aims to optimize model performance. Furthermore, we note that some samples are difficult to be recognized during the encoding process due to their complexity. Given the huge amount of data contained in the large language models, we attempted to use these models to recognize these hard samples and compared their effectiveness with our debiasing method. Test results on the MIMIC-III dataset show that we find our proposed debiasing method significantly outperforms the method that relies only on large language models in dealing with hard samples, thus confirming the effectiveness and superiority of our method.
