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4th International Symposium on Computer Technology and Information Science, ISCTIS 2024

作者： Wu, Xiao Jiang, Xinghe Li, Shaobo Zhou, Yanqiu Guizhou University School of Mechanical Engineering Guiyang China Guizhou University State Key Laboratory of Public Big Data Guiyang China Guizhou University Jiangnan Institute of Mechanical and Electrical Design Guiyang China

ISBN: (纸本)9798350354560

As an advanced version of the Rao algorithm, the fully informed search algorithm (FISA) exhibits exceptional global search capabilities. However, its slow convergence rate somewhat limits its applicability in solving real-world problems. In contrast, the teaching-learning-based optimization (TLBO) algorithm is renowned for its rapid convergence but is highly susceptible to local optima entrapment. To capitalize on the robust global search prowess of FISA and the swift convergence characteristic of TLBO, while addressing optimization challenges, this study introduces an innovative proactive hybrid algorithm termed FISTLBO. Comparative experiments are conducted on the CEC2022 benchmark suite comprising 20-dimensional test functions, pitting the FISTLBO algorithm against six classical algorithms. The results demonstrate that the FISTLBO algorithm not only achieves superior consistency and stability in benchmark function outcomes but also outperforms other comparative algorithms, securing the top rank in the Friedman rank sum test. Consequently, the proposed FISTLBO algorithm proves to be an effective solution for intricate optimization problems. © 2024 IEEE.

关键词： Optimization algorithms

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A Driving Area Detection Algorithm Based on Swin Transformer 5

A Driving Area Detection Algorithm Based on Swin Transformer

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5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Liu, Shuang Li, Ying Jilin University College of Computer Science and Technology Jilin Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin Changchun130012 China

ISBN: (纸本)9798350309034

The detection of drivable areas holds immense significance within the perception system of autonomous vehicles. This capability enables intelligent vehicles to gain a comprehensive understanding of the current road conditions and effectively identify safe areas suitable for safe navigation. Convolutional Neural Networks (CNNs) have achieved significant progress in the application of drivable areas detection, particularly in semantic segmentation. However, most existing CNN-based methods for drivable area detection only consider information within a local neighborhood, thus failing to effectively capture long-range dependencies. To tackle this concern, we propose a novel U-shaped network based on the Swin Transformer. The Swin Transformer block was employed as the fundamental element of both the encoder and decoder. Additionally, an upsampling layer was introduced in the decoder to effectively restore the resolution of the feature map. We evaluate our approach on the Cityscapes dataset, a publicly available benchmark for autonomous driving. Experimental results verify the exceptional performance of our proposed method, achieving an impressive mean Intersection over Union (mIoU) score of 91.61%. Our method outperforms existing state-of-the-art techniques in road segmentation detection, further demonstrating its superiority. © 2023 IEEE.

关键词： CNNS Driving Area Detection Semantic segmentation Swin Transformer

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Assessing the Potential Tidal Energy Resources in Coastal Areas of Fujian Province 6

Assessing the Potential Tidal Energy Resources in Coastal Ar...

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6th International Conference on Energy Systems and Electrical Power, ICESEP 2024

作者： Jiala, Erge Wu, Wenting Fuzhou University Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology The Academy of Digital China Fuzhou China

ISBN: (纸本)9798350353563

In the context of increasingly serious depletion and security risks of fossil energy, environmental pollution, and challenges of climate change, tidal range energy, and tidal current energy have attracted considerable attention due to their high predictability, insensitivity to flood and drought periods, and minimal environmental impact. In this study, we aim to assess the status of tidal range energy and tidal current energy in the coastal areas of Fujian Province using a high-resolution tidal dynamic model developed from the state-of-the-art model, Delft3D FM. Our results indicate that tidal range energy resources are concentrated in the coastal port areas north of central Fujian, with the highest tidal range energy potential of 1.18×105 KWh/m2 in Xinghua Bay. Tidal range energy resources are primarily concentrated in the waters of Sansha Bay, Pingtan Island, and Quanzhou Bay. Among them, the potential annual average power in Sansha Bay can reach 2.60×105 kWh/m2, with an estimated exploitable capacity of 67.24MW after the installation of tidal current power stations at the inlet channels. These research findings provide data support for site selection in the development of tidal range energy resources in the offshore waters of Fujian. © 2024 IEEE.

关键词： Delft3D FM High-resolution modelling Potential resources assessment Tidal energy

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AEMLO: AutoEncoder-Guided Multi-Label Oversampling

arXiv

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arXiv 2024年

作者： Zhou, Ao Liu, Bin Wang, Jin Sun, Kaiwei Liu, Kelin Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunications China

Class imbalance significantly impacts the performance of multi-label classifiers. Oversampling is one of the most popular approaches, as it augments instances associated with less frequent labels to balance the class distribution. Existing oversampling methods generate feature vectors of synthetic samples through replication or linear interpolation and assign labels through neighborhood information. Linear interpolation typically generates new samples between existing data points, which may result in insufficient diversity of synthesized samples and further lead to the overfitting issue. Deep learning-based methods, such as AutoEncoders, have been proposed to generate more diverse and complex synthetic samples, achieving excellent performance on imbalanced binary or multi-class datasets. In this study, we introduce AEMLO, an AutoEncoder-guided Oversampling technique specifically designed for tackling imbalanced multi-label data. AEMLO is built upon two fundamental components. The first is an encoder-decoder architecture that enables the model to encode input data into a low-dimensional feature space, learn its latent representations, and then reconstruct it back to its original dimension, thus applying to the generation of new data. The second is an objective function tailored to optimize the sampling task for multi-label scenarios. We show that AEMLO outperforms the existing state-of-the-art methods with extensive empirical studies. Copyright © 2024, The Authors. All rights reserved.

关键词： Interpolation

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Facial expression recognition under occlusion conditions based on multi-feature cross-attention

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9841-9856页

作者： Guo, Sheng Tan, Mian Cai, Shan Zhang, Zaijun Liang, Yihui Feng, Hongxi Zou, Xue Wang, Lin School of Data Science and Information Engineering Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China School of Mathematics and Statistics Qiannan Normal University for Nationalities Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province Duyun China Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizhou Aerospace Tianma Electromechanical Technology Co LTD Zunyi China

Although facial expression recognition (FER) has a wide range of applications, it may be difficult to achieve under local occlusion conditions which may result in the loss of valuable expression features. This issue has motivated the present study, as a part of which an effective multi-feature cross-attention network (MFCA-Net) is proposed. The MFCA-Net consists of a two-branch network comprising a multi-feature convolution module and a local cross-attention module. Thus, it enables decomposition of facial features into multiple sub-features by the multi-feature convolution module to reduce the impact of local occlusion on facial expression feature extraction. In the next step, the local cross-attention module distinguishes between occluded and unoccluded sub-features and focuses on the latter to facilitate FER. When the MFCA-Net performance is evaluated by applying it to three public large-scale datasets (RAF-DB, FERPlus, and AffectNet), the experimental results confirm its good robustness. Further validation is performed on a real FER dataset with local occlusion of the face. © 2024 – IOS Press.

关键词： Convolution

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Optimizing Random Access to Hierarchically-Compressed data on GPU

Optimizing Random Access to Hierarchically-Compressed Data o...

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Supercomputing Conference

作者： Feng Zhang Yihua Hu Haipeng Ding Zhiming Yao Zhewei Wei Xiao Zhang Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering (MOE) School of Information Renmin University of China Beijing China

ISBN: (纸本)9781665454452

GPU's powerful computational capacity holds great potentials for processing hierarchically-compressed data without decompression in data science domain. Unfortunately, existing GPU approaches offer only traversal-based data analytics; random access is extremely inefficient, substantially limiting their utility. To solve this problem, we develop a novel and broadly applicable optimization that enables efficient random access to hierarchically-compressed data without decompression in GPU memory. We address three major challenges for enabling efficient random access to compressed data on GPUs. The first challenge is designing GPU data structures that support random access. The second challenge is efficiently generating data structures on GPU. Generating data structures for random access is costly on the CPU, and the inefficiency increases dramatically when PCIe data transmission is incorporated. The third challenge is query processing on compressed data in GPU memory. Random accesses, including data updates, result in significant conflicts between massive threads. To solve the first challenge, we propose and modify a number of compressed data structures, including indexing within the complicated GPU memory hierarchy. To address the second challenge, we develop a two-phase process for generating these data structures on the GPU. To handle the third challenge, we propose a double-parsing design to avoid data conflicts. We evaluate our solution on two GPU platforms using five real-world datasets. Experiments show that the random access operations on GPU can achieve 65.04x average speedup compared to the state-of-the-art method.

关键词： Limiting Query processing Instruction sets High performance computing Memory management Graphics processing units data science

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Local-Global History-Aware Contrastive Learning for Temporal knowledge Graph Reasoning 40

Local-Global History-Aware Contrastive Learning for Temporal...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Chen, Wei Wan, Huaiyu Wu, Yuting Zhao, Shuyuan Cheng, Jiayaqi Li, Yuxin Lin, Youfang School of Computer and Information Technology Beijing Jiaotong University Beijing China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing China School of Software Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9798350317152

Temporal knowledge graphs (TKGs) have been identified as a promising approach to represent the dynamics of facts along the timeline. The extrapolation of TKG is to predict unknowable facts happening in the future, holding significant practical value across diverse fields. Most extrapolation studies in TKGs focus on modeling global historical fact repeating and cyclic patterns, as well as local historical adjacent fact evolution patterns, showing promising performance in predicting future un-known facts. Yet, existing methods still face two major challenges: (1) They usually neglect the importance of historical information in KG snapshots related to the queries when encoding the local and global historical information;(2) They exhibit weak anti-noise capabilities, which hinders their performance when the inputs are contaminated with noise. To this end, we propose a novel Local-global history-aware Contrastive Learning model (LogCL) for TKG reasoning, which adopts contrastive learning to better guide the fusion of local and global historical information and enhance the ability to resist interference. Specifically, for the first challenge, LogCL proposes an entity-aware attention mechanism applied to the local and global historical facts encoder, which captures the key historical information related to queries. For the latter issue, LogCL designs a local-global query contrast module, effectively improving the robustness of the model. The experimental results on four benchmark datasets demonstrate that LogCL delivers better and more robust performance than the state-of-the-art baselines. The code of LogCL is available at https://***/WeiChen3690/LoeCL. © 2024 IEEE.

关键词： knowledge graph

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An Ultra-Wideband Cascaded Power Amplifier for Military Individual Radio 7

An Ultra-Wideband Cascaded Power Amplifier for Military Indi...

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7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Gao, Fei Nan, Jingchang Fan, Hongji Wang, Cong Tang, Panpan Li, Zhaoqi Liaoning Technical University Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Huludao China Southeast University Nanjing China Harbin Institute of Technology Department of Microwave Engineering Harbin China

ISBN: (纸本)9798350384437

In recent years, there has been a tremendous interest in military individual radio in wireless communication. To obtain sufficient operating distance and long standby time, the output power and efficiency of power amplifiers have become the research focus by some of the research teams. GaN has higher electron mobility and forbidden bandwidth than other materials. Nowadays, GaN has become the mainstream key technology for radio frequency (RF) power amplifiers in 5G communication systems. In this work, a 500MHz~2500MHz GaN-based HEMT wideband cascaded power amplifier was designed. The output power of the driver amplifier is 36.6dBm ± 0.5dB, the input return loss is 20%, the DC power consumption is 10 dB, input voltage standing wave ratio © 2024 IEEE.

关键词： Gallium nitride

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Spatiotemporal Transformer for data Inference and Long Prediction in Sparse Mobile CrowdSensing 42

Spatiotemporal Transformer for Data Inference and Long Predi...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Wang, En Liu, Weiting Liu, Wenbin Xiang, Chaocan Yang, Bo Yang, Yongjian Jilin University College of Computer Science and Technology China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China Chongqing University College of Computer Science China

ISBN: (纸本)9798350334142

Mobile CrowdSensing (MCS) is a data sensing paradigm that recruits users carrying mobile terminals to collect data. As its variant, Sparse MCS has been further proposed for large-scale and fine-grained sensing task with the advantage of collecting only a few data to infer unsensed data. However, in many real-world scenarios, such as early prevention of epidemic, people are interested in not only the data at the current, but also in the future or even long-term future, and the latter may be more important. Long-term prediction not only reduces sensing cost, but also identifies trends or other characteristics of the data. In this paper, we propose a spatiotemporal model based on Transformer to infer and predict the data with sparse sensed data by utilizing spatiotemporal relationships. We design a spatiotemporal feature embedding to embed the prior spatiotemporal information of sensing map into the model to guide model learning. Moreover, we also design a novel multi-head spatiotemporal attention mechanism to dynamically capture spatiotemporal relationships among data. Extensive experiments have been conducted on three types of typical urban sensing tasks, which verify the effectiveness of our proposed algorithms in improving the inference and long-term prediction accuracy with the sparse sensed data. © 2023 IEEE.

关键词： Forecasting

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Dual-view Label and Feature Supervision Network for Semi-supervised Medical Image Segmentation 24

Dual-view Label and Feature Supervision Network for Semi-sup...

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7th International Conference on Signal Processing and Machine Learning, SPML 2024

作者： Zhou, Zhen Li, Zuoyong Wang, Zhengyuan Teng, Shenghua Wang, Tao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (纸本)9798400717192

Semi-supervised learning has garnered significant attention, particularly in medical image segmentation, owing to its capacity to leverage a large number of unlabeled data and a limited amount of labeled data to improve performance. However, most existing semi-supervised segmentation methods exhibit shortcomings in supervising unlabeled data, both in the label space due to potential noise in pseudo-labels, and the feature space due to indistinct class boundaries. To address the issues above, we propose Dual-view Label and Feature Supervision Network, termed DLFS-Net, to enhance label and feature supervision for unlabeled data. Our approach is based on a dual-view learning strategy that incorporates two modules: the Confidence Fusion Supervision Module (CFS) and the Dual-view Prototype Learning Module (DPL). Specifically, the CFS module generates weight matrices to integrate complementary information from two model outputs, producing more accurate pseudo-labels in the label space. The DPL module reduces intra-class variations in the feature space from two different views through prototype learning to generate clear class boundaries. Experiments on the LA and Pancreas-CT datasets demonstrate that our framework show solid gains (e.g.,1.76% Dice and 2.37% Jaccard improvement on Pancreas-CT dataset with 20% labeled data) compared with the state-of-the-art methods. © 2024 ACM.

关键词： Self-supervised learning

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